Silicon nitride photonic integrated devices for navigation systems
Bibliographic record
Abstract
Integrating photonic devices into commercial and consumer products has expanded their applications beyond research laboratories.This transition is driven by their compact size, robustness under environmental conditions such as vibrations and temperature variations, and low power consumption.Integrated photonic devices are now expanding their development in navigation technologies by providing accurate, reliable positioning, navigation, and timekeeping (PNT) sensing.Silicon nitride (SiN) photonics has emerged as a leading material due to its low loss, moderate Kerr nonlinearity coefficient, negligible two-photon absorption, and broad optical transparency.These characteristics make SiN an ideal choice for advancing integrated photonic devices, enabling high-accuracy distance and rotational movement measurements for advanced navigation systems.This thesis introduces two SiN-based photonic devices exploiting nonlinear and linear optical phenomena.The thesis discusses the design methodology, experimental validation, and design challenges encountered, along with proposed solutions.A comprehensive study on SiN-based photonic devices for navigation sensor systems is also included.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".