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Record W2782337064 · doi:10.1063/1.5003260

Surface recombination velocity imaging of wet-cleaned silicon wafers using quantitative heterodyne lock-in carrierography

2018· article· en· W2782337064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsCanada Foundation for Innovation
KeywordsWaferPassivationMaterials scienceEtching (microfabrication)Heterodyne (poetry)Hydrofluoric acidSIGNAL (programming language)OpticsAmplitudeOptoelectronicsAnalytical Chemistry (journal)ChemistryPhysicsAcousticsNanotechnologyComputer science

Abstract

fetched live from OpenAlex

InGaAs-camera based heterodyne lock-in carrierography (HeLIC) is developed for surface recombination velocity (SRV) imaging characterization of bare (oxide-free) hydrogen passivated Si wafer surfaces. Samples prepared using four different hydrofluoric special-solution etching conditions were tested, and a quantitative assessment of their surface quality vs. queue-time after the hydrogen passivation process was made. The data acquisition time for an SRV image was about 3 min. A “round-trip” frequency-scan mode was introduced to minimize the effects of signal transients on data self-consistency. Simultaneous best fitting of HeLIC amplitude-frequency dependencies at various queue-times was used to guarantee the reliability of resolving surface and bulk carrier recombination/transport properties. The dynamic range of the measured SRV values was established from 0.1 to 100 m/s.

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.

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 score0.096
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.231
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