Interpixel Passivation of CZT Detectors via ALD-Deposited $\mathbf{Al}_{2} \mathbf{O}_{3}$
Bibliographic record
Abstract
This work presents the fabrication and interpixel passivation of CdZnTe (CZT) detectors using a 10 nm -thin layer of$\text{Al}_{2} \mathrm{O}_{3}$deposited at a substrate temperature of 250° C via atomic layer deposition (ALD). The CZT samples, prepared using chemical-mechanical polishing, exhibit a relatively smoother surface but contain uniformly distributed Te-inclusions,$10-15 \mu ~\mathrm{m}$in size, as confirmed using infrared imaging. Two device configurations were developed: metalsemiconductor (MS) and metal-insulator-semiconductor (MIS), both of which were passivated with the$\text{Al}_{2} \mathrm{O}_{3}$layer. The devices demonstrate uniform dark current across various pixel pairs and show nearly an order of magnitude reduction in surface leakage current in interpixel measurements after passivation, in both MS and MIS structures, compared to unpassivated MS devices. This study demonstrates that effective passivation of CZT detectors is achievable at substrate temperatures above 150° C, while also significantly reducing dark current.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".