Every day, technology shows up in public places as companies and public agencies look to cut costs and improve the customer experience. That shift brings real benefits, but it also creates a problem few people think about: the human-machine interface (HMI), usually a set of push buttons, has to survive contact with the public.Devices we own ourselves get treated gently. Devices anyone can walk up to don't get that same care, whether the damage is deliberate vandalism or just careless, over-forceful use. A delicate membrane-based switch matrix won't last long under those conditions. As public-facing equipment keeps expanding, so does the need for HMIs that are built to take a beating.<h2>You've Already Used One Today</h2>Rugged, button-driven interfaces are everywhere once you start looking. Fill up at a gas station, and you're pressing buttons to select fuel grade. Charge an EV, and you're working through a panel of start/stop and payment controls. At the same station, adding air to your tires or running your car through a wash means more button interaction.Take the train instead, and you'll hit buttons to open doors, access the restroom, or use the intercom, even at the machine where you bought your ticket. Once you arrive, an ATM gives you cash through a rugged keypad, an access control panel or intercom gets you through the door, and an elevator panel takes you the rest of the way. None of these systems receive constant monitoring, and none of them can afford to fail.<h2>What Makes a Switch Vandal-Resistant</h2>Switches built for these environments differ from standard components in both materials and construction. Anti-vandal switches are typically metal-bodied: stainless steel remains the top choice for durability and finish, nickel-plated brass balances strength with aesthetics, and anodized aluminum serves applications that call for a lighter, more modern look.Sealing matters just as much as the housing material. These switches need to resist moisture — from weather or from spills, accidental or otherwise — which typically means an IP67 or IP6K7 ingress protection rating, the latter covering high-pressure spray. On the impact side, most anti-vandal switches carry an IK10 rating, the highest available, protecting against impacts up to 20 Joules.Many of these switches also include an illuminated legend option, using an LED to show function and give visual feedback even in low light.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784225953990-1784225953990.png" width="624" height="351" class="fr-fic fr-dii" />Products ATP22, ATP19, ATP16,CTAV001, &amp; CTAV004. Source: C&amp;K Electromechanical Switch Family<h2>C&amp;K's ATP Family: One Platform, Four Sizes</h2>C&amp;K, one of the world's leading switch suppliers, addresses this need with four series in its ATP family: ATP16, ATP19, ATP22, and ATPS19. The main differences between them come down to panel diameter and body depth, while all four hold to the same rugged environmental and impact standards.<h3>What They All Have in Common</h3>Across the entire ATP portfolio:<ul><li>Extreme protection: Every series carries an IP67 rating for water and dust resistance, plus an IK10 rating (with stainless steel bushings) for maximum impact resistance.</li><li>Premium materials: All models use high-strength, lightweight, corrosion-resistant stainless steel housings with scratch-resistant actuators.</li><li>Functionality options: Every series is available in momentary (spring-back) and lock/latching versions.</li><li>Visual indicators: All models support optional LED illumination in multiple colors, with spot, ring, or power-logo configurations.</li></ul><h3>Where They Differ</h3><a href="https://www.littelfuse.com/products/switches/push-button-switches/atp16" target="_blank" rel="noopener noreferrer">ATP16</a> is the smallest of the family at 16mm, and it delivers a notably longer mechanical lifespan of up to 1 million cycles, making it a strong fit for high-traffic access control and intercom systems.ATP19 and <a href="https://www.littelfuse.com/products/switches/push-button-switches/atp22" target="_blank" rel="noopener noreferrer">ATP22</a> share industry-standard 19mm and 22mm dimensions, carry a slightly higher electrical load capacity (3A vs. 2A), and offer a broader range of actuator shapes, including raised and dome options.ATPS19 is built for tight enclosures. It keeps the 19mm diameter but trims 40% off the internal depth of a standard ATP19, making it the choice when space is the limiting factor.<h3>Choosing the Right Fit</h3>The <a href="https://www.littelfuse.com/products/switches/push-button-switches" target="_blank" rel="noopener noreferrer">C&amp;K ATP Series Anti-Vandal Switches</a> bridge the gap between uncompromising ruggedness and design versatility by pairing industrial-grade IP67 sealing and IK10 impact protection with a refined stainless steel finish that holds up against both harsh weather and intentional abuse.Whether the application calls for the extended mechanical life of the compact ATP16, the higher load capacity of the industry-standard ATP19 or ATP22, or the space-saving profile of the ATPS19, the ATP portfolio gives designers the specific configuration needed to build durability into the product from the start. This kind of durability helps build a brand promise for long-term reliability.

Anti-Vandal Switches for Public HMIs (Human-Machine Interface).

Where the Buttons Never Stop Getting Pressed

A full-wave rectifier converts both AC halves to DC, doubling efficiency. Learn working, types, filtering, and applications.

Full-Wave Rectifier: Working, Types, and Applications

Industrial systems are entering a new era where the boundaries between the digital and physical worlds are no longer clear lines but shared fault zones. As factories, utilities, and infrastructure providers embrace smarter automation and tighter connectivity, they also inherit a challenge that grows in lockstep with their technological progress: the urgent need to defend the machinery that powers modern life. What once operated behind closed networks and air-gapped systems is now intertwined with cloud platforms, remote sensors, and global data flows, creating opportunities for efficiency — and vulnerabilities that didn’t exist a decade ago.In this environment, cybersecurity is no longer just a concern for information technology (IT) departments. It has become a frontline requirement for engineers, operators, and policymakers working to keep critical infrastructure safe. The evolution toward Industry 5.0 highlights not only the value of human-centric design and sustainability but also the necessity of resilience, ensuring that the physical backbone of society can withstand the digital threats that now surround it. The European Union’s Network and Information Security 2 (NIS2) Directive reflects this shift, pushing organizations to rethink how they protect operational technology (OT) and reinforcing that safeguarding industrial systems is as essential as maintaining the machines themselves.OT — which concerns the processes and physical equipment that perform actions such as moving water through treatment plants, monitoring factory temperatures, and controlling turbines in power stations — is becoming increasingly interconnected. This connectivity opens doors to efficiency and insight, but it also exposes systems to a growing and dangerous digital threat landscape.This blog explains why OT cybersecurity is suddenly on the front lines, how regulations are evolving, and what engineers and organizations must do to stay resilient.<h2>The New Reality of Connected Industry</h2>A decade ago, most industrial systems were isolated from the outside world. Their priority was reliability, not connectivity. But Industry 4.0 introduced real-time data sharing across millions of sensors, controllers, and machines. This change allowed companies to reduce waste, optimize output, and predict maintenance issues before they occurred.However, this rapid expansion came with a hidden cost: Billions of devices that were never designed with security in mind suddenly became exposed to the internet.And attackers noticed.Recent findings show that 62 percent of water and electricity operators in the US and UK faced cyberattacks in 2024, and many incidents went undetected because companies lacked the tools or expertise to identify them. The threat is no longer theoretical—it’s at our doorsteps.Some examples read like scenes from a cyber-thriller. A casino’s network was breached through a smart thermometer in a fish tank. In another case, a hacker attempted to contaminate a Florida town’s drinking water by infiltrating the supervisory control and data acquisition (SCADA) system of a treatment plant. Attacks also occurred on nation-states, such as the 2022 cyberattack on an Iranian steel facility that caused a massive fire.These events underscore a simple truth: OT cybersecurity is no longer about protecting data; it’s about protecting people, infrastructure, and entire economies.<h2>NIS2: Europe’s Push for Stronger Cyber Defense</h2>Recognizing the rising threat, the EU expanded its original NIS1 Directive into the far more comprehensive NIS2, implemented in January 2023.NIS2 casts a significantly wider net than its predecessor. Industries such as public communications, wastewater management, manufacturing, transportation, postal services, and space operations now fall under stricter cybersecurity requirements. Medium and large organizations must comply, and severe penalties can follow non-compliance.A key shift in NIS2 is accountability: Senior leadership is now directly responsible for cybersecurity readiness. Companies must be able to demonstrate:<ul><li>Proactive risk-management strategies</li><li>Rapid incident reporting within 24 hours</li><li>Clear recovery and continuity plans</li></ul>Supervision, enforcement, and even peer-review mechanisms have been introduced to ensure organizations take these responsibilities seriously.<h2>Building Resilience in a Hyperconnected World</h2>While NIS2 lays out the requirements, implementing effective OT cybersecurity requires several foundational practices that mirror — but are not identical to — traditional IT security practices.<h3>Know What You’re Protecting</h3>A modern industrial site may contain thousands of networked devices, many of which are replaced or updated regularly. Creating an up-to-date inventory is essential to identifying vulnerabilities and prioritizing protections.<h3>Adopt Zero-Trust Principles</h3>Zero trust assumes no user or device is trustworthy by default. Practices such as multi-factor authentication, secure remote access for maintenance vendors, and detailed logging help prevent unauthorized entry.<h3>Patch Without Disruption</h3>OT systems often run around the clock. Shutting them down for updates isn’t always possible. Effective cybersecurity requires carefully planned maintenance windows, offline testing, and fallback procedures.<h3>Segment and Isolate</h3>Separating critical OT assets from main networks can prevent attackers from moving laterally through a system — a common technique in advanced cyberattacks.<h3>Continuously Monitor and Assess</h3>Because threats evolve rapidly, cybersecurity cannot be a one-time effort. Techniques like risk modeling, simulated attacks, and compliance checks help ensure ongoing readiness.<h3>Train the Teams</h3>Human error remains one of the biggest vulnerabilities. Regular training, threat-awareness programs, and collaboration between IT and OT teams strengthen organizational defenses.<h2>What’s at Stake</h2>Industry 5.0 promises smarter, more adaptable industrial ecosystems, but without robust cybersecurity, that promise falls apart. As OT and IT continue to merge, risks expand. A single breach can stop production lines, cause environmental damage, or even threaten public health. The stakes are simply too high for cybersecurity to remain an afterthought.The frameworks and technologies exist. Their success depends on commitment to continuous improvement, collaboration across teams, and investment in resilience.<h2>Conclusion</h2>Industrial cybersecurity has grown beyond a technical problem to become a societal responsibility. As we usher in Industry 5.0, resilience becomes the core of sustainable progress. By understanding the evolving threat landscape and embracing directives like NIS2, organizations can safeguard their operations and the communities that depend on them.For a deeper dive into this topic, read the full article, “<a href="https://resources.mouser.com/iiot/protecting-industrial-infrastructure-with-nis2" target="_blank">Protecting Industrial Infrastructure with NIS2</a>.”

