README.md

metadata

license: mit
pipeline_tag: other

PRISM: Position-encoded Regressive Inverse Spectral Model

PRISM is a unified decoder-only autoregressive transformer designed for inverse thin-film optical design. Given a target optical spectrum, it generates a multilayer thin-film stack (specifying materials and thicknesses) whose physical response matches the target.

• Paper: PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design
• Project Page: PRISM Playground
• Code: GitHub Repository

Architecture Key Innovations

PRISM introduces three primary architectural ideas that distinguish it from prior sequence-to-sequence approaches:

1. Spectrum prefix conditioning: The target spectrum is projected into a single learned token and prepended to the decoder sequence, utilizing causal self-attention for target injection.
2. Cumulative-depth RoPE: Instead of standard sequential token indices, Rotary Position Embeddings (RoPE) use the running cumulative physical depth (in nm) of the film stack. This provides the attention mechanism with a physically meaningful distance metric related to optical path length.
3. Dual output heads: A shared transformer backbone feeds two specialized heads:
   • Material Head: Predicts discrete material selection.
   • Thickness Head: A multi-layer MLP that treats thickness as a continuous regression target (nm), predicting a thickness for every material in the vocabulary at each position to enable joint beam search.

Getting Started

Installation

git clone https://github.com/wang-henry4/prism
cd prism
pip install -e .

Evaluation

You can evaluate a checkpoint using the provided script. It decodes structures, re-simulates them via the Transfer Matrix Method (TMM), and compares them against target spectra.

python evaluate.py \
    --checkpoint saved_models/inverse/inverse_v1/best.pt \
    --val_path ./data/val/part_000.arrow \
    --nk_dir ./nk --n_samples 1000 \
    --beam_width 5 --length_penalty 0.3 \
    --plot_dir ./plots/inverse_eval

Citation

@article{wang2026prism,
  title={PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design},
  author={Wang, Runtian and Xue, Renhao and Chen, Baige and Wu, Hao},
  journal={arXiv preprint arXiv:2605.26502},
  year={2026}
}