PCB Chat

Artificial intelligence is everywhere right now. It’s in the headlines, it’s being built into software tools, and it’s showing up in more and more conversations about engineering, manufacturing, quality, and process improvement.

But here’s the problem.

A company can buy the software. A team can attend the training. Everyone can walk away impressed by what AI might be able to do. And then, a few weeks later, very little actually changes. The tool is still there. The potential is still there. But the habits never formed.

And that matters directly to reliability.

In electronics manufacturing, reliability doesn’t come from good intentions. It comes from repeatable processes, disciplined execution, clear documentation, good decision-making, and the ability to recognize problems before they become failures in the field.

That’s where AI can become more than just another interesting tool. Used properly, AI may be able to enhance workflows such as:

• Reviewing ECOs and identifying downstream process, documentation, material, or inspection impacts

• Analyzing AOI, SPI, test, and process data to detect recurring defect patterns

• Supporting root cause analysis by organizing failure data, inspection results, and corrective action history

• Monitoring process drift in areas such as reflow, cleaning, stencil printing, placement, and environmental conditions

• Assisting with corrective action reports, customer responses, and internal documentation

• Capturing tribal knowledge from meetings, shift reports, troubleshooting notes, and past problem-solving activity

Those are all reliability-related activities. They touch the way products are designed, built, inspected, documented, corrected, and improved. 

But AI only helps if it becomes part of the way work actually gets done.

Mike Konrad's guest is Sean Patterson, author of the article, “Why Your AI Training Isn’t Sticking.” 

Patterson argues that AI adoption is not really a training problem. It’s a habit problem. In other words, the goal isn’t simply to teach people what AI can do. 

The goal is to connect AI to the work people are already doing, such as preparing for meetings, reviewing engineering change orders, working through technical questions, or organizing complex problems.

For those of us focused on reliability, that distinction is important. AI doesn’t replace engineering judgment, process knowledge, or verification. 

But when used properly, it may help strengthen the behaviors that support reliability: asking better questions, capturing knowledge, reducing overlooked details, and improving the consistency of technical decision-making.

Here, Sean talks about why AI training often fails to stick, how small triggers can turn occasional use into practical habits, where AI can fit into engineering and manufacturing workflows, and how companies can approach AI in a way that is useful, responsible, and directly connected to reliability.

What if PCB design respins were a thing of the past? What if updating hardware was as easy as updating software?

Itera CEO AJ Cooper co-founded the company to address this exact issue. And now, Itera has developed the world's first "fluid" PCB that can rewire itself in real time. AJ will discuss this groundbreaking technology in his PCB West keynote speech, "It's Time to Retire the Respin." 

In this episode of PCB Chat, AJ discusses his prior work with Lyft and Uber, his efforts to eliminate PCB design respins once and for all, and the ramifications of this "liquid metal" technology on hardware design. He also provides a preview of his PCB West keynote, which takes place at 11 a.m. on Sept. 30 at the Santa Clara Convention Center.

Wally Rhines is the spokesperson for the ESD Alliance, part of Semiconductor Equipment and Materials International. He joins Andy Shaughnessy every quarter to share details from the latest ESD Alliance report on sales of computer-aided engineering tools, semiconductor tools, PCB design software, related IP and services, and employment.

In this interview, Wally discusses the results for Q1 2026, which marks the 21st consecutive quarter of year-on-year growth. He also delves into some of the market drivers in the electronics industry, as well as challenges and opportunities that he's seeing around the globe.

What if passing the test is not enough?

In reliability engineering, we often place a great deal of confidence in laboratory validation. We test, measure, analyze, and document. And when the product passes, we naturally assume it is ready for the field.

But what happens when a product passes the lab test and still fails in the hands of the customer?
Today, we are going to explore the gap between laboratory reliability testing and actual field performance. 

It is a gap that can be costly, frustrating, and sometimes difficult to explain. 

Products can perform well under controlled test conditions, yet still experience unexpected failures when exposed to real users, real environments, real service conditions, and real-world variability.
 
Mike Konrad's guest is Dr. Nishith Kumar Reddy Gorla of CORE ITS LLC. Dr. Gorla’s work focuses on system-level reliability testing, field reliability alignment, reliability growth, and test-to-field correlation for complex products.

His recent paper, “System-Level Test Case Design for Field Reliability Alignment in Complex Products,” looks at why traditional reliability test methods may miss important field failure modes. More importantly, it proposes a broader approach to test case design that considers not just the product design, but also actual use conditions, field failure data, end-user behavior, service procedures, manufacturing variation, consumables, and product interfaces.

This is an important conversation because reliability is not simply about passing a test. It is about understanding how a product behaves in the real world, where variables interact, users behave unpredictably, environments change, and failure mechanisms may not appear until the system is challenged in realistic ways.

If you design, build, test, specify, or depend on complex products, this discussion should be especially relevant. 

Today, we will talk about why products sometimes pass in the lab but fail in the field, how better system-level test cases can improve field correlation, and what reliability professionals can do to create test methods that more accurately reflect real-world performance.

Today, we’re going to explore a topic that seems to be touching nearly every industry, every workplace, and increasingly every part of our daily lives: artificial intelligence.

But this conversation is not just about what AI can do. It’s about what AI assumes, what it values, what it misses, and what happens when powerful technologies are deployed into environments they were not designed to understand.

Mike Konrad's guest is Dr. James Maisiri, an AI researcher and writer whose work focuses on responsible AI, digital transformation, education, labor markets, and the societal impact of emerging technologies. 
His work has been featured through organizations and publications including UNESCO, Guardian, and others, and his message is both simple and profound: AI is not neutral.

In one of his three TEDx talks, Dr. Maisiri said, "If Africa does not shape AI, then AI will shape Africa." 
While his work often focuses on African communities and institutions, the lesson is much broader. 
Every industry, including electronics manufacturing, needs to think carefully about how we adopt AI, what assumptions are built into these systems, and how we validate their use before trusting their output.

In electronics reliability, we often say that context matters. Materials, environments, residues, humidity, temperature, field conditions, and use cases all influence performance.