Learn why industrial cybersecurity and NIS2 compliance are critical for protecting connected OT systems, infrastructure, and public safety.

Why Industrial Cybersecurity Matters in the Age of NIS2

Currently, the AR/VR industry is evolving from large, prototype devices to sleek, all-day wearable systems. This transformation is making immersive experiences increasingly defined by the intelligence and adaptability of the supporting software stack.TDK has developed Direct Retinal Projection (DRP) technology to enable practical AR experiences, providing unmatched clarity, zero focal strain, and a compact, comfortable form factor suitable for all-day wear. In addition to image display, TDK is actively advancing research and development in areas such as multimodal sensor integration, real-time data fusion, and AI collaboration to support the new user experiences made possible by DRP.Through these innovations, TDK is building a hardware-optimized AR software platform that interprets spatial context and seamlessly integrates multimodal sensor feedback in real time, paving the way for intelligent and adaptive AR experiences.<h2>Software as the Missing Piece in AR</h2>Direct Retinal Projection (DRP) method, every pixel is controlled by MEMS mirrors and modulated RGB laser light sources, and is directly drawn onto the user’s retina through laser raster scanning. Unlike conventional display devices such as waveguides, microdisplays, or projection systems—which project images onto lenses or glass surfaces—DRP precisely controls light to form images directly on the retina. Technically, the approach of sweeping a focused beam left-to-right (and line-by-line) to build an image bears a conceptual resemblance to the raster-scan technique used in old CRT televisions; both rely on a scanned beam to sequentially form the visible frame. However, DRP differs fundamentally in implementation and scale: it uses optical laser beams steered by high-speed MEMS mirrors with nanosecond-level intensity modulation rather than an electron beam on phosphor, and it achieves milliradian angular precision and sub-millisecond timing in a package small and power-efficient enough to be mounted on eyewear for all-day use.This approach requires control mechanisms fundamentally different from those used in conventional flat-panel displays: the MEMS mirrors’ angular control and the laser sources’ intensity modulation must operate with sub-millisecond latency and angular precision on the order of milliradians.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783960669080-1783960669080.png" width="624" height="351" class="fr-fic fr-dii" />Source: TDKMEMS mirrors’ angular control and laser sources’ intensity modulation must operate with sub-millisecond latency and milliradian-level angular precision. Even slight jitter, phase delay, or synchronization errors can destabilize the field of view, diminish immersion, or in the worst case, cause visual fatigue. Therefore, in DRP systems, rendering and sensing are inseparable: images must be drawn pixel by pixel, sweep by sweep, in high-precision synchronization with the wearer’s movements and physiological state. For this reason, a highly integrated system combining flexible software correction mechanisms and fast-response control systems is implemented, allowing for precise adjustment to the subtle individual characteristics and unique variations of MEMS mirrors, lasers, and optical paths.＠「Furthermore, in order to generate AR visuals and provide users with a natural experience, it will likely become essential to integrate high-precision positional data and commands from IMUs (inertial measurement units), eye trackers, master controllers, and the cloud in real time. With information from these sensors and external data sources, the system is expected to instantly respond to the user’s head and gaze movements, as well as changes in the surrounding environment, ensuring that each pixel is rendered at the optimal timing and position. Through this complex coordination, the visuals will dynamically adapt to the user’s actions and environment, creating a highly immersive AR experience that seamlessly blends with the real world. Thus, realizing a retinal projection-based AR system using DRP will likely require not only advances in optics, mechanics, and electronic control, but also a sophisticated software platform capable of real-time integration with a wide range of sensors and external data. TDK is advancing technical preparations to realize these capabilities.<h2>A Rendering Engine Tailored to the Retina</h2>At the heart of this platform is TDK’s retinal-aware rendering engine, designed to operate at the high speed and precision required for DRP. TDK’s current AR glasses are already engineered to meet the demands of DRP, incorporating advanced control, computation, and adjustment capabilities.The DRP renders virtual scenes onto a DRP “canvas: retina” that aligns with the angular sweep of the MEMS scanner. A laser control subsystem then adjusts beam intensity and phase, modulating each color channel accuracy to achieve the desired pixel luminance.TDK is not resting on this achievement and is developing Active-PIC technology to further enhance performance. Active-PIC enables even higher-quality visuals and resolutions beyond 4K. Additionally, Active-PIC is directly integrated with the laser modulator. Conventional color modulation methods vary the drive current to the laser diode, limiting switching speeds to about 1 GHz and resolution to around 2K. In contrast, TDK’s Active-PIC uses electro-optical phase control, allowing RGB beams to be switched and multiplexed at up to 10 GHz, thereby achieving resolutions above 4K without increasing the form factor. The loop from input event to photon emission is typically completed in under 5 milliseconds, guaranteeing perceptual continuity and eliminating image latency.TDK’s current AR glasses are already designed to operate at the high speed and precision required for DRP:<ul><li>They incorporate advanced control, computation, and adjustment capabilities.</li><li>The software renders virtual scenes onto a DRP “canvas” that aligns with the angular sweep of the MEMS scanner. A laser control subsystem then adjusts beam intensity and phase, modulating each color channel accuracy to achieve the desired pixel luminance.</li><li>However, TDK is not resting on this achievement and is developing Active-PIC technology to further enhance performance.</li></ul><h2>Retina-Specific Image Processing</h2>Retinal projection offers a focus-free, immersive visual experience that is not affected by the user's eyesight. However, one of the technical challenges that has limited its widespread adoption is the restricted eye-box. In the DRP method, laser beams deliver images to the retina through the pupil, but if the beam misses the pupil, the image is blocked.TDK addresses this by using optical processing and image recognition technologies to detect the position of the pupil and dynamically adjust the laser projection accordingly. Furthermore, by feeding data from the eye-tracking module back to the rendering core, the system enables foveated rendering, in which only the region around the user’s gaze is rendered in high resolution. This approach maintains visual fidelity while significantly reducing power consumption and computational load.The platform also offers high flexibility through software, making it easy to accommodate future feature expansions and new algorithms. By controlling the entire DRP imaging pipeline—including MEMS mirrors and software controllers—the system achieves a close integration of physiological information and rendering.<ul><li>Retinal projection provides a focus-free and excellent visual experience that is not affected by eyesight.</li><li>However, one technical challenge that has prevented its widespread use is the limited eye-box.</li><li>In the DRP method, laser beams pass through the pupil to project images onto the retina. If the beam does not go through the pupil, it is blocked.</li><li>TDK proposes a method to detect the pupil position using optical processing and image recognition, and to move the laser projection accordingly.</li><li>In addition, data from the eye-tracking module is fed back to the rendering core to maintain visual fidelity.</li><li>TDK achieves all of this while greatly reducing power consumption and computational load.</li></ul><h2>Realizing Natural Interaction through Sensor Fusion</h2>TDK’s software platform is designed to support multimodal user input by integrating the following sensors and actuators:<ul><li>6-axis IMU for head tracking</li><li>ToF (Time-of-Flight) depth sensor for spatial recognition</li><li>Eye tracking for focus-based control</li><li>Microphone for voice commands and environmental recognition</li><li>Cloud/local AI for advanced recognition and analysis</li><li>Bone conduction speakers for audio output</li><li>Haptic devices for tactile feedback</li></ul>These data streams are aggregated by the contextual interaction engine, enabling natural UI experiences such as gaze-based overlays, gesture control, voice commands, and context-aware UI repositioning.Whereas conventional UIs rely on touchscreen input or physical buttons, AR glasses present virtual UI elements in real space, allowing users to interact with floating buttons and menus. While intuitive gesture and gaze-based controls are often depicted in movies, the actual technology and user experience are still evolving.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783960869956-260701_TDK_007.jpg" style="width:100%px" class="fr-fic fr-dib" />Source: TDKTo operate spatial UIs naturally, it is essential to fuse real-time data from multiple sources—such as hand and finger movement, gaze, and voice—to accurately interpret user intent. TDK’s platform combines sensor fusion with high-speed recognition and processing to deliver a next-generation user experience.For example, when a user reaches for a virtual button, the ToF sensor detects hand depth, the IMU analyzes gesture trajectory, and gaze confirms intent. Once the button is activated, haptic devices and bone conduction speakers provide feedback faster than human reaction speed.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783960898323-260701_TDK_008.jpg" class="fr-fic fr-dib" />Source: TDK<h2>Real-Time Edge Processing and Cloud Collaboration</h2>To minimize latency and enable standalone operation, TDK is developing a software stack that supports on-device inference for key modules. These functions are implemented in demonstration units or as subsystem components, and are practically usable in individual applications. Gesture recognition and pose estimation are accelerated by lightweight machine learning models running on edge processors within glasses and wearable devices, while heavy processing is offloaded to the cloud as needed.Furthermore, high-precision recognition and inference are achieved through multimodal sensor fusion technology, which integrates data from multiple sensors such as cameras, IMUs, and microphones. Customizable AI models are also provided, allowing optimization and retraining according to specific use cases and devices, enabling flexible system construction. The platform emphasizes power-efficient design through edge AI and on-device processing, optimizing power consumption and extending battery life.This hybrid architecture distributes and integrates the load and response time of machine learning processing, enabling real-time DRP rendering that matches human responsiveness—even on battery-constrained devices or during multi-user AR sessions.For developers, APIs compatible with Unity, etc are provided, along with extensions for DRP-aware rendering and sensor calibration routines. In addition, a comprehensive toolchain and SDK—including simulation environments, debugging tools, and profiling features—are available to support efficient development. Developers can build applications using traditional tools while automatically benefiting from DRP-specific optimizations applied by the runtime.<h2>The Future of Daily Life Enabled by AR Glasses</h2>As AR glasses become widely adopted and the fusion of “A” for Augmentation and “R” for Reality transforms our daily lives, we can look forward to a future where our experiences become richer and more fulfilling than ever before. In this new era, AR will not only overlay information onto our field of vision, but will fundamentally change how we interact with our surroundings, with others, and even with ourselves.<iframe width="640" height="360" src="https://www.youtube.com/embed/Y6IMXcmbhUE?&amp;wmode=opaque" frameborder="0" class="fr-draggable"></iframe> The evolution of AR glasses brings new possibilities to our lives. In the future, all the technologies required for AR glasses—advanced sensors, optical components, environmental recognition, and user-optimized interfaces—will be realized through AR software supported by the cloud and AI. This AR software will not be limited to programmatic applications; it will also include content, systems, and even the transformation of lifestyles. Through AR software platforms that integrate the vast data processing capabilities of the cloud and the intelligent analysis of AI, digital information will seamlessly merge with the real world, enriching everyday experiences. For example, AR will enable people to visualize abstract concepts in real time, navigate complex environments with ease, and connect with others through immersive shared experiences, regardless of physical distance.In fields such as education, healthcare, and industry, real-time information will be obtained through cloud-based AR software, and AI-driven analysis and remote expert support will be provided through intelligent content and systems. In education, AR will create interactive learning environments where students can explore historical events or scientific phenomena as if they were actually present. In healthcare, AR will assist doctors with real-time data overlays during surgery or patient care, improving accuracy and outcomes. In industry, workers will receive step-by-step guidance and safety alerts directly in their field of view, enhancing productivity and reducing errors.Even at home, shopping, communication, and entertainment will become more intuitive and convenient thanks to the continuous support of AR software powered by the cloud and AI, which includes not only applications but also immersive content and lifestyle enhancements. For example, when shopping for furniture, you will be able to see how it fits in your room before making a purchase, or communicate with friends and family through lifelike avatars that appear in your living space. AR glasses will not simply be information devices—they will upgrade our lives by integrating with our senses and actions, all made possible by advanced AR software utilizing cloud and AI technologies.With further advancement of AR glasses, society as a whole will be able to enjoy new experiences and value provided by AR software platforms that leverage the power of the cloud and AI across applications, content, systems, and daily life. These technological innovations will make daily life more comfortable, richer, and freer. There are high expectations for the future lifestyle brought about by comprehensive AR software, the cloud, and AI, as they enable us to transcend the boundaries between the digital and physical worlds, unlocking new forms of creativity, connection, and personal growth.<hr /><h3>References</h3><ol><li>Envisioning the Future of AR Glasses Through TDK’s Augmented Reality Solutions _ TDK.pdf</li><li>20250203 TDK AR VR.pdf</li><li>20241030 English TDK_full-color laser control device.pdf</li><li>Ring_Controller_for_ARVR.pdf</li><li>Meta_opticsp_説明資料_v4.pptx</li></ol>

