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

Srutarshi Banerjee; Miesher Rodrigues; Manuel Ballester; Alexander Hans Vija; Aggelos Katsaggelos

doi:10.3390/s24010092

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

Sensors (Basel). 2023 Dec 23;24(1):92. doi: 10.3390/s24010092.

Authors

Srutarshi Banerjee¹, Miesher Rodrigues², Manuel Ballester¹, Alexander Hans Vija², Aggelos Katsaggelos¹

Affiliations

¹ Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA.
² Siemens Medical Solutions USA, Inc., Hoffmann Estates, IL 60192, USA.

Abstract

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 a priori 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.

Keywords: charge transport; defects; detrapping; machine learning; material characterization; physics inspired machine learning model (PI-ML); room temperature semiconductor detector; trapping; trapping centers.

Grants and funding

The authors acknowledge Siemens Medical Solutions Inc. for providing financial support to Northwestern University for conducting this research within the scope of a research agreement.