Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors

TL;DR

This paper introduces a learning-based method to characterize and detect defects in CdZnTe radiation detectors, aiming to improve their spectral resolution by compensating for crystal impurities.

Contribution

A novel learning-based approach for spatially mapping bulk properties and defects in CdZnTe detectors, enhancing defect detection and crystal characterization.

Findings

Achieved an average RMSE of 0.43% on noise-free data

Model shows high accuracy in predicting crystal properties

Sensitivity analysis demonstrates robustness to noisy data

Abstract

CdZnTe-based detectors are highly valued because of their high spectral resolution, which is an essential feature for nuclear medical imaging. However, this resolution is compromised when there are substantial defects in the CdZnTe crystals. In this study, we present a learning-based approach to determine the spatially dependent bulk properties and defects in semiconductor detectors. This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. In addition, a sensitivity analysis was performed to determine the effect of noisy data on the accuracy of the model.