  Conventional design and optimization of Photonic Crystal Surface Emitting
Lasers (PCSEL) usually requires expert knowledge in semiconductor physics and
optimization algorithms, which is also known as the inverse design problem.
However, with the trend towards automation and depersonalization of the entire
integrated circuits (IC) industry, the conventional method, with the drawback
of being relatively labor-intensive and sub-optimal, warrants further
refinement. This technical dilemma remained until the emergence of Large
Language Models (LLMs), such as OpenAI's ChatGPT and Google's Bard. This paper
explores the possibility of applying LLMs to machine learning-based design and
optimization of PCSELs. Specifically, we utilize GPT3.5 and GPT4. By simply
having conversations, GPT assisted us with writing Finite Difference Time
Domain (FDTD) simulation code and deep reinforcement learning code to acquire
the optimized PCSEL solution, spanning from the proposition of ideas to the
realization of algorithms. Given that GPT will perform better when given
detailed and specific questions, we break down the PCSEL design problem into a
series of sub-problems and converse with GPT by posing open-ended heuristic
questions rather than definitive commands. This paper shows that LLMs, such as
ChatGPT, can guide the nanophotonic design and optimization processes, on both
the conceptual and technical level, and we propose new human-AI co-design
strategies and show their practical implications. We achieve a significant
milestone for the first step towards an automated end to end nanophotonic
design and production pipeline.