Direct Retinal Projection (DRP) technology enables practical AR experiences, with zero focal strain and an all-day wearable system.

Intelligent and Adaptive AR Experiences with TDK

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power as a single LED. A robot could use such a map to plan a collision-free path to reach its goal.Typically, generating such thorough maps requires power-hungry systems and a great deal of memory to build and store 3D representations of the obstacles in a robot’s environment.The MIT researchers took a different approach by combining an extremely efficient mapping algorithm with specialized hardware designed to accelerate its workload, which minimizes memory and power consumption. This system-on-a-chip consumes only about 6 milliwatts of power, a fraction of the power required by other systems. This low-power operation could also make the chip well-suited for lightweight augmented reality headsets that can be worn for extended periods, for applications like educational medical simulation or detailed repair and assembly work.“This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency. While there has been a lot of work looking into compact 3D maps, what stands out about this work is that it also ensures that the process to generate those maps is as efficient as possible. Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner,” says Vivienne Sze, a professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a <a href="https://arxiv.org/pdf/2603.29005" target="_blank">paper on the chip</a>.She is joined on the paper by co-lead authors and MIT graduate students Zih-Sing Fu and Peter Zhi Xuan Li as well as Sertac Karaman, a professor of aeronautics and astronautics and the director of LIDS. The work was recently presented at the IEEE Very Large-Scale Integrated Circuits Symposium.A more compact mapFor a robot, generating a 3D map that includes the obstacles in its environment usually demands a lot of power because it must store images captured by its camera, and process all the 3D pixels in each image multiple times.Instead of representing the environment using 3D pixels, which are cubes called voxels, the MIT researchers utilized a technique that maps the obstacles in space using ellipsoid blobs called Gaussians. The size, shape, and thickness of these ellipsoids can be smoothly adapted, so they match the shape of curved objects more efficiently than if one uses rigid, cube-shaped voxels. Importantly, the map captures the obstacles and free space around the robot, and together these let the robot plan a safe, collision-free path. Mapping obstacles and free space with voxels typically consumes a lot of memory, which makes traditional methods power-hungry. Because Gaussians can flexibly fit the geometry, a single elongated ellipsoid can represent a region that would take many voxels, so occupied surfaces and free space are captured far more compactly.For their new system-on-a-chip, called Gleanmer, the researchers employed an <a href="https://arxiv.org/pdf/2306.03740" target="_blank">algorithm their lab developed called GMMap</a> that efficiently generates a 3D map of the robot’s environment using Gaussians to represent obstacles. With traditional approaches, a robot would need to load and process each depth image several times to adjust the size and shape of the ellipsoids. The system would usually construct Gaussians by comparing all the pixels in an image to each other. But the amount of memory and power needed to do this remains too high for many edge devices.To solve this problem, the MIT researchers invented a technique that can generate highly accurate Gaussians from depth images with only one pass, after which they can discard the images, so the chip never has to store an entire image at once. Instead of comparing each pixel to every other pixel in the 3D image, their algorithm assumes that nearby pixels belong in the same Gaussian, so it only needs to compare each pixel to its neighbors.“At any point in time, we only need to store a few pixels in memory, which significantly reduces the memory footprint our algorithm requires,” Li says.Leveraging co-designBut as the robot moves through the space, it usually sees the same object from different viewpoints. When it generates Gaussians, some will overlap because they represent the same object. This can make the 3D map too large to store on an edge device.Fusing overlapping Gaussians makes the map more compact, but doing so typically requires the algorithm to process many raw pixels stored in memory. The researchers developed a novel technique to perform this fusion process directly on overlapping Gaussians, without needing to revisit the original pixels. Since Gaussians are more compact than pixels, this significantly reduces memory and power requirements.The same principle runs through their algorithm — most computations operate directly on compact Gaussians rather than the original pixels, enabling energy efficiency.The researchers exploit this principle to design a chip that keeps the Gaussians it is actively working on within small, fast on-chip memory right beside the computational units. This is only possible because the Gaussian map is so compact.The Gaussians the robot needs to work on next are waiting in the on-chip memory units, so they don’t need to be fetched from more distant, power-hungry, off-chip storage. “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” Fu explains.They tested the system-on-a-chip by reconstructing a range of diverse, pre-existing 3D environments. The chip can also reconstruct obstacles and free space directly from live data streamed from an iPhone camera.Gleanmer generated detailed 3D maps in real-time while consuming about 6 milliwatts of power. It required only about 2.5 percent of the power that the best existing chip for map construction would need. By reusing compact Gaussians along the path as it plans, the chip lets a robot chart a safe trajectory using only about 20 percent of the energy it would otherwise need.“We reduce the memory consumption by making sure the algorithm is efficient. Then we accelerate the workload that is performed by that efficient algorithm, so in the end, our chip is as efficient as possible,” Li says.The researchers plan to further improve energy efficiency by moving the processing units on the chip closer to the sensors that gather environmental data. They could also explore additional applications, such as the use of Gaussians to represent schematics. This could help AI systems reason about complex blueprints more efficiently.“Real-time 3D mapping has been the missing piece for small autonomous systems. A drone inspecting a pipeline or a pair of AR glasses navigating a room both need to understand the space around them — instantly, continuously, and at almost no power cost. Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” says Karaman.This work is supported, in part, by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel.

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.

