Machine-learning surrogate model for one-dimensional GaAs/Al$_{0.3}$Ga$_{0.7}$As distributed Bragg reflector spectra

Abstract

We present a Gaussian-process (GP) surrogate model for the normal-incidence reflectance spectrum of one-dimensional GaAs/Al$_{0.3}$Ga$_{0.7}$ distributed Bragg reflectors (DBRs). A Latin-hypercube dataset of 1500 transfer-matrix-method (TMM) simulations is used to train and evaluate the model. Principal component analysis reduces the spectral output to 26 components; one GP is fitted per component. On a held-out test set the GP achieves RMSE=0.085 and $R^2=0.276$, while a Random Forest baseline reaches RMSE=0.065 and $R^2=0.572$. GP inference is 4.4 ms per spectrum compared with ~308 ms for TMM, yielding a ~70x speedup. Uncertainty calibration shows that the GP 95% prediction band covers 98.9% of test residuals. These results establish a rapid surrogate for DBR design-space exploration.