A machine learning framework for quantum cascade laser design

Abstract

A multi-layer perceptron neural network was used to predict the laser transition figure of merit, a measure of the laser threshold gain, of over 900 × 106 Quantum Cascade (QC) laser designs using only layer thicknesses and the applied electric field as inputs. Designs were generated by randomly altering the layer thicknesses of an initial 10-layer design. Validating the predictions with our 1D Schrödinger solver, the predicted values show 5%–15% error for the laser structures, well within QC laser design variations. The algorithm (i) allowed for the identification of high figure of merit structures, (ii) recognized which layers should be altered to maximize the figure of merit at a given electric field, and (iii) increased the original design figure of merit of 94.7–141.2 eV ps Å2, a 1.5-fold improvement and significant for QC lasers. The computational time for laser design data collection is greatly reduced from 32 h for 27 000 designs using our 1D Schrödinger solver on a virtual machine, to 8 h for 907 × 106 designs using the machine learning algorithm on a laptop computer.