New chip could help tiny robots traverse complex environments

<h2 id="isPasted">What is SHINING 3D Metrology Day?</h2>As manufacturers worldwide continue to accelerate digital transformation, the demand for high-precision, efficient, and intelligent measurement technologies has never been greater. To connect local industries with global metrology innovation, SHINING 3D launched SHINING 3D Metrology Day—a global series of industrial metrology seminars. More than a technical event, SHINING 3D Metrology Day serves as a platform where manufacturers, engineers, and industry experts exchange knowledge, share real-world applications, and explore how advanced 3D metrology can solve practical manufacturing challenges. Guided by a unified global framework while adapting to local market needs, the initiative reflects SHINING 3D's long-term commitment to advancing industrial metrology worldwide.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783932602989-1783932602989.jpeg" class="fr-fic fr-dib" /><h2>SHINING 3D Metrology Day 2026 Global Events</h2>During the first half of 2026, SHINING 3D successfully hosted Metrology Day events in Chonburi (Thailand), Tampa (USA), Querétaro (Mexico), and Brisbane (Australia). Bringing together manufacturing professionals across industries including automotive, tooling, heavy industry, and precision engineering, each event focused on one common objective: helping manufacturers improve quality, optimize production workflows, and accelerate digital transformation through high-accuracy 3D measurement technologies.Throughout the events, SHINING 3D introduced its latest developments in industrial 3D metrology, backed by more than 20 years of expertise in high-accuracy 3D vision technology. From industrial 3D scanners and intelligent inspection software to reverse engineering and automated measurement solutions, attendees gained practical insights into complete digital workflows covering design, manufacturing, inspection, and quality control. Rather than simply presenting products, the sessions focused on solving real production challenges with integrated hardware and software solutions that deliver measurable improvements in accuracy, efficiency, and productivity.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783932633942-1783932633942.jpeg" class="fr-fic fr-dib" />A highlight of every Metrology Day was the opportunity to learn directly from manufacturers. Customer representatives shared firsthand experiences of applying SHINING 3D solutions to optimize inspection processes, improve product quality, accelerate reverse engineering, and support continuous innovation. Combined with live demonstrations and hands-on product experiences, these real-world success stories allowed attendees to see how digital metrology can be effectively implemented in everyday manufacturing environments while encouraging open technical discussions and knowledge exchange.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783932610376-1783932610376.jpeg" class="fr-fic fr-dib" /><h2>Looking Ahead: Expanding Metrology Day Worldwide</h2>Today, SHINING 3D supports customers in more than 100 countries through its global network of R&amp;D, technical support, and local service teams. Metrology Day has become an important extension of this global ecosystem, bringing together technology experts, partners, and end users to share best practices and strengthen collaboration across regional manufacturing communities. As the initiative continues to expand, it further reinforces SHINING 3D's position as a trusted provider of high-accuracy industrial 3D metrology solutions and a long-term partner in intelligent manufacturing.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783932620726-1783932620725.jpeg" class="fr-fic fr-dib" />Looking ahead, SHINING 3D will continue bringing Metrology Day to more regions around the world, delivering localized technical seminars, practical application sharing, and hands-on experiences that help manufacturers unlock the full potential of digital metrology. Together with customers and partners worldwide, SHINING 3D remains committed to advancing precision measurement, fostering industry collaboration, and shaping the future of intelligent manufacturing - one Metrology Day at a time.

SHINING 3D Metrology Day 2026 united manufacturers, engineers, and industry experts across four global events to showcase advanced 3D metrology solutions, practical applications, and digital inspection workflows that improve quality, efficiency, and innovation in modern manufacturing.

SHINING 3D Metrology Day 2026: Bringing Global Manufacturers Closer to the Future of Industrial Metrology

Motor Controller box in an industrial setup 

Design, wire, protect, and troubleshoot motor starting systems.

