OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures

Taigao Ma, Haozhu Wang, L. Jay Guo

Keywords: AI for Science; Transformer; Physical Surrogate Model; Multilayer Thin Film Structures; Optical Simulations;

TL;DR: We developed a transformer model called OL-Transformer to expedite the optical simulation for universal types of optical multilayer thin film structures.

Abstract: Deep learning-based methods have recently been established as fast and accurate surrogate simulators for optical multilayer thin film structures. However, existing methods only work for limited types of structures with different material arrangements, preventing their applications towards diverse and universal structures. Here, we propose the Opto-Layer (OL) Transformer to act as a universal surrogate simulator for enormous types of structures. Combined with the technique of structure serialization, our model can predict accurate reflection and transmission spectra for up to $10^{25}$ different multilayer structures, while still achieving a six-fold degradation in simulation time compared to physical solvers. Further investigation reveals that the general learning ability comes from the fact that our model first learns the physical embeddings and then uses the self-attention mechanism to capture the hidden relationship of light-matter interaction between each layer.

Submission Number: 9