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Optimization of periodic column growth in glancing angle deposition for photonic crystal fabrication

2008· article· en· W1981802321 on OpenAlexafffund
Mark A. Summers, Michael J. Brett

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersKillam TrustsUniversity of Alberta
KeywordsMaterials scienceFabricationAnisotropyAzimuthOffset (computer science)SiliconOpticsPhotonic crystalSquare latticeOptoelectronicsCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

We investigate the growth of periodically aligned silicon microstructures for the fabrication of square spiral photonic crystals using the glancing angle deposition phi-sweep process. We report the optimization of the phi-sweep offset angle for fabrication of microstructures with more precise geometry. The effects of varying the sweep offset angle of the phi-sweep process are studied for films deposited onto a square lattice array of growth seeds. To represent one growth segment of the phi-sweep process, we fabricate 15 nm silicon thin films using several azimuthal substrate offsets from 0° to 45° at a vapor incidence angle of 85°. We also deposit silicon square spirals on square lattice arrays with the phi-sweep method, using various sweep offset angles from γ = 0° to 45°. We find that using an offset angle of γ = 26.5° optimizes the shadowing geometry, which minimizes anisotropic broadening, producing greater quality photonic crystal structures. From normal incidence reflection spectroscopy, a maximum full width at half-maximum of 273 ± 3 nm and a relative peak width (Δλ/λ) of 16.1 ± 0.1% were found for a sweep offset angle of γ = 26.5°.

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 score0.302
Threshold uncertainty score0.353

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 head0.009
GPT teacher head0.226
Teacher spread0.218 · 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

Citations41
Published2008
Admission routes2
Has abstractyes

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