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Record W2043374446 · doi:10.1364/ao.39.000157

High-rate automated deposition system for the manufacture of complex multilayer coatings

2000· article· en· W2043374446 on OpenAlexaff
Brian Sullivan, Glenn A. Clarke, Takayuki Akiyama, Norman Osborne, M. Ranger, J. A. Dobrowolski, Louisa Howe, Akira Matsumoto, Yizhou Song, Kazuo Kikuchi

Bibliographic record

VenueApplied Optics · 2000
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsSputter depositionDeposition (geology)Materials scienceSubstrate (aquarium)Cavity magnetronThin filmOpticsOptical coatingSputteringPhysical vapor depositionOptoelectronicsCoatingNanotechnologyPhysicsGeology

Abstract

fetched live from OpenAlex

A previously described automated thin-film deposition system based on rf-magnetron sputtering could deposit quite complex optical multilayer systems with good precision and with no one in attendance [Sullivan and Dobrowolski, Appl. Opt. 32, 2351-2360 (1993)]. However, the deposition rate was slow, and the uniform area on the substrate was limited. We describe an ac-magnetron sputtering process in which the same deposition accuracy has been combined with significantly better film uniformity and a fivefold or sevenfold increase in the deposition rate. This makes the equipment of commercial interest. Experimental results are presented for several difficult coating problems.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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
GenreMethods

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

Citations43
Published2000
Admission routes1
Has abstractyes

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