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Record W4285009521 · doi:10.1364/optica.458727

Spatially resolving amplitude and phase of light with a reconfigurable photonic integrated circuit

2022· article· en· W4285009521 on OpenAlexaff
Johannes Bütow, Jörg S. Eismann, Maziyar Milanizadeh, Francesco Morichetti, Andrea Melloni, David A. B. Miller, Peter Banzer

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersHorizon 2020 Framework Programme
KeywordsAmplitudePhotonicsPhotonic integrated circuitPhase (matter)Integrated circuitOptoelectronicsPhysicsOptics

Abstract

fetched live from OpenAlex

Photonic integrated circuits (PICs) play a pivotal role in many applications. Particularly powerful are circuits based on meshes of reconfigurable Mach-Zehnder interferometers as they enable active processing of light. Various possibilities exist to get light into such circuits. Sampling an electromagnetic field distribution with a carefully designed free-space interface is one of them. Here, a reconfigurable PIC is used to optically sample and process free-space beams so as to implement a spatially resolving detector of amplitudes and phases. In order to perform measurements of this kind we develop and experimentally implement a versatile method for the calibration and operation of such integrated photonics based detectors. Our technique works in a wide parameter range, even when running the chip off the design wavelength. Amplitude, phase and polarization sensitive measurements are of enormous importance in modern science and technology, providing a vast range of applications for such detectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.169
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2022
Admission routes1
Has abstractyes

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