PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913171889979392 |
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| author | Wang, Runtian Xue, Renhao Chen, Baige Wu, Hao |
| author_facet | Wang, Runtian Xue, Renhao Chen, Baige Wu, Hao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_26502 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design Wang, Runtian Xue, Renhao Chen, Baige Wu, Hao Machine Learning Optics 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. |
| title | PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design |
| topic | Machine Learning Optics |
| url | https://arxiv.org/abs/2605.26502 |