Identifying Defects without a priori Knowledge in a Room-Temperature Semiconductor Detector Using Physics Inspired Machine Learning Model

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 1; p. 92
• Main Authors: Banerjee, Srutarshi, Rodrigues, Miesher, Ballester, Manuel, Vija, Alexander Hans, Katsaggelos, Aggelos
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.12.2023; MDPI
• Subjects: charge transport; Charged particles; Electric fields; Electrodes; Energy; Machine learning; material characterization; Neural networks; Partial differential equations; Physics; physics inspired machine learning model (PI-ML); Radiation detectors; room temperature semiconductor detector; trapping; room temperature semiconductor detector; detrapping; defects; material characterization; machine learning; physics inspired machine learning model (PI-ML); trapping; charge transport; trapping centers
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24010092

Room-temperature semiconductor radiation detectors (RTSD) such as CdZnTe are popular in Computed Tomography (CT) imaging and other applications. Transport properties and material defects with respect to electron and hole transport often need to be characterized, which is a labor intensive process. However, these defects often vary from one RTSD to another and are not known during characterization of the material. In recent years, physics-inspired machine learning (PI-ML) models have been developed for the RTSDs which have the ability to characterize the defects in a RTSD by discretizing it volumetrically. These learning models capture the heterogeneity of the defects in the RTSD-which arises due to the fabrication process and the energy bands of elements in the RTSD. In those models, the different defects of RTSD-trapping, detrapping and recombination for electrons and holes-are present. However, these defects are often unknown. In this work, we show the capabilities of a PI-ML model which has been developed considering all the material defects to identify certain defects which are present (or absent). Additionally, these models can identify the defects over the volume of the RTSD in a discretized manner.