Machine Learning Surrogate Model for Accelerating InGaAs/GaAs Quantum Well Laser Design
Abstract
A machine-learning surrogate model based on a multi-layer perceptron neural network is used to predict six laser performance metrics for an indium gallium arsenide quantum well laser given epitaxial layer parameters as inputs. The surrogate model was trained on simulation data from a set of 4999 designs which were generated by varying epitaxial layer parameters and simulating each design with Harold to extract performance metrics. The model predicts all 6 chosen metrics with a coefficient of determination, R2, greater than 0.91 on a held-out test set and demonstrates a ∼ 106 × speed up compared to full device simulations. The model is used to search across 1 million potential epitaxial designs in the parameter space and find designs that minimize threshold current and internal loss. Using the surrogate, rather than full device simulation, reduces computation time for a million designs from 231 days to ∼2 minutes on a standard laptop. It is also demonstrated that use of the Swish activation function, rather than ReLU, together with engineered input features - i.e. derived quantities calculated from the base model inputs - raise the mean R2 from 0.88 to 0.94 without additional training data, and reduce the mean absolute percentage error from 1.78 ± 0.08 % to 1.17 ± 0.09 %.
Related
- Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
- Machine learning surrogate modeling with quantile adjustment for landing gear surface pressure fluctuation prediction
- Parnassus for the CLD Detector: A Generative Machine-Learning Surrogate for Detector Simulation and Reconstruction at the FCC-ee
- Gravitational-wave parameter estimation with machine-learning generated surrogate waveforms
- Architecture Acceleration of Machine Learning Gravitational Waveform Surrogate Models
BibTeX
@misc{verrinder2026machine,
title = {{Machine Learning Surrogate Model for Accelerating InGaAs/GaAs Quantum Well Laser Design}},
author = {Paul Verrinder and Jack Markham},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21430},
eprint = {2610.21430},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21430},
}
RIS
TY - UNPB
TI - Machine Learning Surrogate Model for Accelerating InGaAs/GaAs Quantum Well Laser Design
AU - Paul Verrinder
AU - Jack Markham
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21430
UR - https://arxiv.org/abs/2610.21430
AB - A machine-learning surrogate model based on a multi-layer perceptron neural network is used to predict six laser performance metrics for an indium gallium arsenide quantum well laser given epitaxial layer parameters as inputs. The surrogate model was trained on simulation data from a set of 4999 designs which were generated by varying epitaxial layer parameters and simulating each design with Harold to extract performance metrics. The model predicts all 6 chosen metrics with a coefficient of determination, R^2, greater than 0.91 on a held-out test set and demonstrates a ∼ 10^6 × speed up compared to full device simulations. The model is used to search across 1 million potential epitaxial designs in the parameter space and find designs that minimize threshold current and internal loss. Using the surrogate, rather than full device simulation, reduces computation time for a million designs from 231 days to ∼2 minutes on a standard laptop. It is also demonstrated that use of the Swish activation function, rather than ReLU, together with engineered input features - i.e. derived quantities calculated from the base model inputs - raise the mean R^2 from 0.88 to 0.94 without additional training data, and reduce the mean absolute percentage error from 1.78 ± 0.08 % to 1.17 ± 0.09 %.
ER -