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Record W4391735049 · doi:10.1063/5.0186959

Raman-based mapping and depth-profiling of the relaxation state in amorphous silicon

2024· article· en· W4391735049 on OpenAlexafffund
A. W. Lussier, David Bourbonnais-Sureault, M. Chicoine, Richard Martel, L. Martinů, S. Roorda, F. Schiettekatte

Bibliographic record

VenueJournal of Applied Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsPolytechnique MontréalUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsRaman spectroscopyMaterials scienceSiliconAmorphous solidAmorphous siliconMolecular physicsAnalytical Chemistry (journal)Crystalline siliconOptoelectronicsOpticsCrystallographyChemistry

Abstract

fetched live from OpenAlex

We show that the micro-scale variations in the relaxation state of amorphous silicon (a-Si) can be well-identified by Raman mapping over hundreds or thousands of μm2 in 1–2 h. Pure and relaxed a-Si is obtained by self-implantation in crystalline silicon (c-Si) followed by anneal at 500 °C. It is then locally re-implanted over micro-sized patterns to produce unrelaxed a-Si zones. Raman mappings are obtained by pointwise confocal μ-Raman and hyperspectral Raman imaging. We also measure the depth profiles of the relaxation state in re-implanted a-Si by scanning the edge of a re-implanted sample. We infer from the depth profiles that the minimal damage dose to fully de-relax a-Si is 0.04 displacements per atoms, which is an order of magnitude smaller than the fluence needed to fully amorphize c-Si.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.279

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 head0.011
GPT teacher head0.199
Teacher spread0.189 · 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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