 A semiconductor chip designed via tapeout

Unveiling the Evolution, History, and Significance of the Tapeout Process in Semiconductor Manufacturing.

Tapeout in Semiconductor Manufacturing: An In-depth Exploration

Cell-based therapies, which use biological cells to treat diseases or injuries in the body, offer enormous potential from facilitating drug delivery to enabling more effective treatment of obesity, diabetes, and cancer. Generating on-site oxygen is a critical component of activating such therapies, because there is a high metabolic demand on these densely packed cells, and maintaining cell potency becomes a challenge, due to factors like immune response and dissolved oxygen. As part of an ongoing, multidisciplinary collaboration, a group of researchers from Carnegie Mellon University, Northwestern University, Georgia Institute of Technology and Rice University have identified a novel device that makes on-site oxygen for biological cells transplanted inside the body.Every cell is closely situated to tiny blood capillaries due to its need of sufficient oxygen supply to function. Current approaches to generating on-site oxygen for cell-based therapies have drawbacks including size and supply capacity. Some options introduce gaseous oxygen from outside the body to tackle the problem but are bulky; others are limited by supply capacity, like a can of water that cannot be refilled once it’s been emptied. This new device boasts a compact footprint and relies on the abundance of water in the human body, making it an excellent choice for implantation, along with ensuring a sustainable and lasting source of oxygen.<blockquote>Our device uses electricity and water to make oxygen for the cells—it’s as simple as a Chemistry 101 electrolysis experiment we all did as children, but we’re conducting it in a smarter manner.<cite>Tzahi Cohen-KarniProfessor , <cite id="isPasted">Biomedical Engineering and Materials Science and Engineering</cite> </cite></blockquote>“Our device uses electricity and water to make oxygen for the cells—it’s as simple as a Chemistry 101 electrolysis experiment we all did as children, but we’re conducting it in a smarter manner,” said <a href="https://engineering.cmu.edu/directory/bios/cohen-karni-tzahi.html" target="_blank">Tzahi Cohen-Karni</a>, professor of <a href="https://www.cmu.edu/bme/" rel="noopener" target="_blank">biomedical engineeringOpens in new window</a> and <a href="https://www.mse.engineering.cmu.edu//index.html" rel="noopener" target="_blank">materials science and engineeringOpens in new window</a> at Carnegie Mellon University. “We are using unique materials, including iridium oxide to produce oxygen, while avoiding the production of harmful byproducts, such as chlorine or excess hydrogen peroxide. By controlling how much electricity our device uses, we can change how much oxygen it produces.”The biggest advantage and differentiator of the group’s research, recently published in <a href="https://www.nature.com/articles/s41467-023-42697-2" rel="noopener" target="_blank">Nature CommunicationsOpens in new window</a>, is that it bridges the gap between energy science, biomedical engineering, and electronics. Water splitting research, which is geared toward energy conversion and storage, guided exploration of which materials could safely be used for oxygen evolution reaction in the human body, which led to the group incorporating iridium oxide.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1700813771065-1109-em-boosting-oxygenation-1.png" style="width: 766px;" class="fr-fic fr-dib">A schematic illustration of electrocatalytic on-site oxygenation. Source:&nbsp;Tzahi Cohen-Karni lab (CMU Engineering)“Unlike other approaches out there, we are using electrocatalysis, to reduce the energetic cost to generate the oxygen, and we are also tailoring the conditions of our device to provide oxygen without any byproducts, such as bleach,” added Inkyu Lee, materials science and engineering Ph.D. student. “We are also operating our device under conditions that will allow oxygen generation without bubbles that can disrupt the device.”The cells in this study were biologically engineered to produce human leptin, a key component in the body’s mechanism for weight control and regulation of circadian rhythms, which is also being investigated by the group through the <a href="https://engineering.cmu.edu/news-events/news/2021/06/07-circadian-clocks.html" target="_blank">DARPA Normalizing Timing of Rhythms Across Internal Networks of Circadian Clocks (NTRAIN) project</a>. The NTRAIN effort is managed by Jonathan Rivnay from Northwestern University.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1700813803284-1109-em-boosting-oxygenation-2.png" style="width: 766px;" class="fr-fic fr-dib">A representative set of 3D-rendered immunofluorescence images after 21 days in vitro oxygenation.&nbsp;Source:&nbsp;Tzahi Cohen-Karni lab (CMU Engineering)Additionally, some aspects of this published work will be used in the ARPA-H funded&nbsp;<a href="https://engineering.cmu.edu/news-events/news/2023/09/26-cancer-implant-tech.html" target="_blank">Targeted Hybrid Oncotherapeutic Regulation (THOR) project</a>, which is combining the group’s expertise in an effort to develop sense-and-respond technology to treat ovarian, pancreatic and other difficult-to-treat cancers. The THOR projects is managed by Omid Veiseh from Rice University.“We are the pioneers of this electrocatalytic oxygenation technology, and we have concrete plans to translate our devices to clinical practices in the real world,” emphasized Cohen-Karni. “We are not focusing on only one disease but instead, pushing to translate our work to various disease models.”As this work moves forward, the team is concentrating on perfecting highly stable materials that can operate for months in the body and plan to deploy their cutting-edge approaches to treat chronic diseases.The effort was co-led by Tzahi Cohen-Karni (MSE/BME, CMU) and Jonathan Rivanay (NU) and researchers Inkyu Lee (MSE, CMU) and Abhijith Surendran (NU). Other Carnegie Mellon participants involved in the research were Douglas Weber, professor of mechanical engineering; Adam Feinberg, professor of biomedical engineering; Sharon John, research associate; Seonghan Jo and Yingqiao Wang, materials science and engineering Ph.D. students; Tim Schwartzkopff, chemical engineering Ph.D. student; Omar El-Refy, physics Ph.D. student; and Ian Gimino, chemical engineering undergraduate student.

To advance cell-based therapies, researchers have identified a novel device that makes on-site oxygen for biological cells transplanted inside the body.

