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Record W1958183621 · doi:10.1039/c5nr06822c

Layered nano-gratings by electron beam writing to form 3-level diffractive optical elements for 3D phase-offset holographic lithography

2015· article· en· W1958183621 on OpenAlexaff
Yuan Liang, Peter R. Herman

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHolographyLithographyElectron-beam lithographyOpticsMaterials scienceOffset (computer science)GratingStencil lithographyNext-generation lithographyDiamondX-ray lithographyExtreme ultraviolet lithographyOptoelectronicsNano-Phase (matter)Diffraction gratingMaskless lithographyCathode rayElectronPhysicsNanotechnologyResistComputer science

Abstract

fetched live from OpenAlex

A multi-level nanophotonic structure is a major goal in providing advanced optical functionalities as found in photonic crystals and metamaterials. A three-level nano-grating phase mask has been fabricated in an electron-beam resist (ma-N) to meet the requirement of holographic generation of a diamond-like 3D nanostructure in photoresist by a single exposure step. A 2D mask with 600 nm periodicity is presented for generating first order diffracted beams with a preferred π/2 phase shift on the X- and Y-axes and with sufficient 1(st) order diffraction efficiency of 3.5% at 800 nm wavelength for creating a 3D periodic nanostructure in SU-8 photoresist. The resulting 3D structure is anticipated to provide an 8% complete photonic band gap (PBG) upon silicon inversion. A thin SiO2 layer was used to isolate the grating layers and multiple spin-coating steps served to planarize the final resist layer. A reversible soft coating (aquaSAVE) was introduced to enable SEM inspection and verification of each insulating grating layer. This e-beam lithographic method is extensible to assembling multiple layers of a nanophotonic structure.

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)0.000
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 score0.393
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.315
Teacher spread0.291 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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