A Learning-Based Physical Model of Charge Transport in Room-Temperature Semiconductor Detectors

• Published in: IEEE transactions on nuclear science Vol. 69; no. 1; pp. 2 - 16
• Main Authors: Banerjee, Srutarshi, Rodrigues, Miesher, Vija, Alexander Hans, Katsaggelos, Aggelos K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cadmium; Cadmium tellurides; Cadmium zinc tellurides; Charge carrier processes; Charge transport; Computed tomography; defects; Detectors; detrapping; Electron traps; Electrons; Image reconstruction; Learning; learning-based model; material characterization; Material properties; Mathematical models; Neural networks; Parameters; Physical properties; Physics; Pixels; Radiation; Radiation detectors; Recombination; Recurrent neural networks; Room temperature; room temperature semiconductor detector; Schokley–Ramo theorem; Sensors; Spatial discrimination; Spatial resolution; Training; Transport properties; Trapping; trapping centers; Zinc telluride; Zinc tellurides
• ISSN: 0018-9499; 1558-1578
• DOI: 10.1109/TNS.2021.3130486

Room-temperature semiconductor radiation detectors (RTSDs) such as CdTe are becoming popular in computed tomography (CT) imaging. These detectors are often pixelated, requiring cumbersome postinteraction 3-D event reconstruction, which can benefit from detailed material characterization at the micron level. Transport properties and material defects with respect to electrons and holes are to be characterized, which is a labor-intensive process. Current state-of-the-art characterization is done either as a whole or at most pixel-by-pixel over the detector material. In this article, we propose a novel learning-based physical model to infer material properties at the microscopic level for RTSD. Our approach uses a novel physics-inspired learning model based on physical transport of charges with trapping centers for electrons and holes in the detector. The proposed model learns these material properties from known or measured input charges to the detector along with known or measured output signals and distributed charges in the bulk of the RTSD. The actual physical detector is divided into voxels in space and takes into account different material properties (such as drift, trapping, detrapping, and recombination) in each voxel as learnable model parameters. The model is based on a physics-inspired recurrent neural network model instead of traditional convolutional or fully connected networks. The advantage of our approach is the one-to-one relationship between the actual physical parameters of the voxels and learnable weights in the model, far fewer trainable parameters compared to traditional neural network approaches and less training time. The performance of our model has been evaluated on cadmium zinc telluride (CdZnTe), with voxels of three sizes, 25, 50, and <inline-formula> <tex-math notation="LaTeX">100~ \mu{\mathrm {m}} </tex-math></inline-formula>, for single charge input as well as multiple charge inputs at different voxel positions. Our learning-based model provides material properties with higher spatial resolution and performs well in all scenarios and matches the actual physical parameters better than state-of-the-art classical approaches.