OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures
This work aims to improve inverse design methods for optical multilayer thin films, potentially benefiting photonic applications, but appears incremental as it adapts existing transformer architectures to a specific domain.
The authors tackled the problem of inverse design for optical multilayer thin films, which suffers from slow adaptation, limited structure types, and lack of fabrication considerations, by introducing OptoGPT, a decoder-only transformer model that simultaneously addresses these issues.
Optical multilayer thin film structures have been widely used in numerous photonic applications. However, existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets, or are difficult to suit for different types of structures, e.g., designing for different materials at each layer. These methods also cannot accommodate versatile design situations under different angles and polarizations. In addition, how to benefit practical fabrications and manufacturing has not been extensively considered yet. In this work, we introduce OptoGPT (Opto Generative Pretrained Transformer), a decoder-only transformer, to solve all these drawbacks and issues simultaneously.