cGenEDA: Code-Generation for Electronic Design Optimization

Addressing inverse design problems in engineering generally requires managing complex challenges. Traditionally, such systems are optimized by manually setting up a model in a numerical simulation software and applying parameter optimization. Topology changes and variable parameter sets result in challenging mixed-integer optimization problems. Such problems are hard to solve automatically; that’s why they normally need to be initialized manually with high effort by the design engineer. Generative probabilistic models, like Large Language Models (LLMs), can efficiently produce diverse, valid design concepts that are fully parameterizable, simulatable, and optimizable. We introduce cGenEDA, a Co-Pilot —a system based on multiple LLM agents using in-context prompting to coordinate these tasks, providing a seamless, adaptable toolchain for automated design generation, optimization problem definition, and design automation. Our approach demonstrates that long, interacting simulation model code sequences can be generated accurately and directly used as model input for domain-specific simulators for precise forward simulations. Simultaneously, cGenEDA will set up the optimization problem, combine it with well-known, black-box optimization algorithms, and efficiently solve inverse design problems.