Boosting oxygenation for transplanted cell-based therapies

The semiconductor industry can be described in many ways. Ingenious, impactful, and unpredictable are three descriptors that come to mind. &nbsp;There are many more, but one word that doesn’t apply here is boring. In the 1960s, the late Gordon Moore advanced the insightful observation that chip complexity would follow an exponential path. His now-famous Moore’s law charted the course for the semiconductor industry for over 50 years.The exponential effects predicted by <a href="https://www.synopsys.com/glossary/what-is-moores-law.html" target="_blank">Moore’s law</a> have profoundly impacted the semiconductor industry, all the industries that use semiconductors, and the world at large.&nbsp;Back in 2015, when Moore’s law turned 50, <a href="https://www.scientificamerican.com/article/moore-s-law-keeps-going-defying-expectations/" target="_blank">Scientific American published an article on the longevity and impact of Gordon Moore’s prediction</a>. In that article, the exponential effects of Moore’s law were applied to the automotive industry. The article pointed out:If a 1971 Volkswagen Beetle had advanced at the pace of Moore’s law over the past 34 years, today “you would be able to go with that car 300,000 miles per hour. You would get two million miles per gallon of gas for the mere cost of four cents.”If only Moore’s law could continue forever. But alas, nothing lasts forever. In recent times, Moore’s law has begun to slow down. It’s still predicting (and delivering) semiconductor advances, but it takes more time and money to arrive at those advances, and once there, the benefits aren’t as great as they used to be.Innovation will not be slowed, especially in the semiconductor industry. So, something else must be added to keep things moving at the customary exponential pace. That something else is multi-die systems. Read on to get an industry-wide look at the move toward multi-die systems.<section id="isPasted"><h2>The World According to MIT Technology Review Insights</h2>MIT Technology Review Insights (<a href="https://www.technologyreview.com/?utm_medium=bing&msclkid=fe0081936d391c114f1be58c586ec489" target="_blank">MITTRI</a>) is the custom publishing division of MIT Technology Review, the longest-running technology magazine. MITTRI has been part of the Massachusetts Institute of Technology (MIT) for over 100 years.&nbsp;This organization is famous for its quality research and Synopsys was excited to sponsor a research piece on the move to multi-die systems. Titled <a href="https://www.technologyreview.com/2023/03/31/1070527/multi-die-systems-define-the-future-of-semiconductors/" target="_blank">Multi-die Systems Define the Future of Semiconductors</a>, the report explores the forces that demand a new method of design, one that is not solely reliant on the exponential scaling predicted by Moore’s law. The reasons for that change, the challenges to implementing it, and the benefits awaiting the semiconductor industry and the world are all discussed.&nbsp;</section><section><h2>Multi-Die Systems Report Methodology</h2>The report was driven by three research methods:<ul><li>Independent market research</li><li>A poll of the MIT Technology Review Global Insights Panel, including executives from diverse industries</li><li>Interviews with semiconductor industry experts</li></ul>The poll drew from over 300 responses representing more than 12 industries across the globe. About half the responses came from C-suite executives or directors. The distribution of company sizes and geographic location is shown in the figure below.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5OTg4MTY2ODM5LW11bHRpLWRpZS1wb2xsLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">Survey respondent's statistics.<section id="isPasted">There were many interesting and informative insights because of this poll. One clear statistic that illustrates the move to multi-die systems: 38% of firms polled are looking at multi-die systems for future products. The movement is real and it’s happening now.The third part of the report, the interviews with industry leaders, reveals some telling insights about where the industry is currently and where it needs to go. These passages represent a great opportunity to hear directly from those driving next-generation products to understand their direction, care-abouts, and future aspirations.The panel of experts consisted of:<ul><li>Uri Frank, Vice President of Engineering, <a href="https://about.google/" target="_blank">Google&nbsp;</a></li><li>Sassine Ghazi, President and Chief Operating Officer, <a href="https://www.synopsys.com/" target="_blank">Synopsys&nbsp;</a></li><li>Patrick Moorhead, Founder, CEO, and Chief Analyst, <a href="https://moorinsightsstrategy.com/" target="_blank">Moor Insights &amp; Strategy&nbsp;</a></li><li>François Piednoël, distinguished chief mSoC architect, <a href="https://mbrdna.com/" target="_blank">Mercedes-Benz Research &amp; Development North America&nbsp;</a></li><li>Gerry Talbot, corporate fellow, <a href="https://www.amd.com/en.html" target="_blank">AMD&nbsp;</a></li><li>Kevin Zhang, senior vice president of Business Development, <a href="https://www.tsmc.com/english" target="_blank">TSMC&nbsp;</a></li></ul>Assembling a panel of experts like this is no small feat. It’s a testament to the power of the MIT brand and the importance of the trend.</section><section><h2>Learn More About Multi-Die Systems</h2>Synopsys is committed to working with our ecosystem partners and ushering in a new era of multi-die systems. We are bringing a comprehensive multi-die system solution, including EDA and IP products and deep system design expertise, to the task and we can’t wait to see where these new capabilities will take us.Get an industry-wide look at the move toward multi-die systems:&nbsp;<a href="https://www.synopsys.com/content/dam/synopsys/solutions/multi-die/white-papers/MITTRI.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Access your copy of the new MITTRI report here</a>.</section></section>

A report explores the forces that demand a new method of design, one that is not solely reliant on the exponential scaling predicted by Moore’s law.

