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Foundation models are large machine learning models that can tackle variousdownstream tasks once trained on diverse and large-scale data, leading researchtrends in natural language processing, computer vision, and reinforcementlearning. However, no foundation model exists for optical multilayer thin filmstructure inverse design. Current inverse design algorithms either fail toexplore the global design space or suffer from low computational efficiency. Tobridge this gap, we propose the Opto Generative Pretrained Transformer(OptoGPT). OptoGPT is a decoder-only transformer that auto-regressivelygenerates designs based on specific spectrum targets. Trained on a largedataset of 10 million designs, our model demonstrates remarkable capabilities:1) autonomous global design exploration by determining the number of layers (upto 20) while selecting the material (up to 18 distinct types) and thickness ateach layer, 2) efficient designs for structural color, absorbers, filters,distributed brag reflectors, and Fabry-Perot resonators within 0.1 seconds(comparable to simulation speeds), 3) the ability to output diverse designs,and 4) seamless integration of user-defined constraints. By overcoming designbarriers regarding optical targets, material selections, and designconstraints, OptoGPT can serve as a foundation model for optical multilayerthin film structure inverse design.

OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures