Evaluating LLM-based Workflows for Switched-Mode Power Supply Design
This work addresses the challenge of enhancing productivity in electronic circuit design, specifically for SMPS, by leveraging LLMs, though it is incremental in applying existing AI techniques to a new domain.
The paper tackled the problem of applying large language models (LLMs) to assist in switched-mode power supply (SMPS) design for printed circuit boards, achieving a solve rate increase from 15% to 91% on 269 benchmark tasks through workflows that integrate reasoning, retrieval-augmented generation, and SPICE simulation feedback.
Large language models (LLMs) have great potential to enhance productivity in many disciplines, such as software engineering. However, it is unclear to what extent they can assist in the design process of electronic circuits. This paper focuses on the application of LLMs to switched-mode power supply (SMPS) design for printed circuit boards (PCBs). We present multiple LLM-based workflows that combine reasoning, retrieval-augmented generation (RAG), and a custom toolkit that enables the LLM to interact with SPICE simulations to estimate the impact of circuit modifications. Two benchmark experiments are presented to analyze the performance of LLM-based assistants for different design tasks, including parameter tuning, topology adaption and optimization of SMPS circuits. Experiment results show that SPICE simulation feedback and current LLM advancements, such as reasoning, significantly increase the solve rate on 269 manually created benchmark tasks from 15% to 91%. Furthermore, our analysis reveals that most parameter tuning design tasks can be solved, while limits remain for certain topology adaption tasks. Our experiments offer insights for improving current concepts, for example by adapting text-based circuit representations