| title | PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design |
|---|---|
| authors | Runtian Wang, Renhao Xue, Baige Chen, Hao Wu |
| date | 2026-05-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.26502 |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified decoder-only autoregressive transformer that streamlines this process by jointly predicting discrete material selection and continuous thickness regression within a single backbone. PRISM introduces two primary architectural innovations: (1) spectrum prefix conditioning, which utilizes standard prefix tokens for in-context target injection, and (2) cumulative-depth Rotary Position Embeddings, which encode continuous thickness directly into the positional representation to preserve the physical spatial relationships of the stack. Our benchmarks demonstrate that a PRISM-13M model reduces MAE by over 50\% compared to other transformer baselines while utilizing only one-fifth of the parameters. Furthermore, a 44M-parameter variant achieves state-of-the-art performance (MAE = 0.010) on our in-distribution validation benchmark and operates significantly faster than simulated annealing, offering a highly efficient alternative to classical optimization methods. |
cite
@misc{wang2026prism,
title={PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design},
author={Runtian Wang and Renhao Xue and Baige Chen and Hao Wu},
year={2026},
eprint={2605.26502},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.26502}
}