High pump depletion second‐harmonic generation using domain engineered thin‐film lithium niobate waveguides
Bibliographic record
Abstract
Abstract Thin‐film lithium niobate (TFLN) has emerged as a powerful platform for integrated nonlinear optics owing to its large χ (2) nonlinearity, tight confinement and flexible tunability. To fully excavate such superior nonlinear optical properties, domain engineering is commonly adopted to fulfill the phase matching condition of χ (2) processes. During the past decade, various domain engineered TFLN nonlinear optical devices have been demonstrated, showing extremely high length‐normalized nonlinear optical conversion efficiencies. However, application‐driven scenarios demand absolute energy conversion in nonlinear frequency conversion rather than length‐normalized efficiencies, but the progress has been limited by imperfect fabrication processes. In this work, we realize effective on‐chip nonlinear energy conversion by developing low‐loss and high‐quality domain engineered TFLN waveguides with long interaction length. Ion beam trimming (IBT) technique and an etching‐prior‐poling workflow are adopted for such fabrication. Optical characterization yields an overall second‐harmonic generation (SHG) efficiency of 2,590 %/W. A high pump depletion of 85.7 % is demonstrated under continuous‐wave operation, which directly reflects strong nonlinear energy conversion. These results may lead to breakthroughs in applications like classical optical frequency conversion, quantum frequency conversion, and quantum light generation.
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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 | 0.000 |
| 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".