Ultrabroadband Light Coupling for Integrated Photonics via Nonadiabatic Pumping
Bibliographic record
Abstract
Abstract Enlarging bandwidth capacity of the integrated photonic systems demands efficient and broadband light coupling among optical elements, which is a vital issue in integrated photonics. Here, an ultrabroadband light coupling strategy based on nonadiabatic pumping is developed, and the designs in thin‐film lithium niobate on insulator platform are experimentally demonstrated. It is found that nonadiabatic transition produces a decreased dispersion of the phases related to eigenstates in the waveguides. As a consequence, high‐efficiency and dispersionless directional transfer are realized between edge states, which leads to an ultrabroadband light coupling covering a 1 dB bandwidth of ≈320 nm in experiment (>400 nm in simulation), with a length (≈50 µm) ≈1/10 of that required in conventional adiabatic transfer approach. Moreover, the coupling strategy exhibits a low insertion loss (<1 dB), a low crosstalk (<−10 dB), and a great robustness against structural deviations (≈100 nm). Furthermore, complex functional devices including beamsplitter and multiple‐level cascaded networks are constructed for broadband light routing and splitting. This work preserves significant advantages simultaneously in extending the operation bandwidth to all of the optical communication bands and minimizing the footprint, which demonstrates great potential for large‐scale photonic integration and high‐speed information processing on chip.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".