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Record W2967829315 · doi:10.1109/phosst.2019.8795081

Neuromorphic Silicon Photonics on Foundry and Cryogenic Platforms

2019· article· en· W2967829315 on OpenAlex
Alexander N. Tait, Sae Woo Nam, Richard P. Mirin, Bhavin J. Shastri, Paul R. Prucnal, Thomas Ferreira de Lima, Jeffrey M. Shainline, Sonia Buckley, Adam N. McCaughan, Mitchell A. Nahmias, Jeff Chiles, Hsuan-Tung Peng, Heidi B. Miller

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsQueen's University
Fundersnot available
KeywordsNeuromorphic engineeringFoundryPhotonicsSilicon photonicsSiliconMaterials scienceComputer scienceOptoelectronicsEngineeringMechanical engineeringArtificial neural networkArtificial intelligence

Abstract

fetched live from OpenAlex

Silicon photonics presents an opportunity for complex, multi-purpose information processing with optoelectronics. Using foundry devices, some photonic neural networks support sub-nanosecond signals. Using cryogenic devices, others support single-photon signals. We compare these platforms and summarize recent experimental results on programmability in photonic neurons and networks.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.347

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 head0.017
GPT teacher head0.207
Teacher spread0.191 · 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

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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