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Direct observation of an atomic thin inversion layer at the native oxide/ n-Si interface

2022· article· en· W4312897494 on OpenAlexaff
Yibo Zhang, Joel Y. Y. Loh, Andrew G. Flood, Chengliang Mao, Geetu Sharma, Nazir P. Kherani

Bibliographic record

Venue2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) · 2022
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAtomic layer depositionMaterials scienceInversion (geology)OptoelectronicsLayer (electronics)OxideInterface (matter)Thin filmSiliconComputer scienceNanotechnologyComposite materialGeologyMetallurgy

Abstract

fetched live from OpenAlex

A recombination-free high-quality interface is desired for all semiconductor solar cells. An induced inversion layer and the resulting concentrated surface electric field are critical factors for the reduction of photocarrier recombination. The surface inversion layer has been widely predicted at the interface of two materials with different work functions leading to large energy band bending. Herein, we present the direct observation of an atomic thin hole inversion layer between cubic-phase indium tin oxide (c-ITO)/native oxide/n-Si interface using transmission electron microscopy. Excellent lattice matching, atomic oxidation of pristine Si substrate and interfacial charge engineering enable this high-quality interface and its revelation. A facile process of air-annealing and commensurate adjacent thin film phase transition is explored. The device exhibits an ultra-low recombination rate at the interface and internal quantum efficiency (IQE) of over 97% for a broad range of wavelengths. This presentation will provide an understanding of the silicon - native oxide semiconductor interface as developed via facile processing and electron microscopy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.040
GPT teacher head0.255
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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