On-chip Optical Phase Monitoring in Multi-Transverse-Mode Integrated Silicon-based Optical Processors
Bibliographic record
Abstract
We design a Multi-Transverse-Mode Optical Processor (MTMOP) on 220 nm thick Silicon Photonics exploiting the first two quasi-transverse electric modes (TE0 and TE1). The objective is to measure the optical phase, required for programming the optical processor, without use of conventional optical phase detection techniques (e.g., coherent detection). In the proposed design, we use a novel but simple building block that converts the optical phase to optical power. Mode TE0 carries the main optical signal while mode TE1 is for programming purposes. The MTMOP operation relies on the fact that the group velocity of TE0 and TE1 propagating through amode-sensitivephase shifter are different. The mode-sensitive phase shifter is a waveguide with 0.96 µm width underneath a titanium-tungsten heater. Increasing the width of the phase shifter to 4 µm, the propagation becomes mode-insensitive. We use an unbalanced Mach-Zehnder interferometer (MZI) consists of a mode-sensitive and mode-insensitive phase shifters in the two arms. We set the bias of the phase shifters so that TE0 propagating in the two arms constructively interfere while this will not be the case for TE1. Hence, we detect the phase shift applied to TE0 by measuring the variation in the optical power of TE1. To the best of our knowledge, this design is the first attempt towards realizing a programmable optical processor with fully integrated programming unit exploiting multimode silicon photonics.
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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 | 0.000 | 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".