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Harnessing Electro-Optic Modulation and On-Chip Temporal Interleaving Towards Versatile Frequency Comb Generation

2025· article· en· W4413457945 on OpenAlexaff
Manal Arbati, Ambrine Bougaud, Bruno P. Chaves, Thomas Bunel, Sébastien Février, Brent E. Little, David Moss, Roberto Morandotti, Arnaud Mussot, Benjamin Wetzel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEuropean Research CouncilAgence Nationale de la Recherche
KeywordsInterleavingModulation (music)Frequency modulationComputer scienceChipFrequency combElectronic engineeringOptoelectronicsMaterials scienceTelecommunicationsRadio frequencyOpticsEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

Frequency combs (FCs) have advanced a wide range of scientific fields including spectroscopy, optical communication, and metrology [1]. First developed using mode-locked lasers [2], optical FCs usually rely on stabilization schemes and feedback loops to control the free spectral range (FSR) and the spectral envelope of the comb. Despite the effectiveness of these systems, they often require complex setups and lack tunability. As an alternative, electro-optical modulation (EOM), allows precise control of the FSR and easy implementation with other nonlinear optical systems for versatile applications. However, the limited bandwidth of standard EOMs (~ 50 GHz) restricts the achievable optical spectral profile and bandwidth [3]. To unlock advanced FC tunability, one usually necessitates a cascade of EOMs at the expense of higher losses and complexity, ultimately reaching limited tunability in the output spectral shape. Here, we propose a new approach for versatile frequency comb generation, using a hybrid architecture leveraging EOM picosecond pulse generation paired with adjustable temporal interleaving provided by a photonic integrated circuit (PIC) [4], [5]. The setup, illustrated in Fig. 1(a), is based on an electrical pulse generator (EPG) driving a 20 GHz intensity EOM. Modulating a 1560 nm continuous laser, the system is first used to generate a ~20 ps pulse train with an adjustable repetition rate in the 0.1-10 GHz range. The pulse train is then sent into the PIC encompassing a set of Mach Zehnder interferometers and optical delay lines that allow for adjustable pulse interleaving within the 1–255 ps range (i.e. equivalent to a static modulation up to the THz). Such a coherent superposition of interleaved pulses (yielding different interference patterns as a function of the chosen PIC configuration) is amplified via an Erbium doped fiber amplifier (EDFA) before propagation into a 500 m highly non-linear fiber (HNLF) operating in an anomalous dispersion regime. Figure 1(b-e) shows experimental results at the fiber output, illustrating examples of reconfigurable FCs where the RF and optical spectra are tailored to selectively suppress or enhance specific frequency components. For instance, to demonstrate the versatility of our approach while driving the EPG at 2 GHz, we experimentally show> 44 dB extinction of the 16 GHz harmonic in the RF spectrum while covering ~80 nm in the optical spectrum (Fig. 1(b) - blue lines), or conversely, > 15 dB isolation of the 8 GHz harmonic (Fig. 1(c) - red lines). We further discuss the impact of coherent pulse superposition in FC broadening through cascaded frequency conversion, and the potential of machine learning techniques for on-demand FC tailoring within our hybrid architecture.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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 designNot applicable
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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