The Climb Of AI In PCB Manufacturing

The technology is being used across many practical applications — shaping how we all live and work.

FREMONT, CA: The past decade has seen artificial intelligence (AI) emerge from what was once an innovative idea to a genuinely applicable and powerful technology. We’ve frequently seen AI as killer robots or armies of autonomous machines turning against their owners on the big screen. But in actuality, the technology is being used across many practical applications — shaping how we all live and work.

For many people, their first experience with AI will be through smart assistants like Apple’s Siri and Amazon’s Alexa, which rely on accurate voice recognition to transmit data from the internet. Yet, AI is advancing rapidly with developments like self-driving cars in the pipeline. And it’s just an occurrence of time before it goes one step further.

Stirring towards Industry 4.0

In PCB (printed circuit board) manufacturing, AI presents tremendous opportunities. Most PCB designers manually route and design their boards, which is time-consuming and intricate.

But AI location in PCB design is both possible. It could pave the road for a new period of innovation — where production processes are streamlined, and outcomes are bettered in ways never accomplished.

AI can support automation systems to communicate with each other and operators in real-time and brings several benefits to manufacturing, including better performance, reduced scrap rates, and the more efficient management of assets, inventory, and supply chains.

Introducing AI into PCB manufacturing is especially critical as the market moves toward Industry 4.0 — or the “Smart Factory” of the future. For example, AI can be embedded in the precision placement tool, which can help determine how each component should be placed, thus improving performance while reducing the time needed for assembly. In addition, PCBs are increasingly getting smaller — in line with shrinking devices — and AI will offer manufacturers higher accuracy when placing components in a compact and densely packed part.

Another field of PCB manufacturing where AI is proving beneficial is inspections. Based on the common location of a defect, AI can quickly and easily narrow down defects to save both time and money.

Data is a key component here. Without high-quality, labeled information, AI cannot be successful. For example, defect classification is a crucial aspect of an automated optical inspection (AOI) solution in PCB manufacturing. Usually, AOI machines send images to a remote multi-image verification station where a human operator classifies them as either “true defects” or “false alarms.” Nevertheless, the human element inevitably opens up the process to error, and mistakes can easily be made in classification.

By contrast, an AI solution could make classification decisions autonomously and with consistent precision once it has learned from the human operators’ decisions. Such an AI system relies on accurate data patterns to learn correct algorithmic behavior over time. Even the tiniest difference in data could lead to game-changing results, so data sensitivity is critical.

What’s next?

The AI in present technologies is seen as “narrow” or “weak” AI. It is designed to play a specific task — such as placing components on a PCB board, internet searches, facial recognition, or driving a car.

Nevertheless, the long-term objective of many developers is to create “general” or “strong” AI. Even though narrow AI might outperform humans at its specific task — inspecting PCB boards, solving equations, or detecting cyber threats — strong AI would overtake humans at nearly every cognitive task.