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Record W2902984861 · doi:10.1109/mwp.2018.8552873

Programmable On-Chip Photonic Signal Processor Based on a Microdisk Resonator Array

2018· article· en· W2902984861 on OpenAlexaff
Weifeng Zhang, Jianping Yao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsReconfigurabilityResonatorPhotonicsComputer scienceSignal processingOptical switchElectronic engineeringSIGNAL (programming language)Routing (electronic design automation)ScalabilityDigital signal processorChipOptoelectronicsElectrical engineeringComputer hardwareMaterials scienceDigital signal processingEngineeringEmbedded systemTelecommunications

Abstract

fetched live from OpenAlex

A programmable on-chip photonic signal processor based on a silicon photonic microdisk resonator array is proposed and experimentally demonstrated. The processor has a two-dimensional mesh network structure with multiple input and multiple output ports. In each mesh cell, two identical thermally-tunable high-Q microdisk resonators (MDRs) are used for routing and processing the optical signal, and a low-loss waveguide crossing is employed at the waveguide intersection to enable low-crosstalk optical transmission. By programming the DC voltages applied to the MDRs, the processor can be reconfigured with diverse circuit topologies to perform multiple array signal processing functions. An 8 × 8 programmable signal processor is designed, fabricated and characterized. By controlling the DC voltages, an on-chip tunable optical delay line based on 8 MDRs cascaded in an all-pass filter configuration is experimentally demonstrated. Thanks to scalable mesh structure of the proposed processor, the entire device holds a promising potential for strong reconfigurability and parallel computing with low power consumption.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.689
Threshold uncertainty score0.968

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)0.0010.001

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 head0.015
GPT teacher head0.247
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

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