Discovering the Future of Multi-Die Systems

In those latter applications, lasers that can emit extremely short pulses—those on the order of one-trillionth of a second (one picosecond) or shorter—are especially useful. Using lasers operating on such small timescales, researchers can study physical and chemical phenomena that occur extremely quickly—for example, the making or breaking of molecular bonds in a chemical reaction or the movement of electrons within materials. These ultrashort pulses are also extensively used for imaging applications because they can have extremely large peak intensities but low average power, so they avoid heating or even burning up samples such as biological tissues.In a paper appearing in the journal Science, Caltech's <a href="https://www.eas.caltech.edu/people/amarandi" target="_blank">Alireza Marandi</a>, an assistant professor of electrical engineering and applied physics, describes a new method developed by his lab for making this kind of laser, known as a mode-locked laser, on a photonic chip. The lasers are made using nanoscale components (a nanometer is one-billionth of a meter), allowing them to be integrated into light-based circuits similar to the electricity-based integrated circuits found in modern electronics."We're not just interested in making mode-locked lasers more compact," Marandi says. "We are excited about making a well-performing mode-locked laser on a nanophotonic chip and combining it with other components. That's when we can build a complete ultrafast photonic system in an integrated circuit. This will bring the wealth of ultrafast science and technology, currently belonging to meter-scale experiments, to millimeter-scale chips."Ultrafast lasers of this sort are so important to research, that this year's Nobel Prize in Physics was awarded to a trio of scientists for the development of lasers that produce attosecond pulses (one attosecond is one-quintillionth of a second). Such lasers, however, are currently extremely expensive and bulky, says Marandi—who notes that his research is exploring methods to achieve such timescales on chips that can be orders of magnitude cheaper and smaller, with the aim of developing affordable and deployable ultrafast photonic technologies."These attosecond experiments are done almost exclusively with ultrafast mode-locked lasers," he says. "And some of them can cost as much as $10 million, with a good chunk of that cost being the mode-locked laser. We are really excited to think about how we can replicate those experiments and functionalities in nanophotonics."At the heart of the nanophotonic mode-locked laser developed by Marandi's lab is lithium niobate, a synthetic salt with unique optical and electrical properties that, in this case, allows the laser pulses to be controlled and shaped through the application of an external radio-frequency electrical signal. This approach is known as active mode-locking with intracavity phase modulation."About 50 years ago, researchers used intracavity phase modulation in tabletop experiments to make mode-locked lasers and decided that it was not a great fit compared to other techniques," says Qiushi Guo, the first author of the paper and a former postdoctoral scholar in Marandi's lab. "But we found it to be a great fit for our integrated platform.""Beyond its compact size, our laser also exhibits a range of intriguing properties. For example, we can precisely tune the repetition frequency of the output pulses in a wide range. We can leverage this to develop chip-scale stabilized frequency comb sources, which are vital for frequency metrology and precision sensing," adds Guo, who is now an assistant professor at the City University of New York Advanced Science Research Center.Marandi says he aims to continue improving this technology so it can operate at even shorter timescales and higher peak powers, with a goal of 50 femtoseconds (a femtosecond is one-quadrillionth of a second), which would be a 100-fold improvement over his current device, which generates pulses 4.8 picoseconds in length.<hr>The paper describing the research is titled "<a href="https://www.science.org/doi/10.1126/science.adj5438" target="_blank">Ultrafast mode-locked laser in nanophotonic lithium niobite</a>" and appears in the November 9 issue of Science. Co-authors are Benjamin K. Gutierrez (MS '23), graduate student in applied physics; electrical engineering graduate students Ryoto Sekine (MS '22), Robert M. Gray (MS '22), James A. Williams, Selina Zhou (BS '22), and Mingchen Liu; Luis Ledezma (PhD '23), an external affiliate in electrical engineering; Luis Costa, formerly at Caltech and now with JPL, which Caltech manages for NASA; and Arkadev Roy (MS '23, PhD '23), formerly of Caltech and now with UC Berkeley .

Lasers have become relatively commonplace in everyday life, but they have many uses outside of providing light shows at raves and scanning barcodes on groceries. Lasers are also of great importance in telecommunications and computing as well as biology, chemistry, and physics research.

