🤖 AI Summary
This work addresses the inverse design of multilayer optical thin films, where discrete material selection and continuous thickness optimization are tightly coupled. To tackle this challenge, the authors propose PRISM—a unified decoder-only autoregressive Transformer model that jointly predicts material categories and layer thicknesses through a single backbone network. The method innovatively incorporates spectral prefix conditioning to inject target spectral information and encodes cumulative depth into rotary positional embeddings to accurately capture the physical ordering of layers. The PRISM-13M variant achieves over a 50% reduction in mean absolute error (MAE) with only one-fifth the parameters of prior approaches, while PRISM-44M attains state-of-the-art performance on the in-distribution validation set (MAE = 0.010) and demonstrates significantly faster inference than simulated annealing algorithms.
📝 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.