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Record W4395484165 · doi:10.1134/s1063784223090062

Blazed Silicon Gratings for Soft X-Ray and Extreme Ultraviolet Radiation: the Effect of Groove Profile Shape and Random Roughness on the Diffraction Efficiency

2023· article· en· W4395484165 on OpenAlexaff
Leonid I. Goray, V. A. Sharov, D. V. Mokhov, T. N. Berezovskaya, K. Yu. Shubina, E. V. Pirogov, A. S. Dashkov, A. D. Bouravleuv

Bibliographic record

VenueTechnical Physics · 2023
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsLibrary of Parliament
Fundersnot available
KeywordsMaterials scienceGroove (engineering)OpticsRadiationExtreme ultravioletDiffractionDiffraction efficiencySiliconSurface finishDiffraction gratingOptoelectronicsPhysicsComposite materialLaser

Abstract

fetched live from OpenAlex

Abstract The effect of the groove profile shape and random roughness of the reflecting facet of five silicon diffraction gratings (1°–4° blaze angle, period 0.4, 1.4, 2, and 4 μm, various coatings) operating in the soft X-ray and extreme ultraviolet radiation ranges on the outflow of the diffraction efficiency from working orders is studied. Diffraction gratings were fabricated by wet etching of Si(111) vicinal wafers and characterized by atomic force microscopy to determine the shape of the groove profile and roughness. The diffraction efficiency of gratings operating in classical and conical diffraction mounts was calculated based on realistic groove profiles by computer simulation using the PCGrateTM code and taking into account the scattering intensity using Nevot–Croce or Debye–Waller corrections or using the Monte Carlo method (rigorously). The effect ofthe groove profile shape and roughness on the diffraction efficiency of the fabricated Si gratings is shown.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.127
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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