Highly Efficient Polarization-Insensitive Grating Couplers on Thin-Film Lithium Niobate with an Integrated Gold Layer
Bibliographic record
Abstract
The thin-film lithium niobate platform, which is emerging as a promising photonic integration platform, currently lacks a polarization-insensitive grating coupler (GC), a crucial component for polarization-independent fiber interfaces. This limitation restricts its use in many applications, such as polarization-insensitive modulation systems and polarization management. In this study, we propose a polarization-insensitive nonuniform GC, achieved by intersecting optimal TE- and TM-mode grating periods. Based on our simulation results, the proposed design delivers a coupling efficiency (CE) of 80% for TE and 78.5% for TM polarization, with a polarization-dependent loss of less than 0.14 dB at a wavelength of 1550 nm. The inserted gold layer, i.e., that inside the substrate layer, boosts the CEs of the optimal TE- and TM-mode GC by about 50%, resulting in a highly efficient, polarization-insensitive solution. This advancement enables on-chip polarization diversity applications on the thin-film lithium niobate platform. We also investigate the fabrication and alignment tolerances of the proposed design to ensure robust performance under practical conditions.
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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) | 0.001 | 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 | 0.001 | 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".