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110 I-CONNECT007 MAGAZINE I FEBRUARY 2026 F E AT U R E A RT I C L E BY S T E V E WAT T, Z U K E N Making AI Practical for PCB Design Artificial intelligence has entered nearly every corner of engineering software. In PCB design, however, meaningful adoption has been slower and for good reason. Unlike image genera- tion or text analysis, PCB layout is not a data-rich, rules-light problem. It is a precision-driven engi- neering discipline in which creativity, accuracy, and strict compliance with constraints must coexist. Zuken's work on AI-assisted PCB design reflects this reality. Rather than positioning AI as a replace- ment for engineering expertise, our CR-8000 Autonomous Intelligent Place and Route (AIPR) applies machine learning selectively in ways that align with how designers actually think and work. Why PCB Design Is a Difficult AI Problem In a recent webinar on AI-based PCB place-and- route, Dr. Kyle Miller, R&D manager at Zuken, outlined why many general-purpose AI techniques fall short when applied to PCB design. Modern AI systems often excel at producing outputs that look plausible, but they struggle with precision and repeatability. In PCB design, plausibility is not enough. A route that looks reasonable but violates electrical, physical, or manufacturability constraints is unusable. PCB data is also highly heterogeneous. A single design combines discrete layers, continu- ous geometry, electrical connectivity, Boolean rule sets, and hierarchical structure. Unlike games or image datasets, there is no fixed state space or abundance of interchangeable training examples. Each PCB is unique, and knowledge must be trans- ferred across designs that are similar in intent, not identical in structure. These constraints shaped our AI strategy from the start. The goal was not generative novelty, but engineering fidelity. "In PCB design, we need creativity, accuracy, and precision to work together. That combination is what makes this a fundamen- tally different AI challenge," Miller said. Figure 1: Example of an AIPR routing result. Highlighted areas show a more human-like routing style compared with traditional autorouters.