Motor Control Circuit Guide: Contactors and Wiring

<h2 id="isPasted">Introduction</h2>Object detection is one of the foundational tasks in industrial automation. Whether confirming the presence of a component, tracking products on a conveyor, or verifying pallet positions in a warehouse, sensors provide the information that machines need to make decisions in real time.In real-world applications, however, reliable detection is never as straightforward as it seems. Production environments are filled with variables that can complicate sensing. Parts to be detected may vary in color, shape, or surface finish. Some could be highly reflective, while others could absorb most of the incident light. Environmental factors like dust, oil mist, and changing lighting can further affect performance. Present-day productivity demands require high-speed operation, which makes it difficult to maintain consistent detection.This article explains how time-of-flight (ToF) sensing addresses the limitations of conventional photoelectric sensors in challenging industrial applications. It uses OMRON’s E3AS-HF as an example to showcase how manufacturers can achieve more reliable long-range object detection across varying targets and environmental conditions.<h2>Conventional Photoelectric Sensors and Their Challenges</h2>Photoelectric sensors are electronic devices used to detect the presence or distance of an object without making physical contact. Traditional photoelectric sensors are highly effective in many simpler applications and are widely used in the industry. They can be classified based on their working principles.<h3>Light-Quantity Methods</h3>Sensors using light-quantity methods detect an object by projecting a light beam and monitoring whether it is interrupted or by measuring changes in the amount of light reflected. Types of light-quantity methods include:Through-Beam SensorsThrough-beam sensors separate emitter and receiver units positioned opposite each other. Detection occurs when an object blocks the beam. This approach offers long sensing distances with strong detection margins but requires access to both sides of the target.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531257736-1783531257736.jpeg" width="740" height="185" class="fr-fic fr-dii" />Fig. 1: Through-beam sensor operationRetroreflective SensorsRetroreflective sensors combine emitter and receiver in a single housing. They use a reflector to return the light beam. While the installation is simple, alignment and reflector placement become important to consider.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531204831-1783531204831.jpeg" width="740" height="185" class="fr-fic fr-dii" />Fig. 2: Retroreflective sensor operationDiffuse-Reflective SensorsDiffuse-reflective sensors detect light reflected directly from the target. They are easy to install but are more sensitive to variations in color, reflectivity, and finish.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531328615-1783531328615.jpeg" width="740" height="185" class="fr-fic fr-dii" />Fig. 3: Diffuse-reflective sensor operationIn all these cases, performance depends to some degree on how much light reaches the receiver. Dark or low-reflectivity objects return weaker signals, while glossy or metallic surfaces can reflect light away from the sensor. Dust, oil mist, and ambient light can further reduce detection margins.<h3>Distance-Setting Methods</h3>Distance-setting methods use triangulation to estimate the position of a target by analyzing the angle formed between the incident light from the emitter and the reflected light reaching the receiver. Because they estimate distance rather than relying just on light intensity, they can better distinguish targets and support more precise position-based detection.Their usable sensing range, however, is typically shorter than that of through-beam sensors, and performance is still influenced by target surface properties, measurement distance, and mounting constraints.<h2>When Detection Conditions Become More Demanding</h2>For many applications, conventional photoelectric sensors remain the most practical and cost-effective solutions. Problems arise when the target material varies, installation space is limited, or operating conditions change frequently.A production line may need to detect matte-black rubber, polished metal, painted surfaces, and irregularly shaped parts, often at different angles and distances. To maintain stable performance under such conditions, engineers need to adjust thresholds, reposition sensors, or switch to different sensing technologies for different stations.By determining how far away an object is rather than relying primarily on the amount of reflected light, TOF sensors can deliver more consistent results across a wider range of materials and operating conditions.<h2>ToF Sensing: A More Reliable Way</h2>Unlike conventional reflective photoelectric methods, which can be highly dependent on the intensity of reflected light, ToF sensing determines distance by measuring the time it takes for emitted laser light to return from the target. As light travels at a known speed, that elapsed time can be converted directly into distance. This makes detection less affected by target color, reflectivity, and surface finish while still requiring sufficient returned light for stable measurement. For example, a matte black rubber component and a polished metal part may reflect very different amounts of light, but as long as enough light returns to be measured, the sensor can determine how far away each object is.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531381762-1783531381762.jpeg" width="740" height="277" class="fr-fic fr-dii" />Fig. 4: ToF sensor operationToF sensing still presents engineering challenges. The returning signal can be extremely weak, particularly when detecting dark objects at long distances. Ambient light and electrical noise can obscure the signal, and temperature changes can affect the characteristics of optical components over time. Therefore, achieving stable measurements requires more than simply measuring elapsed time. It depends on high-speed sampling, precise signal processing, and compensation techniques that maintain accuracy under changing conditions.When properly implemented, ToF sensing provides a practical solution to detect a wide range of objects with greater consistency. It can also simplify installation by allowing sensors to be mounted farther away from the target while still maintaining reliable operation. This combination of robustness and flexibility has made ToF sensing increasingly attractive for applications in the automotive industry, logistics, manufacturing, and more.<h2>OMRON E3AS-HF ToF Laser Sensor <iframe id="69c3e8ca390acc4f20b8c5ed" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/omron-industrial-automation-e3as-hf-tof-laser-sensors?iframe=true" frameborder="0" scrolling="no"></iframe></h2>The <a href="https://www.mouser.com/new/omron-electronics/omron-ia-e3as-hf-tof-laser-sensors/">OMRON E3AS-HF</a> is a high-sensitivity ToF laser sensor designed for applications where target conditions and installation constraints can make conventional sensing methods difficult to implement. By combining long-range distance measurement with wide-angle detection characteristics, it provides greater flexibility in how and where sensors can be installed.The sensor supports a sensing range from 50 mm to 6,000 mm and is available in separate spot-beam and diffused-beam models. Depending on model type, dial position, and installation distance, beam options include approximately 2 mm (spot), 9 mm (parallel), and 40 mm (wide-area) detection.  One of the defining characteristics of the E3AS-HF is its angular performance. The sensor can detect at installation angles up to ±85° when measured at 5 m using black paper with 10% reflectance and a 200 ms response time, making it possible to mount the sensor away from moving equipment, robots, and operator pathways.The E3AS-HF brings several features that make setup and maintenance easy. An OLED display provides direct access to settings and measured values, while a bottom-mounted status indicator remains visible when the sensor is installed in elevated or hard-to-reach locations. IO-Link is supported as standard on applicable models, enabling remote parameterization, diagnostics, and condition monitoring.Table 1: Key specifications of OMRON E3AS-HF<div><table><tbody><tr><td>Specification</td><td>OMRON E3AS-HF</td></tr><tr><td>Sensing range</td><td>50 mm to 6,000 mm</td></tr><tr><td>Beam types</td><td>Spot beam and diffused beam</td></tr><tr><td>Light source</td><td>Class 1 red laser (660 nm)</td></tr><tr><td>Response time</td><td>2 ms, 10 ms, 50 ms, or 200 ms</td></tr><tr><td>Outputs</td><td>NPN or PNP digital outputs, 4–20 mA analog output</td></tr><tr><td>Communication</td><td>IO-Link v1.1 (COM3, 230.4 kbps)</td></tr><tr><td>Operating temperature</td><td>–30°C to 55°C</td></tr><tr><td>Environmental ratings</td><td>IP67, IP69K, IP67G, ECOLAB</td></tr><tr><td>Display</td><td>OLED with five language options</td></tr></tbody></table></div>The E3AS-HF combines the sensing range typically associated with larger photoelectric systems with the flexibility of an all-in-one sensor. Its 6 m maximum range allows the sensor to be mounted away from conveyors, robot arms, and other moving equipment, while the selectable response times let engineers balance speed and measurement stability for different applications.Support for both digital and analog outputs, along with IO-Link communication, makes the sensor easy to integrate into modern automation architectures. At the same time, the rugged housing and broad operating temperature range allow it to be used in environments that involve washdowns, cutting oil, dust, and low temperatures.<h2>Design Features Behind Stable Detection</h2>The E3AS-HF combines a number of design elements to ensure consistent measurements across diverse materials, surfaces, and environmental conditions.<h3>High-Speed Sampling and Signal Accumulation</h3>When measurement distances increase, the amount of light returning to the sensor can get extremely small. This is especially true for detecting dark targets such as black rubber or painted metal. To extract useful information from these weak signals, the E3AS-HF accumulates approximately 10 million data points via ultra-fast sampling at 10 billion times per second to improve measurement stability.<h3>Avalanche Photodiode-Based Photodetection</h3>The sensor uses an avalanche photodiode (APD), which offers significantly higher sensitivity than conventional photodiodes. Even when a faint amount of light is reflected from the target, the APD can amplify the weak signal, allowing the sensor to measure the distance reliably.<h3>Inbuilt Heating and Real-Time Temperature Compensation</h3>Optical components are known to change their characteristics as temperatures shift. Since this is their natural behavior, it cannot be avoided but only compensated for. The E3AS-HF uses APD-based photodetection with real-time temperature correction to reduce characteristic variation. Separately, an integrated heater and temperature sensor support low-temperature operation.<h3>Automatic Mutual Interference Prevention</h3>In industrial settings, multiple sensors are often installed close to one another. Emitted laser pulses can interfere, causing false detections. The E3AS-HF is built to handle this challenge by automatically selecting a different emission pattern, which avoids interference without requiring any manual settings. Manual configuration is available too if needed.<h3>Adjustable Beam Characteristics</h3>The sensor is available in spot-beam and diffused-beam versions, with an adjustable beam diameter to match the application. The sensor, when set at a narrow beam, can detect small targets, while at a wider beam, it spreads over larger areas.<h3>Environmental Protection and Antifouling Design</h3>A contaminated lens reduces signal quality over time. To withstand demanding industrial environments, the sensor features an antifouling coating on the lens and carries IP67, IP69K, and IP67G ratings, along with ECOLAB approval for resistance to cleaning agents. It also supports operation from –30°C to 55°C, with an integrated heater and temperature sensor that enables operation down to –30°C (warm-up may be required at temperatures below –10°C). <iframe width="640" height="360" src="https://www.youtube.com/embed/ISediMEzlp0?&amp;wmode=opaque" frameborder="0" class="fr-draggable">        </iframe><h2>Industrial Application Scenarios</h2>OMRON’s E3AS-HF is a versatile ToF laser sensor suitable for a broad range of automation tasks.<h3>Automotive Manufacturing</h3>Automotive production lines include a mix of painted body panels, machined metal components, and dark plastic parts, each exhibiting different optical properties. The E3AS-HF can verify part presence, identify vehicle body types, and confirm the position of painted panels, even when the sensor is mounted at an angle or far from moving robots and tooling.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531531444-1783531531444.jpeg" width="740" height="223" class="fr-fic fr-dii" />Fig. 5: Automotive applications of OMRON E3AS-HF ToF sensors. Source: OMRON<h3>Logistics and Warehousing</h3>In warehouses and distribution centers, sensors are used to detect pallets, cartons, and moving shuttles. Because package colors and surface finishes can vary, maintaining stable detection with intensity-based methods can be difficult. The E3AS-HF’s distance-based operation and automatic mutual interference prevention make it well-suited for pallet detection, conveyor tracking, and automated storage and retrieval systems.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531553492-1783531553492.jpeg" width="740" height="223" class="fr-fic fr-dii" />Fig. 6: Logistics and warehousing applications of OMRON E3AS-HF ToF sensors. Source: OMRON<h3>Manufacturing and Assembly</h3>General manufacturing applications often involve object positioning, height verification, and counting tasks. The E3AS-HF offers accurate distance measurement with selectable beam characteristics, allowing engineers to select a sensing area for small features or larger surfaces. Its analog current output and scaling function can also be used to detect subtle height differences over a narrow measurement range.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783531571754-1783531571754.jpeg" width="740" height="223" class="fr-fic fr-dii" />Fig. 7: Automotive applications of OMRON E3AS-HF ToF sensors. Source: OMRON<h2>Toward More Flexible Industrial Sensing</h2>As production environments become more automated and product variation continues to increase, sensors are expected to perform reliably under a wider range of conditions. Differences in color, reflectivity, shape, and installation geometry can all affect detection performance, particularly when sensing methods depend heavily on the amount of light blocked/returned by the target.ToF sensing offers a different approach compared to traditional photoelectric sensors by measuring distance directly. This makes detection less dependent on the target’s appearance and more on the quality of the sensors deployed.For engineers designing systems that detect diverse targets and require reconfigurability, <a href="https://www.mouser.com/new/omron-electronics/omron-ia-e3as-hf-tof-laser-sensors/">OMRON’s E3AS-HF</a> offers a practical way to maintain more consistent sensing performance.

Conventional photoelectric sensors used for object detection and distance measurement can struggle with dark, glossy, or irregular targets because they rely on light intensity. Time-of-flight laser sensors overcome these challenges by using distance-based sensing instead.

Industrial Object Detection with Long-Range, Wide-Angle Time-of-Flight Sensing: OMRON E3AS-HF

Carlo Prisco and Fabio Marchese from <a href="https://www.priscozen.net/" target="_blank">PriscoZen</a> had a clear goal from the start: not a technical demo, but a real, working platform that could bring machine control, software logic, and visual quality inspection together in a single compact system. Something they could demonstrate live, evolve over time, and show that industrial automation doesn’t have to mean a traditional PLC in every scenario. The result is ZenCell – and its story is a good example of how innovation, more often than not, emerges through iterations and a progression of improvements, rather than a single eureka moment. <h3>Where it started: Arduino® Mega™ board + Raspberry Pi</h3>The first version of ZenCell was built around a practical architecture: a <a href="https://store.arduino.cc/products/arduino-mega-2560-rev3?utm_source=content&amp;utm_medium=blogpost&amp;utm_campaign=unoq_is_the_answer&amp;utm_id=mktg-content" target="_blank" rel="noopener noreferrer">Mega 2560 Rev3</a> handling input and output signals, a Raspberry Pi as the central brain coordinating machine logic and the operating cycle, and a Keyence camera for visual inspection. Logic was distributed across two boards, but it worked – well enough that cycle times were in the range of 1.8 to 2.2 seconds.<iframe width="640" height="360" src="https://www.youtube.com/embed/Zo6YJlm9vR0?&amp;t=1s&amp;wmode=opaque" frameborder="0" class="fr-draggable">  </iframe>The team tested the system first at home, validating the overall logic and machine behavior in early-stage conditions. Then they took it further: <a href="https://youtu.be/4d3Z-rfzTMc?si=i1EmL2N7JobZyTtR" target="_blank">ZenCell was integrated alongside a 3-axis industrial robot</a> next to a molding press, where it held up just as well. The architecture proved its validity not just as a concept, but in conditions close to real production – and it was open, integrated, and flexible.<iframe id="6a3c254c6d19a31976afd60b" width="250" class="specs-iframe fr-draggable" height="440" src="https://wevolver.com/iframe/specs/arduino-mega-2560-rev3?iframe=true" frameborder="0" scrolling="no"></iframe><h3>The shift: from two boards to one <iframe id="6a3c254c6d19a31976afd60b" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/arduino-uno-q-4gb?iframe=true" frameborder="0" scrolling="no"></iframe></h3>Once the <a href="https://www.arduino.cc/product-uno-q?utm_source=content&amp;utm_medium=blogpost&amp;utm_campaign=unoq_is_the_answer&amp;utm_id=mktg-content" target="_blank" rel="noopener noreferrer">UNO Q board</a> was released, PriscoZen saw an opportunity to greatly improve their solution. Thanks to its dual-brain architecture, UNO Q combines an MPU running Linux with an MCU handling real-time control – exactly the split that Raspberry Pi and Mega were covering separately, now unified on a single compact platform. This allowed Carlo and Fabio to rethink how the system was organized.In ZenCell V1 – which will début at Maker Faire Caserta 2026 (May 30-31, 2026) – UNO Q is the true engine of the system: hosting the dedicated ZenCell software, managing the cycle logic, coordinating all connected devices, and handling identification peripherals like QR code and barcode readers directly. The result is a cleaner, more centralized architecture, with cycle times brought down to a range of 0.56 to 0.68 seconds. <h3>ZenCore: the software layer that ties it all together</h3>Alongside the hardware evolution, Carlo and Fabio developed ZenCore – the software platform running on ZenCell V1. Accessible through a local web interface with no complex client software required, ZenCore centralizes operational supervision, workflow and recipe management, I/O signal handling, diagnostics, and vision system integration in a single environment.The long-term vision is a node-based, visual approach to automation logic – connecting devices, commands, states, and sequences in a way that is reusable and adaptable across future applications. <h3>The next step for ZenCell</h3>The roadmap for ZenCell V2 takes the platform further still, with plans to replace the Keyence camera with an open industrial camera and build a proprietary pipeline for image acquisition, dataset development, and defect-recognition model training – all running locally on the next-generation platform.From a distributed two-board prototype to a centralized system built around a single board’s dual architecture: ZenCell is a clear example of what happens when engineers with the right skills and a concrete vision find the right tools to bring it to life. Arduino, Mega, and UNO are trademarks of registered trademarks of Arduino S.r.l.

