Optimization of a quantum cascade laser cavity for single-spatial-mode operation via machine learning

Abstract

Neural networks, trained with the ADAM algorithm followed by a globally convergent modification to Newton’s method, are developed to predict the threshold gain of the fundamental and first higher-order modes as functions of the refractive-index profile in a quantum cascade laser cavity. The networks are used to optimize the design of a refractive-index profile that provides essentially single-spatial-mode performance in a nominally multi-moded cavity by maximizing the threshold-gain differential between the modes. The use of neural networks allows the optimization to be performed in seconds, instead of days or weeks which would be required if Maxwell’s equations were repeatedly solved to obtain the threshold gains.