https://doi.org/10.1007/s10854-021-07623-6

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Title

Synthesis of CdZnTeSe Single Crystals for Room Temperature Radiation Detector Fabrication: Mitigation of Hole Trapping Effects Using a Convolutional Neural Network

Document Type

Article

Abstract

We report the growth of Cd0.9Zn0.1Te0.97Se0.03 (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. 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 Cd0.9Zn0.1Te0.97Se0.03 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 containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored.

Digital Object Identifier (DOI)

https://doi.org/10.1007/s10854-021-07623-6

APA Citation

Chaudhuri, S. K., Kleppinger, J. W., Karadavut, O., Nag, R., Panta, R., Agostinelli, F., Sheth, A., Roy, U. N., James, R. B., & Mandal, K. C. (2022). Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: Mitigation of hole trapping effects using a convolutional neural network. Journal of Materials Science: Materials in Electronics, 33(3), 1452–1463. https://doi.org/10.1007/s10854-021-07623-6

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