Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 29, 2020
- Source ID
- 10.1088/2632-2153/abcf89
Entities
People
- Alexandros Kyrtsos
- Andreu Glasmann
- E. Bellotti
Organizations
- Army Research Office
- United States Army Research Laboratory