A Theoretical Study of Maker Fringe Measurements in Poled Multi-Layer Silica Structures Focusing on the Impact of Layer Quantity and Spacing
Bibliographic record
Abstract
A second order nonlinearity in silica glass, which does not exist intrinsically, can be achieved through thermal poling.The poling process breaks the intrinsic symmetry of the silica allowing for even ordered non-linearities.After poling was first successfully demonstrated in 1991, the achievable nonlinearity has remained weak (after several attempts).Recently, some progress has been made through the development of multi-layer silica structures created through alternating dopant concentration.Larger observed nonlinearities in these samples (through the creation of second harmonic radiation) suggest that this may have created a distributed non-linearity that could result in more efficient frequency doubled laser sources.The impact of layer spacing on the observed nonlinearity has remained an open question.In this thesis a numerical model is developed with the goal investigating the impact of the quantity and spacing between layers on second harmonic generation.The thesis begins with a brief history on poling techniques and various attempts to induce greater second order nonlinearities in silica.We then develop the theoretical model that describes second harmonic generation in multi-layer structures.This section is followed by a description of our experiment which basically is simulation model and the thesis finishes with a discussion of the conclusions drawn from the model.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".