OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures
This work addresses the need for fast and versatile simulation tools in optical engineering, offering a significant improvement over previous methods that were restricted to specific structure types.
The paper tackles the problem of limited applicability of existing deep learning surrogate simulators for optical multilayer thin film structures by proposing the OL-Transformer, a universal simulator that predicts accurate reflection and transmission spectra for up to 10^25 different structures with a six-fold reduction in simulation time compared to physical solvers.
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.