Analysis of the Effective Refractive Index of Silicon Waveguides Through the Constructive and Destructive Interference in a Mach–Zehnder Interferometer
Bibliographic record
Abstract
This paper introduces a method of measuring the delta between the effective refractive index of a silicon waveguide and a waveguide with wider dimensions through the constructive and destructive interference in a Mach-Zehnder interferometer (MZI). The method consists of a fixed effective refractive index variation incorporated by tapering one of the arms in the interferometer to a wider waveguide dimension. The MZI consists of a Y-branch splitter and a multimode interference (MMI) coupler. The Y-branch splitter splits the input light 50/50 into the two arms, and the MMI is used for recombination of the two arms. A change in the effective refractive index of one arm in comparison with the other arm in the interferometer will introduce a phase difference on recombination in the MMI. The MMI has the following three ports: the top and bottom output ports, which are the antisymmetric outputs, and the middle port, which is the symmetric output. When the two signals are in phase, all the light is coupled into the symmetric port, and when the two inputs are π out of phase, the light is coupled 50/50 into the antisymmetric ports. The interferometer is designed on a silicon-on-insulator (SOI) wafer and fabricated through IMEC Belgium. Theoretical, simulation, and measured results are presented and compared.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".