Hammond Pearce

Sahana Srinivasan, Benjamin Tan, Benjamin Turnbull et al.

Large Language Models (LLMs) have demonstrated capabilities in electronic design automation (EDA) for integrated circuits. However, their applications in printed circuit board (PCB) design and analysis tasks remain underexplored. In part, this is due to a (1) a lack of text-based PCB datasets to evaluate LLMs and (2) a lack of methodologies for prompting LLMs with different types of PCB design files. To address this gap, our paper proposes PCB-QA: a manually created questionnaire dataset amounting to 480 question-answer pairs for PCBs, derived from 8 different open-source hardware projects of varying complexities. We examine multiple aspects of PCB designs and cover questions about component connections, datasheet examination, and simulation data obtainable via SPICE. Using our dataset as a benchmark, we prompt LLMs with different representations (and combinations) of PCB design files and record observations. This allows us to measure, for the first time, if LLMs can understand schematics and netlists in their "native forms" (i.e. graphical PDFs, KiCAD-format design files) or if textual formats are preferred. Including both commercial and open-weight models, we benchmark 4 state-of-the-art LLMs on our dataset, finding that Gemini 3 Flash Preview can answer questions with an accuracy of 93% using a proposed JSON-based textual format. This demonstrates that text-based PCB design formats can be evaluated by LLMs. Our open-source questionnaire is the first step towards enabling LLM integrations within the PCB design life cycle.

Jiamin Chang, Salil Kanhere, Piotr Koniusz et al.

Vision-Language Agentic Systems (VLAS) connect visual perception to planning, tool use, and physical actions. This means backdoor-type triggers can propagate through both decision pipelines and their connected interfaces, thus making visual backdoors a system-level threat. Current evaluations on such backdoors focus on clean accuracy and attack success rate (ASR), metrics that capture whether a trigger works, but not whether an attack is actually "precise" -- i.e. whether it triggers hidden behaviors only when intended. In this work, we formalize the failure of trigger precision as "trigger leakage": inputs that are visually or semantically close to the intended trigger and therefore inadvertently activate the attacker-specified behavior. To quantify this leakage, we introduce Neighbor Leakage Rate (NLR). Our experiments show that at a 3% poisoning ratio, icon and text triggers remain robust to common visual transformations, but their neighboring variants leak heavily, with NLR reaching 0.996 (icon) and 0.944 (text). Using textual triggers as a controlled probe, we show that standard fine-tuning learns a broad activation region rather than an exact trigger condition, causing neighboring strings to invoke the malicious behavior even when the exact trigger is absent. Adding edit-distance-one hard-negative samples during training substantially narrows this activation region and reduces leakage, including in image-editing and embodied-manipulation workflows, where leaked triggers can propagate into executable programs and action sequences.

Sahana Srinivasan, Benjamin Turnbull, Hammond Pearce

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.