ZenCell by PriscoZen leverages the Arduino® UNO™ Q dual architecture to replace two boards with one.

ZenCell: the one-board quality inspection solution

Creature combat needs more than movement.For Lords of the Fallen, the dark-fantasy action-RPG developed by HEXWORKS, a CI Games studio, <a href="https://www.creaturebionics.com/" target="_blank" rel="noopener noreferrer">Creature Bionics</a> helped deliver motion capture and casting for a world built around weapons, monsters, cinematic movement, and heavy combat. At the center of that work was Ace Ruele, founder of Creature Bionics, whose background brings together acting, motion capture, creature performance, casting, and direction.For a project like Lords of the Fallen, that mix mattered. The team needed performers who could handle swords, creatures, combat systems, and unusual body mechanics. They also needed a capture workflow that could move fast, work remotely, and deliver high-quality motion data for AAA-ready character work.<a href="https://www.xsens.com/en/entertainment/motion-capture" target="_blank" rel="noopener noreferrer">Xsens</a> helped make that possible.<h3>When fantasy combat needs more than clean movement</h3>Lords of the Fallen is built around dark fantasy, medieval-inspired worlds, creatures, and intense combat. Ace described the project as a fantasy game with a strong focus on combat systems, creature work, and the feel of dark, Soulslike worlds.That kind of production puts real pressure on motion capture. The work is not only about recording clean movement. It is about finding the right performer, shaping the action, reviewing the result, and repeating the process until the motion fits the character.For Ace, motion capture was the right foundation because it gave the team access to real performance.“Motion capture makes it more real,” Ace explains. “You can get the real emphasis of character performance, those nuances that you may not find in hand animation.”The challenge was to keep that realism while capturing fast combat, creature movement, falls, rolls, jumps, and weapon-led action across a flexible production setup.<h3>How Xsens kept capture flexible across locations</h3>Xsens gave Creature Bionics a way to capture performance wherever the action made sense.Shoots could happen in different spaces, from halls to larger rooms, smaller rooms, outside areas, and quick recapture setups. Much of the work was handled remote mocap game studio, with the team tuning in while Ace operated the Xsens setup and directed the performance capture on the floor.Instead of being tied to a fixed stage, Creature Bionics could adapt around the movement. If a character needed more room to run, the team could go outside. If a move needed to be captured again, it did not require rebuilding a full capture volume.Ace recalls how simple the working setup became: “All you needed was a space and one operator.”That flexibility made Xsens part of the creative feedback loop. Before a shoot, Ace would align with the team on characters and reference material. During capture, he could direct the performer, operate the system, and share the Xsens screen with the remote team. After delivery, the game team could test the data on the character and request specific adjustments when the performance needed to change.“We never really did a reshoot on a whole character,” Ace explains. “We might just reshoot movements if there was a better way to do the movement.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783500107504-Ace+Ruele+-+Lord+of+the+Fallen+Customer+Case+-+Xsens+Link+2.jpg" style="width:100%px" class="fr-fic fr-dib" /><a href="https://www.xsens.com/motion-capture/xsens-link-specifications" target="_blank" rel="noopener noreferrer">Xsens Link</a> gave Creature Bionics the freedom to capture physical creature movement in a flexible production setup.<h3 id="isPasted">Making high-impact action easier to capture</h3>For Creature Bionics, the priority was always the performance. Some combat actions required performers to change grip, release props, or work through fast choreography. In those moments, the team made practical production choices around each move, so the performer could focus on body mechanics, timing, weight, and intent.That approach helped keep the movement natural. The team could focus first on how a character attacks, recovers, absorbs impact, or changes direction. From there, the wider animation pipeline could support the final character and weapon work.For impact work, the team also planned around the performer and the suit. If a performer needed to drop onto a crash mat or roll, they adjusted the setup to protect the hardware and keep the movement safe. The value was that these moments could be seen, reviewed, and solved during the session.“There was nothing really specific to Xsens where we couldn’t do something,” Ace says. “We would always see it anyway. We would just reshoot it.”That same production feedback connects naturally to the next-generation Xsens Link. With modular hub placement, teams can position the hub where it fits the performance best, including high-impact movement where back placement may not be ideal. For stunt, combat, and creature work, that gives performers more freedom while helping productions keep the reliability and data quality Xsens is known for.<h3>What Xsens made possible for Creature Bionics</h3>For Lords of the Fallen, Xsens helped Creature Bionics support a large volume of character movement with speed, flexibility, and remote production support.The strongest outcomes were clear: capture could happen across multiple locations, the remote team could review motion during the session, and reshoots were mainly used to improve performance choices rather than fix broken data. Ace estimated a typical Xsens setup at around 20 to 30 minutes from laptop and suit setup through calibration, which helped keep the process practical and efficient.That changed the role Creature Bionics could play. Ace was not only arriving as a performer. He could provide motion capture, help cast performers, direct movement, and support production from a broader creative and technical position.“Having the suit gave me the opportunity to be like a coordinator,” Ace says. “It was being a part of the production rather than just being the actor.”For human and creature performance sessions, Xsens gives teams the speed, flexibility, and high-quality motion data needed to keep production moving. For Creature Bionics, that flexibility helped turn motion capture into a broader production service. It was not only about data. It was about performance, casting, direction, and giving the team a way to keep characters moving.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783500171042-Ace+Ruele+-+Lord+of+the+Fallen+Customer+Case+-+Xsens+Link+3.jpg" style="width:100%px" class="fr-fic fr-dib" />Ace Ruele uses Xsens Link with creature stilts to capture non-human movement for fantasy character performance.<h3 id="isPasted">Meet Creature Bionics</h3>Creature Bionics focuses on creature and fantasy character performance. The company supports productions from early character development through performance direction, casting, training, mocap, and practical creature movement work.Ace explains the idea simply: how do you take a creature that can be performed by an actor and help that actor create the strongest possible performance?That includes understanding the physical demands of a character, advising on what can be handled through VFX, stunts, wires, mocap, or practical movement, and then helping cast or train the right performer. In some cases, Creature Bionics also builds quick previs using real-time tools, giving directors and teams an early look at how a creature could move before the final asset is ready.“We’re actor focused, we’re performance focused,” Ace says. “How do we get a creature that can be played by an actor to create the best performance?”For Lords of the Fallen, that meant Creature Bionics was not only providing mocap data. It helped connect performance, casting, direction, and capture into one flexible production workflow.

