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

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