Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors

This paper discusses two cases of applying artificial neural networks to the capacitance–voltage characteristics of InAsSb-based barrier infrared detectors. In the first case, we discuss a methodology for training a fully-connected feedforward network to predict the capacitance of the device as a function of the absorber, barrier, and contact doping densities, the barrier thickness, and the applied voltage. We verify the model's performance with physics-based justification of trends observed in single parameter sweeps, partial dependence plots, and two examples of gradient-based sensitivity analysis. The second case focuses on the development of a convolutional neural network that addresses the inverse problem, where a capacitance–voltage profile is used to predict the architectural properties of the device. The advantage of this approach is a more comprehensive characterization of a device by capacitance–voltage profiling than may be possible with other techniques. Finally, both approaches are material and device agnostic, and can be applied to other semiconductor device characteristics.