Harnessing Electro-Optic Modulation and On-Chip Temporal Interleaving Towards Versatile Frequency Comb Generation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".