  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.