Ultrafast Lasers on Ultra-Tiny Chips

When Carnegie Mellon’s <a href="https://engineering.cmu.edu/mfi/index.html" rel="noopener" target="_blank">Manufacturing Futures Institute</a> (MFI) issues its annual call for proposals each fall, it makes clear its objectives for offering seed funding to groups of CMU faculty whose research is aligned with MFI’s mission to inspire, engineer, and lead technological and workforce advances for agile, intelligent, efficient, resilient, and sustainable manufacturing.They are interested in funding research that falls into one of MFIs strategic priority areas; plays to CMU’s strengths in fields, such as AI and machine learning, coupled with advanced manufacturing technologies, such as robotics and additive manufacturing; benefits from convergent research and expertise from different disciplines; and shows potential for seeding the future of advanced manufacturing with new ideas that fuel innovation and attract future investments.“We receive so many great proposals across a wide variety of advanced manufacturing topics that deciding which to fund is difficult. We focus on the ones that propose innovative interdisciplinary approaches that can truly advance the state of the art. Industrial relevance is important as well, given our interest in technology transition,” said <a href="https://engineering.cmu.edu/directory/bios/wolf-sandra-devincent.html" target="_blank">Sandra DeVincent Wolf</a>, executive director of the MFI.<blockquote>We focus on funding projects that propose innovative interdisciplinary approaches that can truly advance the state of the art. Industrial relevance is important as well, given our interest in technology transition.<cite>Sandra DeVincent Wolf,&nbsp;Executive Director, <cite id="isPasted">Manufacturing Futures Institute</cite></cite></blockquote>She and <a href="https://engineering.cmu.edu/directory/bios/fedder-gary.html" target="_blank">Gary Fedder</a>, faculty director of MFI, reserve the right to ask for modifications to a proposal when timelines are too aggressive, funding amounts are not fully justified, or the project is missing a partner with a vital set of skills and expertise.That was the case when faculty members <a href="https://engineering.cmu.edu/directory/bios/leduc-philip.html" target="_blank">Phil LeDuc</a>, <a href="https://engineering.cmu.edu/directory/bios/ozdoganlar-burak.html" target="_blank">Burak Ozdoganlar</a>, and <a href="https://engineering.cmu.edu/directory/bios/ren-xi.html" target="_blank">Charlie Ren</a> requested funding last year for the next phase of their ice printing research.The team developed a novel approach to 3D print ice structures that are small enough to create vasculature in artificial tissue and other open features inside a fabricated part by creating sacrificial templates that later form the conduits and voids. It has proven to be a promising method. Still, it relied upon a lot of trial and error, as many automated approaches do initially, in their development to achieve the specific intended geometry.Creating more precise and reproducible geometries necessitates developing a feedback control approach, which would intelligently adjust parameters during printing to achieve the desired intricate geometries.Since LeDuc and Ozdoganlar <a href="https://onlinelibrary.wiley.com/doi/10.1002/advs.202201566" rel="noopener" target="_blank">published their findings</a>, which was featured on the cover of the prestigious Advanced Science journal in July 2022, they have received positive feedback at numerous conference presentations, as well as support that has encouraged them to explore how their ice printing method can be further developed.The initial biomedical engineering applications were clear and showed direct potential for use in personalized medicine. Still, the method also has the potential to revolutionize manufacturing by enabling the automated production of tiny internal 3D microchannels for many applications in various fields ranging from medicine to soft robotics.Micromanufacturing technology and advanced approaches are fundamental to numerous critical applications across various sectors, including scientific research, healthcare, national security, and space exploration, as well as the digital transformation in industry that is at the heart of MFI’s manufacturing mission.According to LeDuc and Ozdoganlar, the experimental and computational methods needed to build models to control the ice printing process are challenging. The microscale size and the fast nature of the thermal-fluidic processes and phase-changes (solid, liquid, gas) processes further exacerbate the challenges. &nbsp;“We know that if we can build a sensor feedback system that can make adjustments in real-time, it will be very powerful for these approaches.” said LeDuc.Wolf knew just the person who could help them develop the sensor hardware and computer vision algorithms needed to create a feedback control approach to produce the degree of control needed for printing the microscale ice structures.Lu Li, a project scientist at Carnegie Mellon’s Robotics Institute, had already brought his artificial intelligence and robotics expertise to several other MFI-funded projects. And according to LeDuc, Li has already made some great contributions to this new project.With Li’s help, the team has reduced the time required for image segmentation from two to three seconds to 50 microseconds. Image segmentation is a computer vision task in machine learning that involves dividing an image into multiple segments or regions based on certain criteria so that the image can be changed into something that is more meaningful and easier to analyze.“This is so typical of how we work at CMU. First of all, the ability to collaborate with someone like Lu Li, who has the skills we needed to move this forward, has been fantastic. And even though the MFI funding is not a huge amount of money, it has allowed us to keep moving forward with an idea that has huge potential,” said LeDuc.Wolf is equally excited to be working with LeDuc, who she says was open to her suggestions, grateful for her recommendation, and brings enthusiasm and energy to our manufacturing community at CMU.

Manufacturing Futures Institute funds a biomanufacturing research project that could revolutionize microchip production for use in medical devices and engineered tissue.

3D micro-ice printing for medical applications

This is the first in a series of articles, from Synopsys that look at&nbsp;<a href="https://www.wevolver.com/article/designing-energy-efficient-ai-accelerators-for-data-centers-and-the-intelligent-edge" target="_blank" rel="noopener noreferrer">AI</a>, automotive, and multi-die chip design.&nbsp;<h2>Introduction</h2>According to Allied Market Research, the global artificial intelligence (AI) chip market is projected to reach $263.6 billion by 2031. The AI chip market is vast and can be segmented in a variety of different ways, including chip type, processing type, technology, application, industry vertical, and more. However, the two main areas where AI chips are being used are at the edge (such as the chips that power your phone and smartwatch) and in data centers (for deep learning inference and training).No matter the application, however, all <a href="https://www.synopsys.com/ai/ai-powered-eda.html" target="_blank">AI chips</a> can be defined as <a href="https://www.synopsys.com/glossary/what-is-ic-design.html" target="_blank">integrated circuits (ICs)</a> that have been engineered to run machine learning workloads and may consist of FPGAs, GPUs, or custom-built ASIC AI accelerators. They work very much like how our human brains operate and process decisions and tasks in our complicated and fast-moving world. The true differentiator between a traditional chip and an AI chip is how much and what type of data it can process and how many calculations it can do at the same time. At the same time, new software AI algorithmic breakthroughs are driving new AI chip architectures to enable efficient deep learning computation.Read on to learn more about the unique demands of AI, the many benefits of an AI chip architecture, and finally the applications and future of the AI chip architecture.<section id="isPasted"><h2>The Distinct Requirements of AI Chips</h2>The AI workload is so strenuous and demanding that the industry couldn’t efficiently and cost-effectively design AI chips before the 2010s due to the compute power it required—orders of magnitude more than traditional workloads. AI requires massive parallelism of multiply-accumulate functions such as dot product functions. Traditional GPUs were able to do parallelism in a similar way for graphics, so they were re-used for AI applications.The optimization we’ve seen in the last decade is drastic. AI requires a chip architecture with the right processors, arrays of memories, robust security, and reliable real-time data connectivity between sensors. Ultimately, the best AI chip architecture is the one that condenses the most compute elements and memory into a single chip. Today, we’re moving into multiple chip systems for AI as well since we are reaching the limits of what we can do on one chip.Chip designers need to take into account parameters called weights and activations as they design for the maximum size of the activation value. Looking ahead, being able to take into account both software and hardware design for AI is extremely important in order to optimize AI chip architecture for greater efficiency.</section><section><h2>The Benefits of AI Chip Architecture</h2>There’s no doubt that we are in the renaissance of AI. Now that we are overcoming the obstacles of designing chips that can handle the AI workload, there are many innovative companies that are experts in the field and designing better AI chips to do things that would have seemed very much out of reach a decade ago.As you move down process nodes, AI chip designs can result in 15 to 20% less clocking speed and 15 to 30% more density, which allows designers to fit more compute elements on a chip. They also increase memory components that allow AI technology to be trained in minutes vs. hours, which translates into substantial savings. This is especially true when companies are renting space from an online data center to design AI chips, but even those using in-house resources can benefit by conducting trial and error much more effectively.We are now at the point where AI itself is being used to design new AI chip architectures and calculate new optimization paths to optimize power, performance, and area (PPA) based on big data from many different industries and applications.</section><section><h2>AI Chip Architecture Applications and the Future Ahead</h2>AI is all around us quite literally. AI processors are being put into almost every type of chip, from the smallest IoT chips to the largest servers, data centers, and graphic accelerators. The industries that require higher performance will of course utilize AI chip architecture more, but as AI chips become cheaper to produce, we will begin to see AI chip architecture in places like IoT to optimize power and other types of optimizations that we may not even know are possible yet.It’s an exciting time for AI chip architecture. Synopsys predicts that we’ll continue to see next-generation process nodes adopted aggressively because of the performance needs. Additionally, there’s already much exploration around different types of memory as well as different types of processor technologies and the software components that go along with each of these.In terms of memory, chip designers are beginning to put memory right next to or even within the actual computing elements of the hardware to make processing time much faster. Additionally, software is driving the hardware, meaning that software AI models such as new neural networks are requiring new AI chip architectures. Proven, real-time interfaces deliver the data connectivity required with high speed and low latency, while security protects the overall systems and their data.Finally, we’ll see photonics and multi-die systems come more into play for new AI chip architectures to overcome some of the AI chip bottlenecks. Photonics provides a much more power-efficient way to do computing and multi-die systems (which involve the heterogeneous integration of dies, often with memory stacked directly on top of compute boards) can also improve performance as the possible connection speed between different processing elements and between processing and memory units increases.One thing is for sure: Innovations in AI chip architecture will continue to abound, and Synopsys will have a front-row seat and a hand in them as we help our customers design next-generation AI chips in an array of industries.Read more about how <a href="https://wevlv.co/synopsys-chip-arch" target="_blank" rel="noopener noreferrer">Synopsys is tackling AI chip architecture challenges</a>.</section>

