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Impact of Br-etching on surface and current-voltage characteristics of CZT detector

2024· article· en· W4402833530 on OpenAlexafffund
Swati Singh, L. Montpetit, G. Nadal, Mahmoud R. M. Atalla, Eloïse Rahier, S. Koelling, Oussama Moutanabbir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsPolytechnique Montréal
FundersCanada Foundation for InnovationCanada Research Chairs
KeywordsEtching (microfabrication)DetectorMaterials scienceCurrent (fluid)VoltageOptoelectronicsEnvironmental scienceElectrical engineeringNanotechnologyEngineering

Abstract

fetched live from OpenAlex

CdZnTe (CZT) is widely recognized as a highly promising material for detecting X-rays and gamma-rays due to its notable advantages over traditional direct conversion detector materials like silicon and germanium, particularly its high density and resistivity, eliminating the need for cryogenic cooling. However, the performance of radiation detectors is significantly influenced by the surface characteristics of the CZT crystal. In this study, we investigate the impact of bromine methanol ($\mathrm{Br}: \mathrm{MeOH}$) etching on eliminating surface-related defects and enhancing the current-voltage characteristics of the CZT detectors. The preparation of CZT detectors often results in surface damage, leading to surface trap states and non-stoichiometry. Optical and atomic force microscopy measurements reveal prominent polishing lines and increased surface roughness of polished CZT wafers. X-ray photoelectron spectroscopy confirms the presence of tellurium oxide on the CZT surface. CZT devices treated with $\mathrm{Br}: \mathrm{MeOH}$ etching demonstrate improved carrier transport at the metal-semiconductor interface compared to untreated ones.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.276
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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