The growing need for the multiband photodetection of a single scene has promoted the development of both multispectral coupling and broadband detection technologies. Photodetectors operating across the infrared (IR) to terahertz (THz) regions have many applications such as in optical communications, sensing imaging, material identification, and biomedical detection. In this review, we present a comprehensive overview of the latest advances in broadband photodetectors operating in the infrared to terahertz range, highlighting their classification, operating principles, and performance characteristics. We discuss the challenges faced in achieving broadband detection and summarize various strategies employed to extend the spectral response of photodetectors. Lastly, we conclude by outlining future research directions in the field of broadband photodetection, including the utilization of novel materials, artificial microstructure, and integration schemes to overcome current limitations. These innovative methodologies have the potential to achieve high-performance, ultra-broadband photodetectors.

In this paper, a novel approach that combines technology computer-aided design (TCAD) simulation and machine learning (ML) techniques is demonstrated to assist the analysis of the performance degradation of GaN HEMTs under hot-electron stress. TCAD is used to simulate the statistical effect of hot-electron-induced, electrically active defects on device performance, while the artificial neural network (ANN) algorithm is tested for reproducing the simulation results. The results show that the ML-TCAD approach can not only rapidly obtain the performance degradation of GaN HEMTs, but can accurately predict the progressive failure under the work conditions with a mean squared error (MSE) of 0.2, informing the possibility of quantitative failure data analysis and rapid defect extraction via the ML-TCAD approach.