ABSTRACT Large language models (LLMs) have shown increasing potential in analogue circuit analysis automation, yet their ability to understand and identify functional blocks can vary significantly depending on the representation format of the circuit. This paper investigates how circuit representation, flat SPICE Netlists versus structured PySpice code, influences LLM performance in functional block recognition tasks. Using a benchmark of ten analogue comparator circuits derived from a standard educational collection, we evaluate five state‐of‐the‐art LLMs across both representations. Each circuit is annotated with ground‐truth sub‐topologies, and models are prompted to extract these blocks in a standardized JSON format. Our results reveal that DeepSeek R1 achieves the highest average accuracy on Netlist inputs, while GPT 5 provides the most balanced performance across both formats. LLaMA 4 shows a slight advantage on PySpice compared to Netlist, indicating that semantic cues in structured code can benefit certain models. Overall, most models still perform better on Netlist than on PySpice, demonstrating that code‐structured representations do not generically improve performance and highlighting the importance of representation format selection in LLM‐driven electronic design automation.
Dăescu et al. (Thu,) studied this question.