“… Figure 1 Detector Configuration (i) Pixelated anode pattern with 9 pixels as the regions of interest, (ii) Detector configuration with pixelated anode on top, CZT in the middle and, cathode at the bottom. Figure adapted with permission 35 . …”
Section: Methodsmentioning
confidence: 99%
“…The simulated data for training the proposed learning model has been generated using the classical physical equations 34 , 35 . A MATLAB code was developed for describing the charge transport equations in the detector, by defining the transport, trapping, de-trapping and lifetimes of electrons and holes which are , , , , , , , , , and respectively as the fixed pre-defined parameters, with electric field along the material 35 . As in our previous work 35 , the classical model was created for a discretized (voxelized) RTSD, with charge input at any voxel.…”
Section: Methodsmentioning
confidence: 99%
“…In most of these physics based machine learning approaches, relatively simpler PDEs have been solved. However, the charge transport in a RTSD has multitude of coupled PDEs 34 involving charge drift, trapping, detrapping and recombination, which has been addressed only in one of our previous works 35 using a physics-inspired learning model.…”
Section: Introductionmentioning
confidence: 99%
“…Our approach follows the principle of physics-guided design architecture. Our previous work 35 focused on characterizing radiation detectors using learning-based approaches, where adequate data dictated by physical equations of electron and hole charge transport in the RTSD. These are the signals at the electrodes, free and trapped charges in the bulk of the material, which were used to train and test the learning-based model.…”
Section: Introductionmentioning
confidence: 99%
“…Depending on the amount of data compared to what is desired by physical equations, the detector can be characterized either precisely based on the numerous trapping centers or as a single equivalent trapping center. As in our previous work 35 , the detector is spatially discretized into voxels. The physical charge transport equations are incorporated into the architecture of the model, in each voxels of the model.…”
Room-temperature semiconductor radiation detectors (RTSD) have broad applications in medical imaging, homeland security, astrophysics and others. RTSDs such as CdZnTe, CdTe are often pixelated, and characterization of these detectors at micron level can benefit 3-D event reconstruction at sub-pixel level. Material defects alongwith electron and hole charge transport properties need to be characterized which requires several experimental setups and is labor intensive. The current state-of-art approaches characterize each detector pixel, considering the detector in bulk. In this article, we propose a new microscopic learning-based physical models of RTSD based on limited data compared to what is dictated by the physical equations. Our learning models uses a physical charge transport considering trapping centers. Our models learn these material properties in an indirect manner from the measurable signals at the electrodes and/or free and/or trapped charges distributed in the RTSD for electron–hole charge pair injections in the material. Based on the amount of data used during training our physical model, our algorithm characterizes the detector for charge drifts, trapping, detrapping and recombination coefficients considering multiple trapping centers or as a single equivalent trapping center. The RTSD is segmented into voxels spatially, and in each voxel, the material properties are modeled as learnable parameters. Depending on the amount of data, our models can characterize the RTSD either completely or in an equivalent manner.
“… Figure 1 Detector Configuration (i) Pixelated anode pattern with 9 pixels as the regions of interest, (ii) Detector configuration with pixelated anode on top, CZT in the middle and, cathode at the bottom. Figure adapted with permission 35 . …”
Section: Methodsmentioning
confidence: 99%
“…The simulated data for training the proposed learning model has been generated using the classical physical equations 34 , 35 . A MATLAB code was developed for describing the charge transport equations in the detector, by defining the transport, trapping, de-trapping and lifetimes of electrons and holes which are , , , , , , , , , and respectively as the fixed pre-defined parameters, with electric field along the material 35 . As in our previous work 35 , the classical model was created for a discretized (voxelized) RTSD, with charge input at any voxel.…”
Section: Methodsmentioning
confidence: 99%
“…In most of these physics based machine learning approaches, relatively simpler PDEs have been solved. However, the charge transport in a RTSD has multitude of coupled PDEs 34 involving charge drift, trapping, detrapping and recombination, which has been addressed only in one of our previous works 35 using a physics-inspired learning model.…”
Section: Introductionmentioning
confidence: 99%
“…Our approach follows the principle of physics-guided design architecture. Our previous work 35 focused on characterizing radiation detectors using learning-based approaches, where adequate data dictated by physical equations of electron and hole charge transport in the RTSD. These are the signals at the electrodes, free and trapped charges in the bulk of the material, which were used to train and test the learning-based model.…”
Section: Introductionmentioning
confidence: 99%
“…Depending on the amount of data compared to what is desired by physical equations, the detector can be characterized either precisely based on the numerous trapping centers or as a single equivalent trapping center. As in our previous work 35 , the detector is spatially discretized into voxels. The physical charge transport equations are incorporated into the architecture of the model, in each voxels of the model.…”
Room-temperature semiconductor radiation detectors (RTSD) have broad applications in medical imaging, homeland security, astrophysics and others. RTSDs such as CdZnTe, CdTe are often pixelated, and characterization of these detectors at micron level can benefit 3-D event reconstruction at sub-pixel level. Material defects alongwith electron and hole charge transport properties need to be characterized which requires several experimental setups and is labor intensive. The current state-of-art approaches characterize each detector pixel, considering the detector in bulk. In this article, we propose a new microscopic learning-based physical models of RTSD based on limited data compared to what is dictated by the physical equations. Our learning models uses a physical charge transport considering trapping centers. Our models learn these material properties in an indirect manner from the measurable signals at the electrodes and/or free and/or trapped charges distributed in the RTSD for electron–hole charge pair injections in the material. Based on the amount of data used during training our physical model, our algorithm characterizes the detector for charge drifts, trapping, detrapping and recombination coefficients considering multiple trapping centers or as a single equivalent trapping center. The RTSD is segmented into voxels spatially, and in each voxel, the material properties are modeled as learnable parameters. Depending on the amount of data, our models can characterize the RTSD either completely or in an equivalent manner.
Room temperature semiconductor radiation detectors (RTSD) for X-ray and $$\gamma$$
γ
-ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space. However, 3D characterization of the material defects and charge transport properties in the sub-pixel regime is a labor intensive process with skilled manpower and novel experimental setups. Presently, state-of-art characterization is done over the bulk of the RTSD considering homogenous properties. In this paper, we propose a novel physics based machine learning (PBML) model to characterize the RTSD over a discretized sub-pixelated 3D volume which is assumed. Our novel approach is the first to characterize a full 3D charge transport model of the RTSD. In this work, we first discretize the RTSD between a pixelated electrodes spatially into 3 dimensions—x, y, and z. The resulting discretizations are termed as voxels in 3D space. In each voxel, the different physics based charge transport properties such as drift, trapping, detrapping and recombination of charges are modeled as trainable model weights. The drift of the charges considers second order non-linear motion which is observed in practice with the RTSDs. Based on the electron–hole pair injections as input to the PBML model, and signals at the electrodes, free and trapped charges (electrons and holes) as outputs of the model, the PBML model determines the trainable weights by backpropagating the loss function. The trained weights of the model represents one-to-one relation to that of the actual physical charge transport properties in a voxelized detector.
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