  Foundation models are large machine learning models that can tackle various
downstream tasks once trained on diverse and large-scale data, leading research
trends in natural language processing, computer vision, and reinforcement
learning. However, no foundation model exists for optical multilayer thin film
structure inverse design. Current inverse design algorithms either fail to
explore the global design space or suffer from low computational efficiency. To
bridge this gap, we propose the Opto Generative Pretrained Transformer
(OptoGPT). OptoGPT is a decoder-only transformer that auto-regressively
generates designs based on specific spectrum targets. Trained on a large
dataset of 10 million designs, our model demonstrates remarkable capabilities:
1) autonomous global design exploration by determining the number of layers (up
to 20) while selecting the material (up to 18 distinct types) and thickness at
each 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 design
barriers regarding optical targets, material selections, and design
constraints, OptoGPT can serve as a foundation model for optical multilayer
thin film structure inverse design.

Optics;Machine Learning