---
license: mit
task_categories:
  - tabular-regression
tags:
  - thin-film
  - optics
  - photonics
  - inverse-design
  - transfer-matrix-method
  - spectral-data
pretty_name: PRISM Thin-Film Optical Design Dataset
size_categories:
  - 10M<n<100M
---
# PRISM -- Thin-Film Optical Design Dataset

Synthetic dataset of thin-film multilayer optical stacks and their simulated reflectance/transmittance spectra. Generated for training and evaluating [PRISM](https://github.com/wang-henry4/prism) (Position-encoded Regressive Inverse Spectral Model), an autoregressive transformer for inverse thin-film design.

Each sample is a (materials, thicknesses, spectrum) triple: a thin-film stack definition and its physically simulated optical response.

## Subsets

### Training data

| Subset | Layers | Thickness step | Thickness range | Splits | Total samples |
|---|---|---|---|---|---|
| `max_len_20_10nm` | 1--20 | 10 nm | 10--500 nm | train / dev / val | 10,110,000 |

### Validation-only (out-of-distribution)

These subsets have no training split and are used to evaluate generalisation.

| Subset | Layers | Thickness step | Thickness range | Samples | Purpose |
|---|---|---|---|---|---|
| `max_len_20_5nm` | 1--20 | 5 nm | 5--250 nm | 110,000 | Dev + val for 5 nm (alternate path) |
| `max_len_20_15nm` | 1--20 | 15 nm | 15--750 nm | 20,000 | OOD thickness step |
| `max_len_20_20nm` | 1--20 | 20 nm | 20--1000 nm | 10,000 | OOD thickness step |
| `max_len_30_10nm` | 20--30 | 10 nm | 10--500 nm | 10,000 | OOD sequence length |
| `max_len_40_10nm` | 30--40 | 10 nm | 10--500 nm | 10,000 | OOD sequence length |
| `max_len_50_10nm` | 40--50 | 10 nm | 10--500 nm | 10,000 | OOD sequence length |
| `thick/15nm` | 20 | 15 nm | 15--750 nm | 10,000 | Thick designs only (cum. depth >= 11,000 nm) |
| `thick/20nm` | 20 | 20 nm | 20--1000 nm | 10,000 | Thick designs only (cum. depth >= 11,000 nm) |
| `max_length_10` | 1--10 | 5 nm | 5--250 nm | 30,000 | Short sequence validation |

## Github
https://github.com/wang-henry4/prism/blob/main/master_eval.sh