AI chips, designed for machine learning tasks, are spurring new chip architectures for efficient deep learning computation. 

Why AI Requires a New Chip Architecture

Silicon has emerged as the most widely used semiconductor material in the electronic industry, paving the way for the digital age. However, many are still oblivious to the unique properties and characteristics that make silicon ideal for a range of applications. This article explores the fundamentals of semiconductor materials, the properties of silicon that make it a prominent player in the semiconductor industry, and its diverse applications in electronic devices.

Silicon Semiconductor: A Comprehensive Guide to Silicon and its Use in Semiconductor Technology

	After 20 years of calling me to fix their email and antivirus settings, my parents started calling my 13-year-old daughter for help instead. Technology seems to have been nagging rather than helping grandparents, but the advent of cheap computing gives fundamental reasons to change this balance.


Cheap Computing and the Balancing Act of Population Decline

Verilog, a Hardware Description Language (HDL) used to model electronic systems.

This article aims to provide a comprehensive guide to RTL design, including the fundamentals, operations, and advanced concepts while addressing the challenges and nuances that make this design approach valuable. 

RTL Design: A Comprehensive Guide to Understanding and Implementing Register-Transfer Level Design

Thin film deposition is a process used to create thin film coatings on different materials. Thin films can consist of metal, semiconductors, and dielectrics, providing them with different properties. These properties translate to benefits such as electrical insulation, optical transmission, and corrosion resistance, that can be used to improve substrate performance. This article explores the processes of thin film deposition, along with its types, parameters, benefits, drawbacks, and applications.

