Broadband and low-loss TM-pass polarizer using tilted subwavelength structures
Bibliographic record
Abstract
Photonic systems built on the Silicon-on-Insulator platform exhibit a strong birefringence, and must thus be operated with a single polarization for most applications. Hence, on-chip polarizers that can effectively suppress an undesired polarization state are key components for these systems. Polarizers that extinguish TE polarized light while letting TM polarized light pass with low losses are particularly challenging to design for the standard 220 nm Silicon-on-Insulator platform, because the modal confinement is stronger for TE polarization than for TM polarzation. Here, we propose and design a broadband, low loss and high extinction ratio TM-pass polarizer by engineering a Bragg grating that reflects the fundamental TE mode into the first order TE mode using a subwavelength metamaterial which at the same time allows the TM mode to pass. Our device achieves an extinction ratio in excess of 20 dB, insertion losses below 0.5 dB and back-reflections of the fundamental TE mode of the order of -20 dB in a bandwidth of 150 nm as demonstrated with full 3D-FDTD simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".