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Record W4390822547 · doi:10.1116/6.0003322

Fabrication of blazed gratings by tilted reactive ion beam etching with the side mask for augmented reality applications

2024· article· en· W4390822547 on OpenAlexafffund
Y.F. Wang, Aixi Pan, Xiaoli 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
KeywordsMaterials scienceFabricationOpticsShadow maskResistLithographyEtching (microfabrication)Reactive-ion etchingElectron-beam lithographyOptoelectronicsNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

A novel manufacturing method using the side mask and tilted reactive ion beam etching (RIBE) is proposed for the fabrication of blazed gratings. Electron beam lithography was carried out to pattern a groove with a nanoscale line width, and then appropriate mask materials were filled into the SiO2 trench by atomic layer deposition. After being etched by RIBE at specific tilted angles, the required blazed gratings were achieved. In contrast to the typical fabrication process with a patterned mask on top of the surface to be etched, V-shaped nano structures filled into the trenches were used as the side mask. During etching, the surface material located in the shadow of the side mask along incident ion beams was not etched. The depth and blazed angle of blazed gratings are determined by the height of the side mask and the mounting angle of the sample, respectively. In addition, the fabricated blazed gratings can be used as imprinting moulds to duplicate blazed gratings for augmented reality applications. Due to more options for side mask materials, this method is able to provide high repeatability and accurate controllability for fabricating blazed gratings.

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 score0.047
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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

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