Thin Film Deposition: Everything You Need to Know

Smart connected devices are being deployed in all industries, but the one that could have the greatest impact for individuals is within healthcare. In this article we will take a deep dive into the <a href="https://www.renesas.com/eu/en/application/iot-applications/smart-health/smart-connected-pulse-oximeter" rel="noopener noreferrer" target="_blank">smart connected pulse oximeter</a>, which is a super-low power system that can accurately measure blood oxygen levels in patients. We will take a look at the system&rsquo;s components, the unique features of the design, and why this solution is just the beginning in a bright future for smart medical devices.<h2>What is a pulse oximeter?</h2>A pulse oximeter measures the oxygen saturation of the blood in order to determine how well the heart is pumping oxygenated blood to the furthest extremities, such as the arms and legs [1]. Common devices clip a probe onto the patient&rsquo;s ear or fingertip to measure blood oxygen levels. Traditionally, these devices were attached to a monitor directly, or are often seen as part of a blood pressure machine in doctor&rsquo;s offices and hospitals.&nbsp;Healthcare professionals use oximeters quite frequently. They can be used to monitor a patient&rsquo;s current condition if they are suspected of having or diagnosed with a heart attack, heart failure, anemia, lung cancer, asthma, or pneumonia. In addition, patients that have undergone surgery that required sedation or are being subjected to a stress test to assess their cardiovascular abilities will also be monitored using a pulse oximeter&nbsp;[1]. The data captured by the oximeter provides crucial feedback to doctors, nurses, and technicians, which can help treat and monitor patients every day.During the COVID-19 pandemic, doctors began using at-home pulse oximeters to monitor blood oxygen levels of high-risk patients with the virus, such as those with diabetes, those who were pregnant, those with chronic illnesses, and those who were elderly. By taking readings on the portable sensor twice a day, patients were asked to let their doctor know if the reading dropped below 95%. If at any point a patient&rsquo;s blood oxygen level dropped to 90% or lower, they were told to seek immediate attention and to get to the emergency department. Early detection of lower blood saturation limits led to better outcomes in patients who used a pulse oximeter. A study in South Africa determined that a 50% reduction in patient mortality was possible if pulse oximeters were used to get patients the care they required as soon as blood oxygen levels dropped [2]. &nbsp;&nbsp;<h2 dir="ltr">Case Study: Renesas Smart Oximeter design</h2>To bring smart wireless technology to a commonly used medical tool, <a href="https://www.renesas.com/eu/en/application/iot-applications/smart-health/smart-connected-pulse-oximeter" rel="noopener noreferrer" target="_blank">Renesas developed the Smart Connected Pulse Oximeter.&nbsp;</a>Improving on traditional wired probes, the Smart Connected Pulse Oximeter uses a super-low power Bluetooth&reg; Low Energy (LE) System-on-Chip to relay the data to a smartphone app. The company has developed both iOS and Android applications that patients can download for free, and the software itself is open source for the reference design, allowing for future app improvements and collaborations within the industry.&nbsp;For example, healthcare practitioners could work together with app developers to create an interface that would serve their patient population and their own record-keeping needs. Doctors would have the best idea of how tech-savvy their patient population is and how simple the user interface would need to be. Bluetooth&reg; connection, for instance, may need to be automatic, or there may need to be tutorials in place for patients. In addition, doctors would be able to help direct the ideal frequency of checks per day and work with developers to add in notifications for the patient, so they don&rsquo;t forget to check their blood oxygen saturation. Because the device records the levels in the app, it could be further developed to connect to servers that the doctor has access to in order to help monitor the patient.&nbsp;The Smart Oximeter by Renesas is powered by two AAA batteries on the back of the board. Patients connect their smartphone to the oximeter using the Bluetooth&reg; module and can then rest their fingertip on an OB1203 biosensor. The biosensor on the chip is the smallest available at 4.2mm x 2 mm x 1.2 mm. A red light can be seen under the fingertip while measurements are taken, including heart rate, blood oxygen concentration, profusion index, and battery level, which show up in the smartphone application. Measurements take approximately six seconds, and through the use of a medical-grad algorithm have been shown to be accurate on even the most difficult-to-measure patients.Previously, pulse oximeters were not wireless devices that could connect to smartphones. They were small devices that patients used to measure their blood oxygen levels at home. But with advances in&nbsp;Bluetooth&reg; technology, including the size of the chip and the minimal energy it consumes, the pulse oximeter can now be brought online. Currently, this allows for a small device to be used at home, and it allows for greater accuracy in readings helping more patients get to the hospital if their blood oxygen levels get too low. Adding the&nbsp;Bluetooth&reg; capability also opens the door to remote monitoring both by healthcare practitioners and loved ones who are keeping track of their friends&rsquo; and family&rsquo;s health. The data can now be transmitted to the caretaker&rsquo;s phone.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgyNTA0Mzg1MDU2LXB1bHNlIDEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib">Layout of the system. Image credit; Renesas<h2 dir="ltr">Benefits of the Smart Oximeter for the healthcare industry and end consumers</h2>The Smart Oximeter with the available mobile applications is a complete solution available to different healthcare providers. While hospital departments and doctor&rsquo;s offices may have traditional wired oximeters in place, they can be bulky and cumbersome to move from room to room. The Smart Oximeter offers a compact and portable solution for healthcare professionals, and those who are providing their services in patient&rsquo;s homes. They can easily transport the Smart Oximeter on their person and can rest assured it will be using the very little battery energy throughout the day.Because of the lightweight design and mobile monitoring characteristics, the Smart Oximeter could also be used on gym or physiotherapy equipment, like treadmills, ellipticals, and stationary bikes. Patients would be able to monitor their blood oxygen levels, and healthcare providers could receive the data from their patients even between appointments.&nbsp;As a wearable device, patients can rest assured that they can check if their blood oxygen saturation is sufficient or not. This will also ensure patients get to their doctor or local emergency department when blood oxygen levels are too low. The simplicity of the Smart Oximeter by Renesas means it can be used in multiple settings to assist the maximum number of people afflicted with conditions or undergoing treatments and surgeries that may lower their blood oxygen saturation.<h2 dir="ltr">Expanding IoT and smart healthcare solutions to save lives</h2>While engineers have been expanding the Internet of Things (IoT) into all technical realms, the most common examples that seem to be at the forefront are those in manufacturing or smart home systems or even industrial energy applications. But by taking health monitors, sensors, and the latest wireless technology into the IoT domain, many people will benefit from the advances, and not just select high-tech industries.&nbsp;A simple, but powerful, device like the oximeter could be made readily available to patients and their healthcare professionals to more effectively monitor them. For example, if a patient sees their specialist once every two weeks to have their blood oxygen saturation measured, there is a large time lapse within which the patient may decline. Putting the Smart Oximeter into the hands of healthcare frontline professionals and patients could truly save many lives. And with the adoption of smart mobile monitoring, the industry and patients will grow more comfortable of the technology and the elevated level of monitoring and care they will have access to, resulting in many more smart applications in the years to come.&nbsp;<h2 dir="ltr" id="isPasted">Summary</h2>The Renesas Smart Oximeter is a complete solution to monitoring a patient&rsquo;s blood oxygen saturation wirelessly. Powered by two AAA batteries on the back of the chip, the Renesas Smart Oximeter uses a super-low power Bluetooth&reg; System-on-Chip to connect to a smartphone app where the heart rate, blood oxygen concentration, profusion index, and battery levels are displayed. The biosensor on the chip is the smallest available at just 4.2 mm x 2 mm x 1.2 mm, where a patient rests their fingertip.&nbsp;By bringing a smart mobile solution to the healthcare industry, Renesas has made the oximeter more portable, more energy efficient, and more accurate than traditional oximeters. If deployed to healthcare practitioners and their patients more widely, monitoring accuracy and frequency would increase to ensure adequate patient blood oxygen levels, meaning the Renesas Smart Oximeter could literally save lives. &nbsp;&nbsp;<h2 dir="ltr">References:</h2>[1] Johns Hopkins Medicine. Pulse Oximetry [Internet]. Maryland: The Johns Hopkins University; 2022 [cited 2022 May 12]. Available from: <a href="https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/pulse-oximetry" rel="noopener noreferrer" target="_blank">https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/pulse-oximetry</a>[2] Parker-Pope, Tara. Do I Still Need a Pulse Oximeter? A new study shows just how lifesaving home monitoring of oxygen levels can be. [Internet]. New York: The New York Times; 2021 [cited 2022 May 15]. Available from: <a href="https://www.nytimes.com/2021/10/05/well/live/covid-pulse-oximeter.html" rel="noopener noreferrer" target="_blank">https://www.nytimes.com/2021/10/05/well/live/covid-pulse-oximeter.html</a>

A life-saving application of a super-low power system. 


