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Related Experiment Video

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Identifying Defects without a priori Knowledge in a Room-Temperature Semiconductor Detector Using Physics Inspired

Srutarshi Banerjee1, Miesher Rodrigues2, Manuel Ballester1

  • 1Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary

Physics-inspired machine learning models can now identify unknown defects in room-temperature semiconductor radiation detectors (RTSD). These models characterize defects volumetrically, improving material characterization for applications like Computed Tomography (CT).

Keywords:
charge transportdefectsdetrappingmachine learningmaterial characterizationphysics inspired machine learning model (PI-ML)room temperature semiconductor detectortrappingtrapping centers

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Area of Science:

  • Materials Science
  • Semiconductor Physics
  • Machine Learning

Background:

  • Room-temperature semiconductor radiation detectors (RTSD), like CdZnTe, are vital for Computed Tomography (CT) and other imaging applications.
  • Characterizing material defects affecting electron and hole transport in RTSDs is crucial but labor-intensive and defects vary significantly between devices.
  • Existing defects are often unknown prior to characterization, posing a challenge for accurate material assessment.

Purpose of the Study:

  • To develop and demonstrate a physics-inspired machine learning (PI-ML) model capable of identifying unknown material defects within RTSDs.
  • To characterize RTSD defects volumetrically, capturing spatial heterogeneity arising from fabrication and material properties.
  • To assess the PI-ML model's ability to determine the presence or absence of specific defects throughout the detector volume.

Main Methods:

  • Development of a PI-ML model designed to account for all potential material defects in RTSDs.
  • Volumetric discretization of the RTSD to enable spatially resolved defect analysis.
  • Application of the PI-ML model to identify and locate defects, including trapping, detrapping, and recombination sites for electrons and holes.

Main Results:

  • The PI-ML model successfully identified the presence or absence of specific, previously unknown defects within the RTSD.
  • Defect identification was achieved in a spatially resolved, volumetric manner across the detector.
  • The model demonstrated the capability to capture the heterogeneity of defects inherent in the RTSD material.

Conclusions:

  • PI-ML models offer a powerful, data-driven approach to characterize complex defects in RTSDs.
  • This methodology significantly reduces the labor intensity associated with traditional defect characterization.
  • The ability to identify unknown and spatially varying defects enhances the quality control and performance prediction of RTSDs for critical applications like CT imaging.