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Record W1606941831 · doi:10.1063/1.4923379

Uncertainty analysis for the coefficient of band-to-band absorption of crystalline silicon

2015· article· en· W1606941831 on OpenAlexfundno aff
Carsten Schinke, Christian Peest, J. Schmidt, Rolf Brendel, Karsten Bothe, M. Vogt, Ingo Kröger, S. Winter, A. Schirmacher, Siew Yee Lim, Hieu T. Nguyen, Daniel Macdonald

Bibliographic record

VenueAIP Advances · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersInstitute of Gender and HealthLeibniz-GemeinschaftGottfried Wilhelm Leibniz Universität HannoverDeutsche ForschungsgemeinschaftDeutsche Bundesstiftung Umwelt
KeywordsAttenuation coefficientMaterials scienceTransmittanceAbsorption (acoustics)SiliconMeasurement uncertaintyOpticsCrystalline siliconAnalytical Chemistry (journal)EllipsometryTemperature coefficientMolar absorptivityWavelengthOptoelectronicsThin filmChemistryPhysicsNanotechnology

Abstract

fetched live from OpenAlex

We analyze the uncertainty of the coefficient of band-to-band absorption of crystalline silicon. For this purpose, we determine the absorption coefficient at room temperature (295 K) in the wavelength range from 250 to 1450 nm using four different measurement methods. The data presented in this work derive from spectroscopic ellipsometry, measurements of reflectance and transmittance, spectrally resolved luminescence measurements and spectral responsivity measurements. A systematic measurement uncertainty analysis based on the Guide to the expression of uncertainty in measurement (GUM) as well as an extensive characterization of the measurement setups are carried out for all methods. We determine relative uncertainties of the absorption coefficient of 0.4% at 250 nm, 11% at 600 nm, 1.4% at 1000 nm, 12% at 1200 nm and 180% at 1450 nm. The data are consolidated by intercomparison of results obtained at different institutions and using different measurement approaches.

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)0.008
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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".

Quick stats

Citations498
Published2015
Admission routes1
Has abstractyes

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