LLM4Laser: Large Language Models Automate the Design of Lasers

TL;DR

This paper demonstrates that Large Language Models can effectively guide the design and optimization of Photonic Crystal Surface Emitting Lasers (PCSELs) through conversational AI, enabling a fully automated laser development pipeline.

Contribution

It introduces a novel human-AI co-design paradigm where LLMs assist in conceptualizing and technically implementing laser design processes, including code generation for simulations and algorithms.

Findings

LLMs can generate simulation and optimization code for laser design.

Conversational prompts improve LLM performance in complex technical tasks.

The approach advances towards fully automated AI-driven laser development pipelines.

Abstract

With the rapid evolution of global autonomous driving technology, the demand for its core sensing hardware, Light Detection and Ranging (LiDAR), is escalating. As the light source part of the LiDAR system, lasers, particularly the cutting-edge Photonic Crystal Surface Emitting Lasers (PCSEL), have correspondingly attracted extensive research attention. The conventional manual design and optimization of PCSEL typically require expertise in semiconductor physics and months of dedicated effort to achieve satisfactory results. While AI-driven approaches can expedite this process, laser designers still need to invest time in learning the AI algorithms involved. Meanwhile Large Language Models (LLMs), leveraging their powerful reasoning abilities, can effectively comprehend natural language and provide constructive feedback in multi-turn dialogues. They have already demonstrated potential to assist humans in scientific fields such as robotics design and chemical discovery. A question naturally arises is: Can LLMs transform the lasers design process? This paper proposes a novel human-AI co-design paradigm to show that LLMs can guide the laser design and optimization process both conceptually and technically. Specifically, by simply having conversations, GPT assisted us with writing both Finite Difference Time Domain (FDTD) simulation code and deep reinforcement learning (RL) 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 prompts, 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. We achieved a significant milestone towards self-driving laboratories, that is, a fully automated AI-driven pipeline, for laser design and production.