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

arXiv: https://arxiv.org/abs/2305.11984 · HTML: https://mlsanity.com/paper/2305.11984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Transformers for Modeling Physical Systems](https://mlsanity.com/paper/2010.03957/) (2020)
- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures](https://mlsanity.com/paper/2304.10294/) (2023)
- [Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test](https://mlsanity.com/paper/2012.04379/) (2020)
- [Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks](https://mlsanity.com/paper/1809.00972/) (2018)
- [Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction](https://mlsanity.com/paper/2207.02724/) (2022)

## Built on by (most cited first, 1 of 1)

- [Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping](https://mlsanity.com/paper/2507.18644/) (2025)

## Abstract (arXiv)

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 1025 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.