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.

本文讨论了将人工神经网络应用于基于InAsSb的红外传感器的电容-电压特性的两种情况。在第一种情况下，我们讨论了一种训练完全连接的前馈网络的方法，以预测器件的电容与吸收剂，势垒和接触掺杂密度，势垒厚度和施加电压的关系。我们通过在单参数扫描，部分相关图和两个基于梯度的灵敏度分析的两个示例中观察到的趋势的基于物理学的论证来验证模型的性能。第二种情况着重于解决反问题的卷积神经网络的发展，其中使用电容-电压曲线来预测设备的架构特性。与其他技术相比，这种方法的优点是可以通过电容-电压曲线对设备进行更全面的表征。最后，这两种方法都与材料和器件无关，并且可以应用于其他半导体器件特性。