With Xsens Motion Capture

Lords of the Fallen: capturing creature combat

For over a decade now, Berlin-based BigRep has been the go-to hardware supplier for large-scale FDM 3D printing. It’s no surprise why: the company’s flagship BigRep ONE 3D printer, with a build space of one cubic meter, set the standard for large-format polymer AM upon its release in 2014 and has since empowered users from across diverse segments, from education to aerospace, to create big—really big—parts. As the BigRep ONE has shaped the evolution of large-format 3D printing applications, BigRep has also continued to evolve its technology, continually upgrading and developing both hardware and software for improved print results and greater productivity. This commitment to growth has culminated in the launch of the BigRep ONE.5X, the latest generation of its flagship solution, which delivers the same large build volume with greater intelligence and automation. As we’ll see, the BigRep ONE.5X is unlocking greater productivity in terms of print speed, machine up times, and consistency. Not only that, the 3D printing solution is built for autonomous, hands-off 3D printing, ensuring that the printing process remains easy and accessible for all types of users.  <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783446499908-BigRep-one-Twin-mode-square-d1f53aa598f9ab6f7cabd4a163ba7330-jrvxk2filqzu.png" style="width:677px" class="fr-fic fr-dib" />BigRep One Twin Mode. Source: BigRep<h2>Building on Success </h2>Before we explore the many new features and upgrades in the BigRep ONE.5X, let’s take a quick look at the generations of BigRep ONE that came before and the legacy of the BigRep name.  For this we’ll have to rewind back to February 2014, when the company announced—for the very first time—its large-scale 3D printer the BigRep ONE. At the time, the response within the additive and broader manufacturing markets was one of enthusiasm, for this was the biggest FDM 3D printer to ever launch. Today, the BigRep ONE system (now on its fifth generation) has a global installed base of over 500 and is a key manufacturing solution in the creation of prototypes, tools, and end-use parts in education, aerospace, architecture, automotive, defense, design, and more.  Over the years, the BigRep ONE solution has always maintained its key strengths: its one-cubic-meter build volume, its open source materials, and its user accessibility. At the same time, continuous upgrades have expanded and maximized these strengths. For instance, the introduction of dual PEX2 extruders in 2024 enabled the printing of more materials as well as significantly higher flow rates. Remote printing capabilities also helped to increase productivity for users by enabling them to manage their print jobs from anywhere.    <iframe id="6a0302387d5ba8f97f8d62af" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/bigrep-one5x?iframe=true" frameborder="0" scrolling="no"></iframe>The latest generation of its flagship system, the BigRep ONE.5X, takes the system’s large-format printing capabilities to another level, where hardware upgrades and intelligent tools come together for reliable, hands-off industrial AM.   <h2>Maximizing Productivity with Automation </h2>The BigRep ONE.5X is at the cutting-edge of large-scale FDM 3D printing. With innovations carried over from BigRep’s fourth-generation system, the BigRep ONE.5, the new system builds on over 10 years of large-scale AM engineering.   Among the innovations to be carried over are the aforementioned dual PEX2 extruders, which are more lightweight and faster than previous extruders. Notably, this extrusion system features a dual-drive feeding wheel that facilitates more precise and reliable printing even as flow rates are increased. The PEX2 extruders are also compatible with high-performance diamond nozzles, which makes it possible to print tough materials like fiber-reinforced and engineering thermoplastics. Other innovations from the preceding generation include BigRep’s SWITCHPLATE removable and flexible print bed surfaces, which come in a variety of coatings for different materials; and the Keep-Dry Box for filament storage and preservation.  Now for the new. In the BigRep ONE.5X, BigRep has rolled out a host of automation-focused upgrades geared towards lights-off, round-the-clock printing.  These upgrades include: <ul><li>XYZ auto-calibration and adaptive bed mesh leveling for consistent first layers.</li><li>A vibration compensation algorithm for cleaner, more accurate parts.</li><li>An intuitive touchscreen interface driven byBigRep’sproduction OS architecture, that includes smart material build profiles and remote monitoring via BigRep CONNECT.</li><li>Auto-sequential printing, a smart print queue function that automatically prints the next job in the queue (withsamematerial/nozzle settings) if there is room on the build platform.</li><li>Relay mode, a new function where the 3D printer will use the backup extruder if the primary extruder runs out of filament (providing the material and nozzle match). </li></ul>This combination of upgrades has delivered measurable productivity improvements for BigRep’s large-format 3D printing, including up to 10 times faster print rates without sacrificing quality. And it’s not just faster print speeds that lead to productivity gains: with improved extrusion accuracy (particularly at corners and edges); higher print success rates thanks to auto calibration and SWITCHPLATE-improved adhesion; maximized platform usage with auto-sequential printing; and autonomous, hands-off 3D printing, the BigRep ONE.5X is BigRep’s most advanced, productive system to date.  <a href="https://bigrep.com/applications/industrial-design-prototypes/" target="_blank" rel="noopener noreferrer"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783446746877-BigRep-ONE.5_Mid_Switchplate-scaled-square-17eeb6750b5b16e3588fce495de61c93-spktly8gxhdz.jpg" style="width:680px" class="fr-fic fr-dib" />BigRep ONE.5X Switchplate. Source: BigRep</a>The new system also continues BigRep’s tradition of accessibility, with an improved user experience driven by more automation (i.e. less manual intervention requirements) and intuitive user interfaces. This allows more levels of users to 3D print large-scale parts, opening up the technology to more people and, ultimately, more ideas. <h2>Upgrading the BigRep ONEUpgrading the BigRep ONE </h2>In line with its accessibility ethos, BigRep is giving existing customers (specifically of the BigRep ONE.4 and the BigRep ONE.5) the ability to upgrade their systems with the ONE.5X Controls Upgrade Kit. New customers or those looking to expand their fleets of large-format 3D printers can also purchase the fifth generation system outright.  Those interested in upgrading older generations of BigRep’s 3D printing technology can take their existing hardware to the next level in terms of motion control, automation, print accuracy, overall efficiency, and ease of use. The upgrade kit, which will be installed by BigRep-certified technicians, comes with a range of components, including a Mainboard Kreuzboard + CB-XB-MAIN, motor drivers, an integrated control system upgrade, pre-configured firmware package, and a bed probe sensor upgrade.  Once installed, users will benefit from faster print speeds (up to 250 mm/s), greater precision for edges and corners, and improved motion control. Moreover, the combination of the intuitive interface, remote monitoring, and latest slicer iteration (Blade 5.16.0) means that users of all experience and skill levels can print high-quality parts with consistency and repeatability, whether it's a turbine prototype, a boat fender, or an art installation. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783447132017-smaller+ONE5-pellet-extruder_studio_2-square-9e744ea52136847e4422309e9452c933-p8cuf1ve9tn5.jpg" style="width:675px" class="fr-fic fr-dib" />BigRep ONE with Pellet Extruder. Source: BigRep<h2>BigRep, a Leader in Big PrintingBigRep, a Leader in Big Printing </h2>With new hardware and software upgrades, the BigRep ONE.5X represents where large-scale 3D printing is headed: towards greater productivity, automation, and accessibility. Moreover, BigRep’s commitment to evolving and improving its large-format 3D printing solution is in line with its commitment to its growing customer base. This is illustrated by the company’s upgrade offers, which enable customers to boost their existing hardware solutions without having to invest in a wholly new system.  Ultimately, the BigRep ONE.5X delivers greater quality, productivity, and efficiency in <a href="https://bigrep.com/bigrep-one/" target="_blank" rel="noopener noreferrer">large-format 3D printing</a> for end users across industries like architecture, automotive, aerospace, defense, design, education, and even more.

Unlock increased productivity with the BigRep ONE.5X, a cutting-edge large-scale FDM 3D printing.

BigRep ONE.5X Unlocks Hands-Off Large-Scale 3D Printing

A definitive guide to Verilog HDL — syntax, modeling styles, and its role in the FPGA/ASIC design flow, explained for engineers.

Verilog HDL: Fundamentals for FPGA and ASIC Design

When a standard Large Language Model (LLM) is confronted with a problem, it tries to solve it by matching it to similar information it has seen before, and then give an answer based on those past patterns. But how it decides which information to use and what value it gives to different pieces of information can be somewhat inscrutable from the outside.The LLM MiCRo (Mixture of Cognitive Reasoners) is architecturally divided into four specialized areas that act like different parts of the human brain, allowing users to have more control over how it approaches a question, and to better understand how it comes to its answers. The model, which was presented at the International Conference on Learning Representations, comes from the NLP Lab, part of the School of Computer and Communication Sciences (IC), and the NeuroAI Lab, part of IC and the School of Life Sciences at EPFL.The four expertsTo create MiCRo, researchers identified four regions of the brains specializing in different functions, which they call ‘experts’: language, logic, social reasoning, and world knowledge.“The brain is organized into specialized regions, each tuned to handle a specific function. So far, we don’t see this division of labor as clearly in current language models,” says Badr AlKhamissi, a PhD candidate leading this research. “We picked four brain regions that neuroscientists know well and gave the model its own specialized modules, each one trained to be analogous to one of those brain regions.”An LLM usually functions as a stack of layers that a problem or question can be processed through. In the case of MiCRo, each layer is divided into the four different experts. You give a sentence to the model starting at layer one, for example “The cat is asleep". Then within this layer, the router can choose one expert for the first word “the”, but a different epxert for second word “cat” and so on, making it modular and highly adaptable.“Each word of a sentence can go to different experts,” AlKhamissi explains. “So one sentence can actually be processed by multiple experts at each layer.”Consider a prompt like: "Emma wants to split a CHF 60 dinner bill among three friends, but she knows that Jake lost his job last week and is too proud to say he's struggling." A purely mathematical module handles the arithmetic: CHF 60 divided by three is CHF 20 each. But the social reasoning module picks up on something subtler: Emma's awareness of Jake's situation, his unspoken pride, and the implicit suggestion that she might quietly cover his share. Both kinds of reasoning are needed to fully understand what's going on, and in MiCRo, each aspect of the prompt is routed to the expert best equipped to handle it.“When we see how the model works, we can see that it routes the words that relate to the social aspects to the social expert, and when it does the mathematical part, it routes those numbers to the logic expert.”This separation makes it easier to see how the model is ‘thinking’ and why it makes certain decisions. It also means decisions can be steered – for example, you can decide to increase the impact of the social expert, or suppress the logic expert, depending on what kind of model you want to use in a certain situation.“In traditional LLMs, you can do this via prompting by telling the model to make the output more social or make it more related to emotions,” AlKhamissi says. “But here, this is done by intervening in the architecture itself without doing any prompting.”“A virtuous circle”To create MiCRo, the EPFL team worked with Greta Tuckute, a neuroscientist from Harvard and MIT, to understand which parts of the human brain are activated by different problems, and then applied that learning to the model.To identify the region analogous to the ‘logic’ expert in the brain, neuroscientists give humans demanding tasks, such as hard mathematical equations, and less demanding tasks, like easy mathematical equations, and then recorded their brain activity to find which brain regions are the most active for the demanding tasks versus non-demanding tasks. AlKhamissi’s team then did the same for the model, giving it demanding mathematical equations to see which experts would be most activated.“The cool thing is we just used exactly what they do in neuroscience, but in the model. And the model was able to identify those experts on its own.”While neuroscience informs the model, the model also informs the understanding of the brain, potentially allowing neuroscientists to discover the contributions of different areas for a given problem or question; for example that a certain sentence activates the language areas 20%, the mathematical areas 50%, and the social reasoning areas 40%.“For my PhD work, I have been interested in this virtuous circle between neuroscience and AI. In one direction, we use findings and insights from neuroscience about the brain and integrate them into language models,” AlKhamissi says, “and now, with models like MiCRo, we can explore the other direction and ask how we can use AI models to help us understand the brain in a better way.”

