Modelling Polarization Effects in CdZnTe Sensor at Low Bias
Bibliographic record
Abstract
Semi-insulating CdTe and CdZnTe crystals fabricated into pixelated sensors and integrated into radiation detection modules have demonstrated great ability to operate under the rapidly changing X-ray irradiation environments. Such challenging conditions are required by all photon-counting-based applications including medical CT, airport scanners and non-destructive testing. High-flux sensors typically require operating bias of the order of 1000 V inducing in 2 mm thick detector the electric field 5 kV/cm or locally even higher in polarized detector. This high electric field might cause some long-term reliability concerns so it would be desirable to reduce its value. In addition, the requirement of generating high-voltage causes limitations for portable scanning equipment. It is therefore important to study what minimum electric field is required for proper counting operation.In this presentation, we investigate the possibility to pursue the detector at the high-flux up to 80 Mcps/mm2X-ray irradiation at a low electric field satisfactory for maintaining good counting operation. We numerically simulate the high-flux-induced polarization in commercial 2 mm thick pixelated CdZnTe detector with 330 μm pixel pitch used in spectral Computed Tomography applications and define defect model that is consistent with the electric field profile visualized by Pockels effect. We find the low bias 300 V as acceptable for utilization the detector for photon counting and spectral sensing applications. We calculate collected charge and construct X-ray spectrum considering the processing of the signal by allied electronics. We identify a strong ballistic deficit arising from the extended charge collection time as the main reason for the spectrum distortion. Finally, we suggest possible optimization of the setup to improve the charge collection efficiency and present optimized spectrum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".