Learning-based physical models of room-temperature semiconductor detectors with reduced data. Banerjee, S., Rodrigues, M., Ballester, M., Vija, A. H., & Katsaggelos, A. K. Scientific Reports, 13(1):168, Nature Publishing Group UK London, 2023. doi abstract bibtex 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.
@article{banerjee2023learning,
abstract = {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.},
author = {Banerjee, Srutarshi and Rodrigues, Miesher and Ballester, Manuel and Vija, Alexander Hans and Katsaggelos, Aggelos K.},
doi = {10.1038/s41598-022-27125-7},
issn = {20452322},
journal = {Scientific Reports},
number = {1},
pages = {168},
pmid = {36599876},
publisher = {Nature Publishing Group UK London},
title = {{Learning-based physical models of room-temperature semiconductor detectors with reduced data}},
volume = {13},
year = {2023}
}
Downloads: 0
{"_id":"3rZHG3HYyCpC4jgyg","bibbaseid":"banerjee-rodrigues-ballester-vija-katsaggelos-learningbasedphysicalmodelsofroomtemperaturesemiconductordetectorswithreduceddata-2023","author_short":["Banerjee, S.","Rodrigues, M.","Ballester, M.","Vija, A. H.","Katsaggelos, A. K."],"bibdata":{"bibtype":"article","type":"article","abstract":"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.","author":[{"propositions":[],"lastnames":["Banerjee"],"firstnames":["Srutarshi"],"suffixes":[]},{"propositions":[],"lastnames":["Rodrigues"],"firstnames":["Miesher"],"suffixes":[]},{"propositions":[],"lastnames":["Ballester"],"firstnames":["Manuel"],"suffixes":[]},{"propositions":[],"lastnames":["Vija"],"firstnames":["Alexander","Hans"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"doi":"10.1038/s41598-022-27125-7","issn":"20452322","journal":"Scientific Reports","number":"1","pages":"168","pmid":"36599876","publisher":"Nature Publishing Group UK London","title":"Learning-based physical models of room-temperature semiconductor detectors with reduced data","volume":"13","year":"2023","bibtex":"@article{banerjee2023learning,\nabstract = {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.},\nauthor = {Banerjee, Srutarshi and Rodrigues, Miesher and Ballester, Manuel and Vija, Alexander Hans and Katsaggelos, Aggelos K.},\ndoi = {10.1038/s41598-022-27125-7},\nissn = {20452322},\njournal = {Scientific Reports},\nnumber = {1},\npages = {168},\npmid = {36599876},\npublisher = {Nature Publishing Group UK London},\ntitle = {{Learning-based physical models of room-temperature semiconductor detectors with reduced data}},\nvolume = {13},\nyear = {2023}\n}\n","author_short":["Banerjee, S.","Rodrigues, M.","Ballester, M.","Vija, A. H.","Katsaggelos, A. K."],"key":"banerjee2023learning","id":"banerjee2023learning","bibbaseid":"banerjee-rodrigues-ballester-vija-katsaggelos-learningbasedphysicalmodelsofroomtemperaturesemiconductordetectorswithreduceddata-2023","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["ePKPjG8C6yvpk4mEK","37Qfzv6wRptYkoSCL","zFPgsTDAW8aDnb5iN","E6Bth2QB5BYjBMZE7","nbnEjsN7MJhurAK9x","PNQZj6FjzoxxJk4Yi","7FpDWDGJ4KgpDiGfB","bod9ms4MQJHuJgPpp","QR9t5P2cLdJuzhfzK","D8k2SxfC5dKNRFgro","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":[],"search_terms":["learning","based","physical","models","room","temperature","semiconductor","detectors","reduced","data","banerjee","rodrigues","ballester","vija","katsaggelos"],"title":"Learning-based physical models of room-temperature semiconductor detectors with reduced data","year":2023}