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Record W2566062678 · doi:10.1364/boe.8.000500

Optimized sensitivity of Silicon-on-Insulator (SOI) strip waveguide resonator sensor

2017· article· en· W2566062678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomedical Optics Express · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité LavalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsSilicon on insulatorResonatorMaterials scienceWaferOptoelectronicsWaveguideFabricationSilicon photonicsSensitivity (control systems)SiliconPhotonicsCMOSOpticsElectronic engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Evanescent field sensors have shown promise for biological sensing applications. In particular, Silicon-on-Insulator (SOI)-nano-photonic based resonator sensors have many advantages for lab-on-chip diagnostics, including high sensitivity for molecular detection and compatibility with CMOS foundries for high volume manufacturing. We have investigated the optimum design parameters within the fabrication constraints of Multi-Project Wafer (MPW) foundries that result in the highest sensitivity for a resonator sensor. We have demonstrated the optimum waveguide thickness needed to achieve the maximum bulk sensitivity with SOI-based resonator sensors to be 165 nm using the quasi-TM guided mode. The closest thickness offered by MPW foundry services is 150 nm. Therefore, resonators with 150 nm thick silicon waveguides were fabricated resulting in sensitivities as high as 270 nm/RIU, whereas a similar resonator sensor with a 220 nm thick waveguide demonstrated sensitivities of approximately 200 nm/RIU.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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