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Record W4402415674 · doi:10.1038/s42005-024-01769-5

Design of a monolithic silicon-on-insulator resonator spiking neuron

2024· article· en· W4402415674 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

VenueCommunications Physics · 2024
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsQueen's University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsResonatorSilicon on insulatorSiliconMaterials scienceOptoelectronicsElectrical engineeringElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Increasingly, artificial intelligent systems look to neuromorphic photonics for its speed and its low loss, high bandwidth interconnects. Silicon photonics has shown promise to enable the creation of large scale neural networks. Here, we propose a monolithic silicon opto-electronic resonator spiking neuron. Existing designs of photonic spiking neurons have difficulty scaling due to their dependence on certain nonlinear effects, materials, and devices. The design discussed here uses optical feedback from the transmission of a continuously pumped microring PN modulator to achieve excitable dynamics. It is cascadable, capable of operating at GHz speeds, and compatible with wavelength-division multiplexing schemes for linear weighting. It is a Class 2 excitable device via a subcritical Hopf bifurcation constructed from devices commonly found in many silicon photonic chip foundries.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.438

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 science0.0020.001
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 head0.070
GPT teacher head0.301
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