PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design
Summary
PRISM is a decoder-only autoregressive transformer that solves the inverse problem of multilayer thin-film optical coating design by jointly predicting material selection and thickness, achieving state-of-the-art performance with significantly smaller models.
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Paper page - PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design
Source: https://huggingface.co/papers/2605.26502
Abstract
PRISM is a decoder-only autoregressive transformer that efficiently solves the inverse problem of multilayer thin-film optical coatings design by jointly predicting material selection and thickness while leveraging spectrum prefix conditioning and cumulative-depth Rotary Position Embeddings.
Theinverse problemofmultilayer thin-film optical coatingsdesign represents a complexcombinatorial-continuous optimizationchallenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unifieddecoder-only autoregressive transformerthat streamlines this process by jointly predicting discretematerial selectionand continuousthickness regressionwithin 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.
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