High-performance dual-polarization mode-order converter in thin-film lithium niobate
Bibliographic record
Abstract
Mode-order converters are crucial components in multimode optical transmission and switching systems. Thin-film lithium niobate (TFLN) has become a promising platform for scalable photonic integrated circuits due to its superior material properties. Here, we present a dual-polarization mode-order converter in TFLN to implement TE0–TE1, TE1–TE0, TM0–TM1, and TM1–TM0 mode conversion processes simultaneously. Numerical simulations show that it can achieve an insertion loss below 0.28 dB and a total crosstalk lower than −25.4 dB over a 300-nm bandwidth. Then, to the best of our knowledge, a TFLN-based dual-polarization mode-order converter has been experimentally demonstrated for the first time, with an insertion loss below 1.09 dB and a crosstalk of each input mode lower than −21.4 dB within the 1500–1600 nm wavelength range. Owning to the low loss, low crosstalk, broad bandwidth, and high fabrication tolerance, the proposed device offers a scalable and efficient solution for hybrid multiplexing in advanced photonic networks.
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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 | 0.000 | 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".