Enhancing quantum cascade laser active region design through inverse neural networks: A machine learning approach to metric-based structure generation

In this study, we introduce an automated design method for Quantum Cascade Laser (QCL) active region (AR) structure employing a generative neural network, termed an inverse network, which creates the structure designs based on specific k · p metric inputs related to device performance. The training dataset, derived from an earlier study, was selectively filtered to remove entries affected by energy-level hybridization or splitting, yielding ∼300 000 valid entries. A pre-trained forward network that processes QCL-AR structures and returns corresponding k · p metrics serves as the evaluator for the inverse network, supplanting traditional loss functions such as mean squared error or mean absolute error. This strategy overcomes the problem of non-uniqueness in the mapping from k · p metrics to QCL-AR structures. The inverse network incorporates a random layer, allowing it to produce a variety of QCL-AR structures from identical predicted metrics, thereby increasing the model’s practicality. Performance testing indicates high accuracy in the metrics of the generated QCL-AR structures, with the coefficient of determination, R2 scores, for key energy-level differences between the upper-laser (ul) level and the lower-laser level, E43, and between the next-higher-energy level above the ul level and the ul level, E54, of 0.9153 and 0.9701, respectively; and for the electron lifetimes τ43 and τ54 of 0.9568 and 0.9175. As an example, we show how the network generates a QCL-AR structure with the potential for low threshold-current density by suppressing shunt-type carrier leakage from the ul level through a higher energy AR state.