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Record W2995976872 · doi:10.1364/oe.27.037400

Visible-light silicon nitride waveguide devices and implantable neurophotonic probes on thinned 200 mm silicon wafers

2019· article· en· W2995976872 on OpenAlexafffund
Wesley D. Sacher, Xianshu Luo, Yisu Yang, Fu‐Der Chen, Thomas Lordello, Jason C. C. Mak, Xinyu Liu, Ting Hu, Tianyuan Xue, Patrick Lo, M. L. Roukes, Joyce K. S. Poon

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMaterials scienceOpticsWaferOptoelectronicsWaveguideSiliconSilicon nitrideGratingWavelengthPhotonicsPhysics

Abstract

fetched live from OpenAlex

We present passive, visible light silicon nitride waveguides fabricated on ≈ 100 µm thick 200 mm silicon wafers using deep ultraviolet lithography. The best-case propagation losses of single-mode waveguides were ≤ 2.8 dB/cm and ≤ 1.9 dB/cm over continuous wavelength ranges of 466-550 nm and 552-648 nm, respectively. In-plane waveguide crossings and multimode interference power splitters are also demonstrated. Using this platform, we realize a proof-of-concept implantable neurophotonic probe for optogenetic stimulation of rodent brains. The probe has grating coupler emitters defined on a 4 mm long, 92 µm thick shank and operates over a wide wavelength range of 430-645 nm covering the excitation spectra of multiple opsins and fluorophores used for brain stimulation and imaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.282
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations136
Published2019
Admission routes2
Has abstractyes

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