Large language models (LLMs) are revolutionizing photonic circuit design, a complex field requiring expertise in various scientific disciplines. Sharma, Fu, and Ansari et al. have developed an innovative tool that transforms plain-text instructions into functional circuit schematics, offering a breakthrough in automating design tasks. This multi-agent framework operates in four steps: converting natural language into initial schematics, enhancing designs based on components, creating valid layouts, and simulating circuit performance. The researchers evaluated their tool using advanced models from Google, OpenAI, and other leading organizations, with Gemini 2.5 achieving the best results. While effective for simple instructions, challenges remain with complex prompts, particularly in circuit hierarchy interpretation. The ongoing effort aims to improve initial entity extraction, making photonics design more accessible and efficient. This pioneering work sets the stage for future advancements in photonic circuit automation, highlighting the transformative potential of AI in technical design processes.
For more details, refer to the article: https://doi.org/10.1063/5.0300741.
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