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Record W4412968914 · doi:10.1117/12.3063216

Aluminum nitride photonic integrated circuits with applications to trapped atoms

2025· article· en· W4412968914 on OpenAlexaff
Nikolay Videnov, Michal Bajcsy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhotonicsNitridePhotonic integrated circuitOptoelectronicsElectronic circuitMaterials scienceIntegrated circuitPhotonic crystalComputer scienceElectronic engineeringNanotechnologyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Cold atom experiments require many distinct laser wavelengths, each frequency-locked to their respective atomic transition and with precise acousto-optic and/or electro-optic modulation for shaping light pulses in frequency and intensity. These necessary optics are the primary challenge to the next generation of portable trapped atom quantum sensors. We present progress towards an optics lab-on-a-chip with on-chip laser source and modulators for portable trapped atom quantum experiments. This is achieved by leveraging the unique properties of aluminum nitride (AlN): optical transparency into the ultra-violet and non-zero electro-optic coefficient. These two features alone allow on chip integration of laser source and electro-optic modulation in the same waveguide layer. We show lasing results and new modulation results at 852 nm (S1/2-P3/2 in cesium).

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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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