John Malboeuf, Vice President of Global Sales, DDM Novastar

AI: The New Teacher for the Factory Floor

The electronics industry, especially in complex areas like microelectronics packaging, is facing a big problem: our most experienced workers are starting to retire. These skilled engineers and technicians hold decades of knowledge about how to fix tricky machines, spot tiny defects, and make manufacturing processes run smoothly. When they leave, all that valuable information often leaves with them. At the same time, the technology we build—like advanced chips for AI—is becoming more complicated than ever. We have complex jobs and fewer experts to do them. This is a serious challenge for the future of manufacturing.

This is where Generative AI (GenAI) steps in to become the new teacher on the factory floor. Think of GenAI, like tools such as ChatGPT, as a powerful student who can read and remember everything. We can feed this AI all of our company's information: old maintenance logs, technical manuals, machine error codes, and even the handwritten notes left by the retiring experts.

The AI can learn from all this "messy" data and turn it into clear, simple instructions. For example, a new technician could simply ask the AI, "Why is the wire bonder giving me an error code 452?" The AI would instantly summarize the best fix based on 20 years of past experience.

By using AI in this way, we can quickly speed up the training process for new employees. Instead of taking five or ten years for a new hire to become a true expert, GenAI can provide them with expert guidance right away, often through tablets or even Augmented Reality (AR) glasses. This instantly makes them more effective at their job, whether they are inspecting for defects or repairing equipment.

AI doesn't replace the human worker; it gives them a superpower, allowing them to solve problems faster and with more confidence. This closes the gap between the new generation and the retiring experts.

For Circuitnet readers—the managers and engineers who run the plants—the message is clear: we must start building a better system to capture our institutional experience/knowledge. If valuable manufacturing data sits locked away in old computer files or on paper, the AI cannot learn from it. The time to organize this information and start feeding it into a structured, AI-ready system is now. By doing this, we secure our company's most important asset—its knowledge—and ensure the next generation is ready to handle the demanding manufacturing of 2026 and beyond.