Photonic Crystal Surface Emitting Lasers (PCSEL)'s inverse design demands expert knowledge in physics, materials science, and quantum mechanics which is prohibitively labor-intensive. Advanced AI technologies, especially reinforcement learning (RL), have emerged as a powerful tool to augment and accelerate this inverse design process. By modeling the inverse design of PCSEL as a sequential decision-making problem, RL approaches can construct a satisfactory PCSEL structure from scratch. However, the data inefficiency resulting from online interactions with precise and expensive simulation environments impedes the broader applicability of RL approaches. Recently, sequential models, especially the Transformer architecture, have exhibited compelling performance in sequential decision-making problems due to their simplicity and scalability to large language models. In this paper, we introduce a novel framework named PCSEL Inverse Design Transformer (PiT) that abstracts the inverse design of PCSEL as a sequence modeling problem. The central part of our PiT is a Transformer-based structure that leverages the past trajectories and current states to predict the current actions. Compared with the traditional RL approaches, PiT can output the optimal actions and achieve target PCSEL designs by leveraging offline data and conditioning on the desired return. Results demonstrate that PiT achieves superior performance and data efficiency compared to baselines.

翻译：光子晶体表面发射激光器（PCSEL）的逆向设计需要物理学、材料科学和量子力学领域的专家知识，这使得其工作极度耗费人力。以强化学习（RL）为代表的先进人工智能技术，已成为增强和加速这一逆向设计过程的强大工具。通过将PCSEL的逆向设计建模为序列决策问题，RL方法可以从零开始构建出满足要求的PCSEL结构。然而，在线交互过程中需要依赖精确且昂贵的仿真环境所导致的数据低效性，限制了RL方法的广泛应用。近年来，序列模型（尤其是Transformer架构）因其简洁性和可扩展至大型语言模型的特性，在序列决策问题中展现出令人瞩目的性能。本文提出了一种名为PCSEL逆向设计Transformer（PiT）的新框架，将PCSEL的逆向设计抽象为序列建模问题。PiT的核心是一个基于Transformer的结构，利用历史轨迹和当前状态预测当前动作。与传统RL方法相比，PiT能够通过利用离线数据并依据目标回报进行条件设定，输出最优动作并实现目标PCSEL结构。实验结果表明，PiT在性能和数据效率方面均优于基线方法。