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Surveying GenAI-Based Automation in Printed Circuit Board Design and Test

A survey submitted to arXiv on 10 June 2026 examines how generative artificial intelligence is being applied across the printed circuit board (PCB) design life cycle, from supply chains and system specification through circuit design, layout and optimisation, validation and test, and PCB assembly and distribution. The survey presents a taxonomy of discovered works categorised by intent and contribution, and identifies key technical challenges including domain-specific data scarcity and limited support for integration with existing PCB tools. The authors conclude that many opportunities remain for integrating GenAI into PCB design and test tasks, noting that prior hardware-focused GenAI work has concentrated on integrated circuit design automation and hardware description languages.

read2 min views1 publishedSep 17, 2026
Surveying GenAI-Based Automation in Printed Circuit Board Design and Test
Image: source
  [Submitted on 10 Jun 2026]


[View PDF](https://arxiv.org/pdf/2606.17074)

[HTML (experimental)](https://arxiv.org/html/2606.17074v1)

Abstract:Generative artificial intelligence (GenAI) is increasingly used for applications in the hardware and software domains. It purports to reduce the manual effort involved in the development and testing of complex systems before release. Within the hardware space, most tasks have focused on design automation of integrated circuits, particularly with hardware description languages. However, other types of hardware also exist! In this survey, we instead examine how GenAI has been and is being across the printed circuit board (PCB) design life cycle. This includes everything from supply chains, system specification, circuit design, layout and optimisation, validation and test, and PCB assembly and distribution. Through this lens we present a taxonomy of discovered works, categorising them according to their intent and contributions. This survey also identifies key technical challenges that GenAI faces in this space, such as domain-specific data scarcity and limited support for integration with existing PCB tools. Finally, future research directions are discussed: our survey shows that there are many opportunities remaining when considering how GenAI may be integrated into various tasks in PCB design and test.

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