Free space and waveguide Talbot effect: phase relations and planar light circuit applications
Bibliographic record
Abstract
Optical fields that are periodic in the transverse plane self-image periodically as they propagate along the optical axis: a phenomenon known as the Talbot effect. A transfer matrix may be defined that relates the amplitude and phase of point sources placed on a particular grid at the input to their respective multiple images at an image plane. The free-space Talbot effect may be mapped to the waveguide Talbot effect. Applying this mapping to the transfer matrix enables the prediction of the phase and amplitude relations between the ports of a Multimode Interference (MMI) coupler– a planar waveguide device. The transfer matrix approach has not previously been applied to the free-space case and its mapping to the waveguide case provides greater clarity and physical insight into the phase relationships than previous treatments. The paper first introduces the underlying physics of the Talbot effect in free space with emphasis on the positions along the optical axis at which images occur; their multiplicity; and their relative phase relations determined by the Gauss Quadratic Sum of number theory. The analysis is then adapted to predict the phase relationships between the ports of an MMI. These phase relationships are critical to planar light circuit (PLC) applications such as 90° optical hybrids for coherent optical receiver front-ends, external optical I-Q modulators for coherent optical transmitters; and optical phased array switches. These applications are illustrated by results obtained from devices that have been fabricated and tested by the PTLab in Si micro-photonic integration platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".