Simply saving lives: A deep look at the simple but brilliant pulse oximeter

We see light and colours around us every day. However, to analyse the information it carries, we must analyse light using spectrometers, in the lab. These devices detect sparkles and substances that our eyes would otherwise not notice.Now, an international team of researchers, including the University of Cambridge, have designed a miniaturised spectrometer that breaks all current resolution records, and does so in a much smaller package, thanks to computational programmes and AI.The new miniaturised devices could be used in a broad range of sectors, from checking the quality of food to analysing starlight or detecting faint clues of life in outer space. The&nbsp;<a href="https://www.science.org/doi/10.1126/science.add8544" target="_blank">results</a> are reported in the journal Science.Traditionally, spectrometers rely on bulky components to filter and disperse light. Modern approaches simplify these components to shrink footprints, but still suffer from limited resolution and bandwidth. Additionally, traditional spectrometers are heavy and take up extraordinary amounts of space, which limits their applications in portable and mobile devices.To tackle these problems, and shrink the size of the system, researchers have coupled layered materials with artificial intelligence algorithms. The result is an all-in-one spectrometer thousands of times smaller than current commercial systems. At the same time, it offers performance comparable to benchtop systems. In other words, these new spectrometers will provide portable alternatives to uncover otherwise invisible information, without even going into the lab.&ldquo;We eliminate the need for detector arrays, dispersive components, and filters. It&rsquo;s an all-in-one, miniaturised device that could revolutionise this field,&rdquo; said Dr Hoon Hahn Yoon, from Aalto University in Finland, first author of the paper. This spectrometer-on-chip technology is expected to offer high performance and new usability across science and industry.The detector uses van der Waals heterostructures &ndash; a &lsquo;sandwich&rsquo; of different ingredients, including graphene, molybdenum disulfide, and tungsten diselenide. Different combinations of material components enable light detection beyond the visible spectrum, as far as the near-infrared region. This means the spectrometer detects more than just colour, enabling applications such as chemical analysis and night vision.&ldquo;We detect a continuum spectrum of light, opening a world of possibilities in a myriad of markets,&rdquo; said Dr Yoon. &ldquo;Exploring other material combinations could uncover further functionalities, including even broader hyperspectral detection and improved resolution.&rdquo;AI is a key aspect of these devices, commonly called &lsquo;computational&rsquo; spectrometers. This technology compensates for the inherent noise increase that inevitably occurs when the optical component is wholly removed.&ldquo;We were able to use mathematical algorithms to successfully reconstruct the signals and spectra, it&rsquo;s a profound and transformative technological leap,&rdquo; said lead author Professor Zhipei Sun, also from Aalto University, and a former member of Cambridge&rsquo;s Department of Engineering. &ldquo;The current design is just a proof-of-concept. More advanced algorithms, as well as different combinations of materials, could soon provide even better miniaturised spectrometers.&rdquo;Spectrometers are used for toxin detection in food and cosmetics, cancer imaging, and in spacecraft &ndash; including the James Webb Space Telescope. And they will soon become more common thanks to the development and advancement of technologies such as the Internet of Things and Industry 4.0.The detection of light &ndash; and the full analysis of spectroscopic information &ndash; has applications in sensing, surveillance, smart agriculture, and more. Among the most promising applications for miniaturised spectrometers are chemical and biochemical analysis, thanks to the capabilities of the devices to detect light in the infrared wavelength range.The new devices could be incorporated into instruments like drones, mobile phones, and lab-on-a-chip platforms, which can carry out several experiments in a single integrated circuit. The latter also opens up opportunities in healthcare. In this field, spectrometers and light-detectors are already key components of imaging and diagnostic systems &ndash; the new miniaturised devices could enable the simultaneous visualisation and detection of &lsquo;chemical fingerprints&rsquo;, leading to possibilities in the biomedical area.&ldquo;Our miniaturised spectrometers offer high spatial and spectral resolution at the micrometre and nanometre scales, which is particularly exciting for responsive bio-implants and innovative imaging techniques,&rdquo; said co-author&nbsp;<a href="http://www.eng.cam.ac.uk/profiles/th270" target="_blank">Professor Tawfique Hasan</a>, from the&nbsp;<a href="https://www.graphene.cam.ac.uk/" target="_blank">Cambridge Graphene Centre</a>.This technology has huge potential for scalability and integration, thanks to its compatibility with well-established industrial processes. It could open up the future for the next generation of smartphone cameras that evolve into hyperspectral cameras that conventional colour cameras cannot do. Researchers hope their contribution is a stepping stone towards the development of more advanced computational spectrometers, with record-breaking accuracy and resolution. This example, they say, is just the first of many.<hr>Reference: Hoon Hahn Yoon et al. &lsquo;<a href="https://www.science.org/doi/10.1126/science.add8544" target="_blank">Miniaturized Spectrometers with a Tunable van der Waals Junction</a>.&rsquo; Science (2022). DOI: 10.1126/science.add8544.This article first appeared on the University of Cambridge <a href="https://www.cam.ac.uk/research/news/artificial-intelligence-powers-record-breaking-all-in-one-miniature-spectrometers" target="_blank">website</a>. Written by Fernando Gomollon-Bel and edited by Sarah Collins.&nbsp;

Using Artificial Intelligence (AI) to replace optical and mechanical components, researchers have designed a tiny spectrometer that breaks all current resolution records.

