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Record W4392593845 · doi:10.1515/nanoph-2023-0819

Optimization of a programmable <i>λ</i> /2‐pitch optical phased array

2024· article· en· W4392593845 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

VenueNanophotonics · 2024
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMax-Planck-GesellschaftCMC Microsystems
KeywordsPhased arrayPhased-array opticsComputer scienceMacrocell arrayComputer architectureElectronic engineeringOptoelectronicsMaterials scienceOpticsPhysicsTelecommunicationsEngineeringLogic gateLogic synthesis

Abstract

fetched live from OpenAlex

A challenge in optical phased arrays (OPAs) is to achieve single-lobe emission using densely spaced emitters without incurring inter-waveguide optical crosstalk. Here, we propose to heuristically optimize the amplitude and phase of each grating antenna in an OPA to correct for optical non-idealities, including fabrication variations and inter-waveguide crosstalk. This method was applied to a silicon photonic integrated circuit with 1 mm-long gratings at 775 nm spacing for operation in a wavelength range of 1450-1650 nm. We achieved a wide two-dimensional beam-steering range of 110° × 28°, evaluated over a 127° × 47° field-of-view (FOV). Within this FOV, we measured an average sidelobe suppression of 8.2 dB and focused on average, 34.5 % of the emitted power into the main lobe. We achieved a peak sidelobe suppression of 14.5 dB and 50 % of the power concentrated in the main lobe. The approach is suitable for applications that require alias-free out-of-plane emission.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.478
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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