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Record W4414490674 · doi:10.1364/oe.576868

On-chip near 100 dB pump filtering using silicon ion implantation

2025· article· en· W4414490674 on OpenAlexaff
Akhil Varri, Daniel Wendland, Ravi Pradip, Zhe Zhao, Emma Lomonte, Francesco Lenzini, Frank Brückerhoff‐Plückelmann, Wolfram H. P. Pernice

Bibliographic record

VenueOptics Express · 2025
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsXanadu Quantum Technologies (Canada)
FundersHORIZON EUROPE European Research CouncilHORIZON EUROPE European Innovation CouncilDeutsche Forschungsgemeinschaft
KeywordsPhotonicsPhotonSilicon photonicsAbsorption (acoustics)SiliconDetectorIon implantationQuantum opticsQuantum channelSilicon nitride

Abstract

fetched live from OpenAlex

Integrated photonics is an exciting platform for quantum communication, computing, and sensing due to its inherent stability and scalability. However, full monolithic or hybrid photonic quantum technology demonstrations remain limited owing to performance and integration challenges. One of the main challenges is the filtering the high-power classical pump light from the generated photons in spontaneous parametric down-conversion sources. Here, we report what we believe to be a novel optical filtering technique based on silicon ion implantation in silicon nitride (SiN) waveguides. Our approach effectively introduces selective absorption of near-visible light, achieving nearly 100 dB suppression of the pump light at 775 nm. On the contrary, the insertion loss for light in the telecom C-band near 1550 nm is just around 4 dB with a scope of significant reduction. This result paves the way for practical and efficient integration of quantum sources, filters, and detectors on-chip, advancing the field toward scalable quantum photonic systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.698

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 head0.253
Teacher spread0.237 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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