Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

Chaudhuri, Sandeep K.; Kleppinger, Joshua W.; Karadavut, OmerFaruk; Nag, Ritwik; Panta, Rojina; Agostinelli, Forest; Sheth, Amit; Roy, Utpal N.; James, Ralph B.; Mandal, Krishna C.

doi:10.1007/s10854-021-07623-6

Title: Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

Journal Article · 05 January 2022 · Journal of Materials Science. Materials in Electronics

DOI: https://doi.org/10.1007/s10854-021-07623-6 · OSTI ID:1993009

^[1]; Kleppinger, Joshua W. ^[1]; Karadavut, OmerFaruk ^[1];

^[1]; Panta, Rojina ^[1];

^[1];

^[1]; Roy, Utpal N. ^[2];

^[2];

^[1]

University of South Carolina, Columbia, SC (United States)
Savannah River National Laboratory (SRNL), Aiken, SC (United States)

In this article, we report the growth of Cd_0.9Zn_0.1Te_0.97Se_0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd_0.9Zn_0.1Te_0.97Se_0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.

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Research Organization:: Savannah River National Laboratory (SRNL), Aiken, SC (United States); Savannah River Site (SRS), Aiken, SC (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Environmental Management (EM); USDOE Office of Nuclear Energy (NE). Nuclear Energy University Program (NEUP)

Grant/Contract Number:: 89303321CEM000080; NE0008662

OSTI ID:: 1993009

Report Number(s):: SRNL-STI-2021-00650

Journal Information:: Journal of Materials Science. Materials in Electronics, Journal Name: Journal of Materials Science. Materials in Electronics Journal Issue: 3 Vol. 33; ISSN 0957-4522

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
CdZnTeSe (CZTS)
convolutional neural network (CNN)
crystal growth
machine learning (ML)
room-temperature radiation detection
vertical Bridgman method (VBM)

Title: Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

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