An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.

EPFL researchers create an AI model that thinks like we do

Nuttida Rungratsameetaweemana is challenging a story neuroscience has told for decades. According to the conventional account, our eyes collect raw information and relay it through a series of nerves and waystations that lead deep into the brain, eventually reaching the cortex. There, the thinking begins as information is processed and put to use for higher tasks such as reasoning, judgment, and decision-making.Her group's work is complicating that account. Last year, the team <a href="https://www.engineering.columbia.edu/about/news/how-thoughts-influence-what-eyes-see">published</a> fMRI scans showing unexpected levels of activity in the earliest visual areas of the cortex, the regions that first receive visual signals. Rather than passively relaying what the eyes take in, those early areas seemed to process the same information differently depending on what the research participant was doing. When asked to sort shapes by one set of rules, a participant's early visual system behaved one way. When asked to apply a different set of rules to the same shape, it behaved differently.In a new paper published today in PLOS Biology, Rungratsameetaweemana and her team at Columbia Engineering show how the brain might pull this off. They built a simple neural network that follows many of the rules that govern real brains. Like the brain, their model contained one class of neurons that drive other neurons to fire and another class that suppress firing.The team had the model perform a task similar to what the human participants had done while in an fMRI machine. When the researchers looked inside the model to see how the neural network had solved the problem, they found that it relied on one arrangement of digital neurons. Inhibitory neurons that suppress other inhibitory neurons seem to pass key information from the “thinking” part of the system to the “sensing” component of the system. To test whether that wiring was essential, they weakened those connections in the model, and its ability to switch between tasks collapsed. Weakening other types of connections left performance largely intact. The pattern held up against the living brain as well. In recordings from the visual cortex of mice, silencing the inhibitory cells that anchor this circuit reduced the cortex's ability to track the task context, just as the model predicted.We caught up with Rungratsameetaweemana, Maa-Liao Assistant Professor of Biomedical Engineering, to learn more about the research. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783422903370-1783422903370.png" class="fr-fic fr-dib" />This builds on last year's fMRI study. Why turn to AI models next?A brain scan gives you a picture of the whole brain, but it’s coarse — you can't see what individual cells are doing, or what's happening at the level of circuits. Seeing which regions light up and patterns of activity gave us a reason to go deeper. To get at the mechanism, we needed something we could take apart and change, so we turned to neural models. We build these from scratch, so it’s possible to see exactly what the network is doing to solve the task.Why keep the models so simple?If a model has abilities the brain doesn't have, then anything we find inside it won't tell us much about real brains. So we did the opposite and built something that only includes features we know to be true about biology. A lot of that builds on earlier work from Tomas Gallo Aquino and Robert Kim, the paper's co-first authors, among other studies. We know there are excitatory and inhibitory neurons, so we built those in. We know the brain is organized in a hierarchy, so in the second part of the paper we gave the network two regions: a sensory module that receives input directly, and a higher-level module downstream. Then we can ask how the network uses those ingredients to do the task.Why do these inhibitory-on-inhibitory connections matter so much?They give the system very fine control over how information gets represented. These inhibitory neurons turn out to be really important for keeping everything well-controlled, for making sure the right thing is represented in the right way. There are four kinds of connections you can have between these cells, and the one that matters for this kind of flexible processing is inhibition acting on inhibition. We know it's important, because the model fails when we take it away. We don't yet know why it has to be this particular wiring. That question is an important topic for research teams across the world.How did you get started with this line of research?Back in 2015, I began working with patients who are missing the hippocampus, the region that lets us form and hold onto memories. If the brain were truly modular, losing that middle piece should leave you unable to do a great many things. But it doesn't. These patients can still do all sorts of tasks. That was the first real evidence, to me, that the early regions of the brain are doing more than just relaying sensory information. And it points to something useful: if information is stored redundantly, you can lose a part and still get by. I think that's how the brain actually works.What could it mean for AI?Compare the brain to something like ChatGPT or a large language model. We can do far more, across far more situations, on a tiny fraction of the energy — and without being trained on the whole internet. The brain got there through evolution, through the redundancy built into its wiring. Our models are recurrent neural networks, which are quite different from the transformers behind today's large language models. The goal is to work out these principles one by one and use them to make AI leaner and more adaptive. This inhibition-on-inhibition motif is one of them.What's next?We've gone back to humans. We're working closely with clinical collaborators who monitor epilepsy patients with electrodes placed deep inside the brain, letting us record neural activity directly while those patients perform cognitive tasks. These fine-grained measurements will give us data to test our hypotheses against real neural activity.

The circuitry underlying the remarkable efficiency of the brain is coming into sharper focus.

The Circuit that Lets Your Brain Think and See

Single electrically charged atoms – ions – have been successfully used for some time as quantum bits in quantum computers and quantum sensors. Unlike the bulky ion traps of the early years, there are now miniaturised chips in which ions can be trapped and manipulated only a hair’s breadth above the surface of the chip. This has many advantages, but also one decisive drawback: noisy electromagnetic fields coming from the chip itself can severely impair the sensitive quantum states of the ions and hence the performance of the computer or sensor.A team of researchers led by Jonathan Home, a professor at the Institute for Quantum Electronics at ETH Zurich, has now developed a technique that allows them to create a very precise three-dimensional map of electric and magnetic fields very close to the surface of the chip. In the future, in this way materials for chip production can be better optimised and tested for their suitability for use in quantum applications. The results of their research were recently published in the scientific journal Science Advances.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783420674228-heading+%289%29.jpg" style="width:100%px" class="fr-fic fr-dib" />Apparatus for the experiments: The trap chip is housed in the gold-coloured holder; on the chip, a single trapped ion was able to measure electromagnetic fields and create a three-dimensional map of them.  (Image: Edgar Brucke / ETH Zurich)<h2 id="isPasted">Arbitrary movement in three dimensions</h2>“Two years ago, we developed a novel chip trap that allowed to move an ion arbitrarily in three dimensions”, explains doctoral student Tobias Sägesser. Whereas in conventional traps, charged atoms are trapped by oscillating electric fields in the radio frequency range, the ETH researchers use a so-called Penning trap that is based on a combination of static electric and magnetic fields.“This has two significant advantages”, says Shreyans Jain, also a doctoral student in Home’s group: “On the one hand, this allows us to position the ions in three dimensions, which isn’t possible using radio-frequency traps. On the other hand, the absence of oscillating fields within the trap makes it easier to detect tiny oscillating fields on the chip.”The measurement of very small oscillating electric fields works as follows: first, the researchers use laser beams to cool a trapped beryllium ion to the point that it reaches the lowest quantum mechanical oscillation state in the trap – in other words, it effectively comes to a standstill. Then they change the electric voltages on the electrodes of the trap so that the ion moves to the desired point above the chip. “In doing so, we can vary the height above the chip from 50 micrometres up to 450 micrometres and scan an area of 200 by 200 micrometres”, says Sägesser.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783420638141-1783420638141.png" class="fr-fic fr-dib" />This is how the researchers conducted their experiment: using laser beams (red), the trapped ion (green) was first cooled down. Then it was positioned at a specific point at which the stray fields were to be measured.  (Illustration: Tobias Sägesser / ETH Zurich)<h2 id="isPasted">Precise field measurement via ion oscillations</h2>Once the final destination is reached, the researchers have to wait. The oscillating electric fields on the chip now continuously jolt the ion so that it starts oscillating back and forth in the trap more and more strongly. This means that its quantum mechanical oscillation state changes, which the researchers can measure using additional laser pulses after a waiting time. From this change they can then calculate the strength of the disturbing electric field.“In this way, we have set a new record for the most sensitive measurement of an oscillating electric field in a chip trap”, says Sägesser. Within a measurement time of one second, he and his colleagues were able to detect an oscillating field with an amplitude of just 10 nanovolts per meter. For comparison: even at a distance of several kilometres the electromagnetic field of a mobile phone is still ten thousand times stronger.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1783420730121-1783420730121.png" class="fr-fic fr-dib" />Experimental setup, viewed from above: The chip trap in which the experiments took place is located in the container beneath the silver-coloured lens. (Image: Tobias Sägesser / ETH Zurich)The researchers can also measure static electric fields by visualising the deflection of the ion from its resting position by a stray electric field using a microscope. To measure magnetic fields, they determine the variation of the ion’s energy levels.<h2>A new tool for determining material properties</h2>“For more than thirty years, researchers have tried to find out where the electric field noise close to a chip comes from”, says Home. His new method now makes it possible to measure these fields very precisely and with 3-d spatial resolution, and to compare the results to model calculations. This allows one to distinguish between different possible sources of interference. Furthermore, the Penning trap can be temporarily detached completely from all outside voltage sources, such that detrimental influences from the environment can be excluded. “Until now, one had to make certain assumptions about these influences without knowing whether they were correct”, Home explains.Looking ahead, Home sees the new method as an additional tool for the characterisation of materials. For instance, the ions can be used to scan different areas in a chip with different surface materials to determine which material creates the smallest electric fields. The manufacturing processes for the chips could also be optimised in this way.

Researchers at ETH Zurich have developed a method that uses a single ion to detect electromagnetic fields above a surface and to create a three-dimensional map of them. In the future, this approach can be used to improve chips for quantum computers and quantum sensors.

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