Second-order susceptibility measurement of thin films by reflective second harmonic generation method: toward measurement standards in nonlinear optics
Bibliographic record
Abstract
There is a strong need for a simple and reliable characterization technique of nonlinear optical effects applicable to thin films. The second-order susceptibility is the parameter that plays a key role not only in SHG but also in cascading phenomena and optical bistabilities. The measurement methods of second-order susceptibility are divided into absolute and relative techniques. Relative measurements were performed to obtain the relative values of second-order susceptibility by comparison with a reference material, which is often crystalline quartz. There is a different approach for nonlinear optical characterization that depends on the type of sample: crystal or thin film. There are two methods used for second-order susceptibility measurement of thin films: a Maker fringe and reflective SHG. The Maker fringe is limited to the investigation of thin films on transparent substrates. Here we discuss an experimental protocol based on reflective SHG for nonlinear optical characterization of thin films. Since most measurements are performed relative to a reference material, the establishment of a well-accepted value for a standard material is important. The SHG in reflection of z-cut quartz is discussed in detail. The method is simple and reliable but limited to thin films. Measurements at different wavelengths and mapping will be reported.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".