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Record W4392709810 · doi:10.1116/6.0003479

Fabrication of slanted gratings by using glancing angle deposition

2024· article· en· W4392709810 on OpenAlexafffund
Hongwen Wu, Aixi Pan, Chenxu Zhu, Bo Cui

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2024
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationIndustry Canada
KeywordsFabricationMaterials scienceOpticsDeposition (geology)OptoelectronicsPhysicsGeology

Abstract

fetched live from OpenAlex

Slanted gratings, commonly used for manipulating light in various applications, are typically fabricated using conventional top-down methods. However, these methods have limitations on material choice. This paper explores the use of glancing angle deposition (GLAD) to fabricate slanted gratings with various materials and slant angles on silicon (Si) and quartz (SiO2) substrates. The process involves the first step of creating a template using electron beam lithography, lift-off, and dry etching, and the second step of electron beam evaporation at a glancing angle on the prefabricated template. The template consists of grating structures with very shallow trenches. Different materials, such as chromium (Cr), copper (Cu), aluminum oxide (Al2O3), and titanium oxide (TiO2), were used in the GLAD process to create slanted grating structures on Si or SiO2 substrates, showcasing their versatility. Here, the formation of the slanted grating is due to the shadowing effect that leads to deposition onto the protruded grating lines but not into the trench. Using TiO2 as the source material, the GLAD technique can produce slanted gratings with various angles by adjusting the deposition angle. The optical characteristics of the slanted grating prepared using GLAD were verified through simulations with COMSOL software, confirming its excellent light guide performance.

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)0.003
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 score0.022
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.241
Teacher spread0.230 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicOptical Coatings and GratingsFrench-language works237,207