The Human Factor, Revisited: Engineering from First Principles

High-voltage electronics now sit at the center of future infrastructures. Electrified transportation, industrial automation, medical systems, and energy management all rely on silicon operating under conditions where margins for error continue to shrink. Voltages are rising, lifetimes are extending, and failure is no longer an inconvenience but a risk for the system user.

At the same time, engineering capabilities have never been more advanced. Simulation predictability has improved. Manufacturing processes are more controlled. Tools and production lines are more sophisticated and more monitored than at any point in the industry’s history.

Yet many of the most consequential failures in high-voltage systems today remain difficult to predict, difficult to diagnose, and expensive to correct once they appear. Not because the physics are unknown, but because complexity has moved upstream. It now lies in the interaction between components, processes, and long-term system behavior.

This shift has quietly impacted what engineering excellence means.

From Yield to Operational Lifetime Thinking

More than two decades ago, long before electrification became a dominant design constraint, Alain R. Comeau (Founder, CEO/CTO at SimpleChips Technology, Inc.) wrote about mixed-signal yield improvement and the role of the human factor in resolving complex engineering problems. At the time, one of the main focuses of the electronic industry was on manufacturing yield, performance and cost, which were always present. The challenge was identifying subtle interactions between process steps, device physics, and design assumptions that only revealed themselves late in production or late in operation.

Despite different tools, voltage levels, and system complexity, the mindset remains strikingly relevant. Today’s challenges have moved beyond yield in the narrow sense. Modern high-voltage ICs must operate reliably across years or decades. They must tolerate environmental stress, electrical noise, thermal cycling, aging mechanisms, and real-world usage patterns that no datasheet can fully capture. Many of the challenges that once appeared as yield issues now re-emerge as lifetime and reliability concerns, governed by the same underlying physics. In other words, the question is not only whether a device works when it leaves the factory but also whether it continues to behave predictably across its entire operational life.

This transition from yield thinking to long-term operational thinking has profound implications. It requires engineers to reason across time, not just across corners. It demands an understanding of how small design decisions impact systems long after deployment. It places a premium on judgment, not only optimization.

How Systems Became a Unit of Design

High-voltage ICs do not exist in isolation (pun intended!). They shape and are shaped by the systems they enable. Parasitics interact with layout choices. Packaging affects thermal behavior. Electric field stress accumulates slowly, often invisibly. Minor variations at the silicon level can translate into significant differences at the system level years later.

As systems have grown more integrated, the boundary between component behavior and system behavior has blurred. A failure observed at the system level may originate in process variation, device geometry, layout assumptions, or operational context, not necessarily in a single place, sometimes across several layers at once.

This is why high-voltage design can become a systems problem in the truest sense. The system reliability can equally become a real concern upon performance short of expectation or specification. The component-level optimization alone cannot capture the interactions that define long-term reliability.

This shift places new demands on engineering teams. It requires fluency across domains. It rewards engineers who can think in chains of causality rather than isolated parameters. It elevates first-principles reasoning over pattern matching.

Root-Cause Engineering as a Design Discipline

In complex systems, failures rarely announce their origin clearly. What appears as drift, noise, or instability at the output is often the final expression of a sequence of contributing factors: subtle process characteristics, marginal operating assumptions, and environmental stress that compounds over time.

M. Comeau first described this decades ago as a problem chain, a sequence of conditions that must all align for a failure to occur. Breaking any link in that chain prevents the failure entirely. What has changed is where this thinking is applied.

As M. Comeau puts it, “good information leads to good decisions.” As a result of good information, better solutions have been created. Problems exist as a result of a chain of events (the problem chain) such as “ProblemA = EventB and EventC.” If EventB is eliminated, then ProblemA can never happen. This rationale was fundamental in understanding the root causes behind some of the most complex problems SimpleChips has resolved.

Root-cause analysis is often treated as a corrective activity, something that happens after an issue appears in the field. In high-voltage systems, this is too late. The cost of failure grows substantially once systems are deployed.

Modern root-cause engineering must begin at the time of design. It must shape architecture decisions, not merely validate them. Engineers must ask how and where assumptions could fail under real operating conditions, not only whether they hold under nominal ones. It may be relevant to consider that high-voltages can kill you, for example.

This requires a mindset that values understanding over speed. It favors deliberate design choices over maximal feature density. Essentially, it encourages teams to explore limit cases early, even when no immediate issues are foreseen.

“The essence of this kind of problem resolution consists in identifying the nature of the problem itself, understanding how it happens, and resolving it. These three steps are really under people’s control.”

— Alain R. Comeau

The Human Factor, Reimagined

As engineering challenges have grown more complex, the limiting factor has shifted from access to tools or data to the ability of teams to interpret, connect, and act on what those tools reveal.

The human factor in modern engineering centers on culture. This involves how teams communicate across disciplines, whether organizations reward deep reasoning or superficial optimization, and whether uncertainty is explored or ignored. High-voltage IC design exposes these dynamics quickly. There is little room for fragmented ownership or siloed decision-making. Reliability emerges from shared understanding rather than isolated excellence.

This is where experienced engineering leadership proves essential (a.k.a. The Human Factor), especially in shaping the framing of problems. Encouraging these engineers to transcend immediate specifications, foster environments where assumptions are challenged early and knowledge is shared freely, provides a positive environment helpful in minimizing failure risks.

The influence of M. Comeau at SimpleChips reflects this philosophy. His work has consistently emphasized the importance of understanding physical mechanisms, manufacturing realities, and system-level consequences as a unified whole. The result is clearer engineering thinking and, thus, better products.

SimpleChips as an Expression of First-Principles Thinking

SimpleChips operates at the intersection of these ideas. Its approach to IC design reflects a deliberate choice to:

1. Prioritize system predictability over maximal integration

2. Focus on well-defined building blocks that reduce uncertainty at the system level

3. Design with long-term behavior in mind rather than purely initial performance

This philosophy is visible in how SimpleChips frames the problems and positions its products. The goal is not to overwhelm designers with complexity, but rather to simplify critical functions in a way that supports robust system designs. Each product reflects an understanding of where failures can emerge and how thoughtful architecture can prevent them.

In other words, these products are outcomes rather than starting points. They demonstrate how first-principles engineering and human-centered problem solving translate into tangible design advantages.

“Artificial Intelligence or Artificial Opinion? The answer is in the question.”

— Alain R. Comeau

Why This Mindset Matters Now

Today, the pace of electrification is accelerating at unprecedented rates. Voltage domains are expanding, and systems are becoming more autonomous and interconnected. At the same time, expectations around safety, reliability, and lifetime performance continue to rise.

In this environment, engineering success depends less on incremental optimization and more on clarity of thought. Teams that understand how failures develop will outperform those that react to them. That is why organizations that invest in engineering culture will build more resilient systems than those that focus solely or primarily on tooling.

The next phase of IC design will be shaped by those who can reason across physics of silicon, and systems without losing sight of human judgement. It is for those who recognize that reliability is a discipline rather than a feature. Even with A.O. as a tool, it is ultimately the human who is making the decision.

This is the paradigm’s answer that M. Comeau and SimpleChips continue to advance, one rooted in first principles that treats engineering judgment as a strategic asset. It is leadership that truly believes that, even in a world of increasingly capable tools, it is still people who make the difference.