Artificial intelligence powers record-breaking all-in-one miniature spectrometers

In this article, we look at how Super Low power Wi-Fi and Bluetooth LE enable smart locks to be more agile and reliable by demonstrating these features with a case study from Renesas. 


Super Low Power Wi-Fi and Bluetooth LE Unlock Smart Locks

This article was first published on <a href="https://news.mit.edu/2022/stackable-artificial-intelligence-chip-0613" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don&rsquo;t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device&rsquo;s internal chip &mdash; like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste.&nbsp;Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip.The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip&rsquo;s layers to communicate optically. Other modular chip designs employ conventional wiring to relay signals between layers. Such intricate connections are difficult if not impossible to sever and rewire, making such stackable designs not reconfigurable.The MIT design uses light, rather than physical wires, to transmit information through the chip. The chip can therefore be reconfigured, with layers that can be swapped out or stacked on, for instance to add new sensors or updated processors.&ldquo;You can add as many computing layers and sensors as you want, such as for light, pressure, and even smell,&rdquo; says MIT postdoc Jihoon Kang. &ldquo;We call this a LEGO-like reconfigurable AI chip because it has unlimited expandability depending on the combination of layers.&rdquo;The researchers are eager to apply the design to edge computing devices &mdash; self-sufficient sensors and other electronics that work independently from any central or distributed resources such as supercomputers or cloud-based computing.&ldquo;As we enter the era of the internet of things based on sensor networks, demand for multifunctioning edge-computing devices will expand dramatically,&rdquo; says Jeehwan Kim, associate professor of mechanical engineering at MIT. &ldquo;Our proposed hardware architecture will provide high versatility of edge computing in the future.&rdquo;The team&rsquo;s results are published today in&nbsp;Nature Electronics. In addition to Kim and Kang, MIT authors include co-first authors Chanyeol Choi, Hyunseok Kim, and Min-Kyu Song, and contributing authors Hanwool Yeon, Celesta Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Subeen Pang, Sang-Hoon Bae, Hun S. Kum, and Peng Lin, along with collaborators from Harvard University, Tsinghua University, Zhejiang University, and elsewhere.<h2>Lighting the way</h2>The team&rsquo;s design is currently configured to carry out basic image-recognition tasks. It does so via a layering of image sensors, LEDs, and processors made from artificial synapses &mdash; arrays of memory resistors, or &ldquo;memristors,&rdquo; that the team&nbsp;<a href="https://news.mit.edu/2020/thousands-artificial-brain-synapses-single-chip-0608" target="_blank">previously developed</a>, which together function as a physical neural network, or &ldquo;brain-on-a-chip.&rdquo; Each array can be trained to process and classify signals directly on a chip, without the need for external software or an Internet connection.In their new chip design, the researchers paired image sensors with artificial synapse arrays, each of which they trained to recognize certain letters &mdash; in this case, M, I, and T. While a conventional approach would be to relay a sensor&rsquo;s signals to a processor via physical wires, the team instead fabricated an optical system between each sensor and artificial synapse array to enable communication between the layers, without requiring a physical connection.&nbsp;&ldquo;Other chips are physically wired through metal, which makes them hard to rewire and redesign, so you&rsquo;d need to make a new chip if you wanted to add any new function,&rdquo; says MIT postdoc Hyunseok Kim. &ldquo;We replaced that physical wire connection with an optical communication system, which gives us the freedom to stack and add chips the way we want.&rdquo;The team&rsquo;s optical communication system consists of paired photodetectors and LEDs, each patterned with tiny pixels. Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data to the next layer. As a signal (for instance an image of a letter) reaches the image sensor, the image&rsquo;s light pattern encodes a certain configuration of LED pixels, which in turn stimulates another layer of photodetectors, along with an artificial synapse array, which classifies the signal based on the pattern and strength of the incoming LED light.<h2>Stacking up</h2>The team fabricated a single chip, with a computing core measuring about 4 square millimeters, or about the size of a piece of confetti. The chip is stacked with three image recognition &ldquo;blocks,&rdquo; each comprising an image sensor, optical communication layer, and artificial synapse array for classifying one of three letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and measured the electrical current that each neural network array produced in response. (The larger the current, the larger the chance that the image is indeed the letter that the particular array is trained to recognize.)The team found that the chip correctly classified clear images of each letter, but it was less able to distinguish between blurry images, for instance between I and T. However, the researchers were able to quickly swap out the chip&rsquo;s processing layer for a better &ldquo;denoising&rdquo; processor, and found the chip then accurately identified the images.&ldquo;We showed stackability, replaceability, and the ability to insert a new function into the chip,&rdquo; notes MIT postdoc Min-Kyu Song.The researchers plan to add more sensing and processing capabilities to the chip, and they envision the applications to be boundless.&ldquo;We can add layers to a cellphone&rsquo;s camera so it could recognize more complex images, or makes these into healthcare monitors that can be embedded in wearable electronic skin,&rdquo; offers Choi, who along with Kim previously developed a&nbsp;<a href="https://news.mit.edu/2021/smart-skin-vitals-0630" target="_blank">&ldquo;smart&rdquo; skin</a> for monitoring vital signs.Another idea, he adds, is for modular chips, built into electronics, that consumers can choose to build up with the latest sensor and processor &ldquo;bricks.&rdquo;&ldquo;We can make a general chip platform, and each layer could be sold separately like a video game,&rdquo; Jeehwan Kim says. &ldquo;We could make different types of neural networks, like for image or voice recognition, and let the customer choose what they want, and add to an existing chip like a LEGO.&rdquo;<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2022/stackable-artificial-intelligence-chip-0613" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing elements that can communicate with each other. Credit: Figure courtesy of the researchers and edited by MIT News

The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.

Engineers build LEGO-like artificial intelligence chip

There's never been a better time to dive into custom chip design, with both FPGA and ASIC design routes being more accessible than ever.

As critical components in contemporary computing, choosing between ASICs and FPGAs carries significant implications, underscoring the importance of understanding their unique strengths and trade-offs in the constantly evolving digital landscape.