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Record W4404539824 · doi:10.1021/acsphotonics.4c02115

Double Etch Method for the Fabrication of Nanophotonic Devices from van der Waals Materials

2024· article· en· W4404539824 on OpenAlexaff
Otto Cranwell Schaeper, Lesley Spencer, Dominic Scognamiglio, Waleed El-Sayed, Benjamin Whitefield, Jake Horder, Nathan Coste, Paul E. Barclay, Milos Toth, Anastasia Zalogina, Igor Aharonovich

Bibliographic record

VenueACS Photonics · 2024
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Calgary
FundersAustralian Research Council
KeywordsFabricationvan der Waals forceNanophotonicsMaterials scienceNanotechnologyOptoelectronicsPhysicsMoleculeQuantum mechanics

Abstract

fetched live from OpenAlex

The integration of van der Waals (vdW) materials into photonic devices has laid out a foundation for many new quantum and optoelectronic applications. Despite tremendous progress in the nanofabrication of photonic building blocks from vdW crystals, there are still limitations, specifically with large-area devices and masking. Here, we focus on hexagonal boron nitride (hBN) as a vdW material and present a double etch method that overcomes problems associated with methods that employ metallic films and resist-based films for masking. Efficacy of the developed protocol is demonstrated by designing and fabricating a set of functional photonic components─including waveguides, ring resonators, and photonic crystal cavities. The functionality of the fabricated structures is demonstrated through optical characterization over several key spectral ranges. These include the near-infrared and blue ranges, where the hBN boron vacancy (V B – ) spin defects and the coherent B center quantum emitters emit, respectively. The double etch method enables fabrication of high-quality factor optical cavities and constitutes a promising pathway toward on-chip integration of vdW materials.

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 score0.185
Threshold uncertainty score0.369

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 head0.018
GPT teacher head0.285
Teacher spread0.266 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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