Silicon Photonics Mode-Selective Phase Shifter
Bibliographic record
Abstract
A novel mode-selective thermo-optic phase shifter (MS-TOPS) enabled by subwavelength grating (SWG) structures is proposed and experimentally demonstrated on a 220 nm waveguide thick silicon photonics chip for the first two quasi-transverse electric modes (TE0, TE1). Mode-selective relative phase manipulation of modes unlocks several processing tasks in mode division multiplexing systems. This integrated solution provides a direct phase manipulation of modes without converting them to their fundamental modes. A Mach-Zehnder interferometer is deployed as a test structure incorporating the proposed MS-TOPS in one arm and a mode-insensitive thermo-optic phase shifter (MI-TOPS) in another. The effect of the SWG duty cycle ratio is investigated by both numerical simulations and experimental measurements. A mode-selectivity of 1.44 is experimentally demonstrated. In other words, the thermo-optic coefficient of TE0 is 44% larger than the one for TE1. The phase shifter's insertion loss is at most 2.5 dB and a worst-case crosstalk of -13.1 dB over a 40 nm wavelength range from 1520 to 1560 nm. A cascaded configuration of the proposed MS-TOPS and an MI-TOPS provides sufficient degrees of freedom to manipulate the relative phase of each mode independently. Potential numerous applications of such devices include optical switching, multimode quantum optical processors, and scaling-up conventional optical processors with a mode selective building block.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".