PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design

• URL: http://arxiv.org/abs/2605.26502v2
• Date: Fri, 29 May 2026 08:00:08 GMT
• Title: PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design
• Abstract summary: We present PRISM, a unified decoder-only autoregressive transformer.<n> PRISM predicts discrete material selection and continuous thickness regression within a single backbone.<n>Our benchmarks demonstrate that a PRISM-13M model reduces MAE by over 50% compared to other transformer baselines.
• Score: 2.432257332677549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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