Inverse-designed grating couplers for hybrid silicon nitride photonics using metaheuristic methods
Bibliographic record
Abstract
Silicon nitride (Si 3 N 4 ) has emerged as an attractive photonic platform for implementing passive optical components due to its ultra-low propagation losses and broad transparency window. However, its low refractive index contrast presents persistent design challenges for low-loss surface grating couplers, resulting in poor radiation and sub-optimal grating-to-fiber mode matching. High-index overlays situated atop the Si 3 N 4 structure are a promising strategy to tackle these limitations. Although this hybrid configuration offers enhanced design flexibility, it also introduces additional complexity into the design space, rendering conventional optimization methods inefficient. To address this, in this work, we investigate inverse-designed grating couplers employing metaheuristic optimization techniques tailored for the hybrid α −Si/Si 3 N 4 platform. In particular, we apply three distinct metaheuristic algorithms—particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA)—in conjunction with finite-difference time-domain (FDTD) simulations. Our results demonstrate that all three methods can effectively explore the multi-dimensional design space and achieve low fiber-chip coupling losses at the telecom waveband. This work establishes a scalable inverse design framework for the development of high-performance grating couplers on hybrid Si 3 N 4 platforms, demonstrating the practical viability of metaheuristic optimization for low-loss input/output optical interfaces. The proposed approach not only advances current coupling performance but also paves the way for future innovations in the automated design of photonic chip interfaces for next-generation on-chip systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".