A Learning-Based Physical Model of Charge Transport in Room-Temperature Semiconductor Detectors. Banerjee, S., Rodrigues, M., Vija, A. H., & Katsaggelos, A. K. IEEE Transactions on Nuclear Science, 69(1):2–16, IEEE, jan, 2022.
A Learning-Based Physical Model of Charge Transport in Room-Temperature Semiconductor Detectors [link]Paper  doi  abstract   bibtex   
Roomerature 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 100$μ$ m, 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.
@article{banerjee2021learning,
abstract = {Roomerature 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 100$\mu$ m, 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.},
author = {Banerjee, Srutarshi and Rodrigues, Miesher and Vija, Alexander Hans and Katsaggelos, Aggelos K.},
doi = {10.1109/TNS.2021.3130486},
issn = {0018-9499},
journal = {IEEE Transactions on Nuclear Science},
keywords = {Charge transport,Defects,Detrapping,Learning-based model,Material characterization,Room temperature semiconductor detector,Schokley-Ramo theorem,Trapping,Trapping centers},
month = {jan},
number = {1},
pages = {2--16},
publisher = {IEEE},
title = {{A Learning-Based Physical Model of Charge Transport in Room-Temperature Semiconductor Detectors}},
url = {https://ieeexplore.ieee.org/document/9625943/},
volume = {69},
year = {2022}
}
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