Noise Analysis of Second‐Harmonic Generation in Undoped and MgO‐Doped Periodically Poled Lithium Niobate
Bibliographic record
Abstract
Noise characteristics of second‐harmonic generation (SHG) in periodically poled lithium niobate (PPLN) using the quasiphase matching (QPM) technique are analyzed experimentally. In the experiment, a0.78 μm second‐harmonic (SH) wave was generated when a 1.56 μm fundamental wave passed through a PPLN crystal (bulk or waveguide). The time‐domain and frequency‐domain noise characteristics of the fundamental and SH waves were analyzed. By using the pump‐probe method, the noise characteristics of SHG were further analyzed when a visible light (532 nm) and an infrared light (1090 nm) copropagated with the fundamental light, respectively. The noise characterizations were also investigated at different temperatures. It is found that for the bulk and waveguide PPLN crystals, the SH wave has a higher relative noise level than the corresponding fundamental wave. For the same fundamental wave, the SH wave has lower noise in a bulk crystal than in a waveguide, and in MgO‐doped PPLN than in undoped PPLN. The 532 nm irradiation can lead to higher noise in PPLN than the 1090 nm irradiation. In addition, increasing temperature of device can alleviate the problem of noise in conjunction with the photorefractive effect incurred by the irradiation light. This is more significant in undoped PPLN than in MgO‐doped one.
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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".