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Record W4402854857 · doi:10.1021/acs.nanolett.4c03402

Acoustic Modulation of Individual Nanowire Quantum Dots Integrated into a Hybrid Thin-Film Lithium Niobate Photonic Platform

2024· article· en· W4402854857 on OpenAlexaff
Thomas Descamps, Tanguy Schetelat, Jun Gao, Philip J. Poole, Dan Dalacu, Ali W. Elshaari, Val Zwiller

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersH2020 Future and Emerging TechnologiesWallenberg Centre for Molecular and Translational MedicineKnut och Alice Wallenbergs StiftelseWallenberg Center for Quantum Technology, Chalmers University of TechnologyEuropean Commission
KeywordsLithium niobateNanowirePhotonicsMaterials scienceQuantum dotOptoelectronicsModulation (music)Thin filmNanotechnologyPhysicsAcoustics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Surface acoustic waves are a powerful tool for controlling quantum systems, including quantum dots (QDs), where the oscillating strain field can modulate the emission wavelengths. We integrate InAsP/InP nanowire QDs onto a thin-film lithium niobate platform and embed them within Si 3 N 4 -loaded waveguides. We achieve a 0.70 nm peak-to-peak wavelength modulation at 13 dBm using a single focused interdigital transducer (FIDT) operating at 400 MHz, and we double this amplitude to 1.4 nm by using two FIDTs as an acoustic cavity. Additionally, we independently modulate two QDs with an initial wavelength difference of 0.5 nm, both integrated on the same chip. We show that their modulated emissions overlap, demonstrating the potential to bring them to a common emission wavelength after spectral filtering. This local strain-tuning represents a significant step toward generating indistinguishable single photons from remote emitters heterogeneously integrated on a single chip, advancing on-chip quantum information processing with multiple QDs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.919

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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