Optimized 3D design of an MMI splitter with ion exchange technology
Bibliographic record
Abstract
Multimode interference (MMI) couplers are important integrated optical components for the optical signal processing and routing. The realization of these components by ion exchange on glass substrates is particularly attractive for low cost integration. The design and analysis of MMI devices have generally been based on the self imaging principle in step-index waveguides, whereas waveguides fabricated by ion exchange on glass are practically graded-index due to the nature of the thermal diffusion of exchanged ions. In addition, the ion exchange process results in a guide with depth that depends on the mask opening (the guide width) which causes a high insertion loss at the interface between single mode and multimode sections of the MMI. To overcome these problems 3D simulation of the ion exchanged MMI structures is strongly required. In this work such 3D simulation is achieved on two levels. First the non-linear diffusion equation describing the ion exchange process is solved numerically using a finite-difference method with a modified algorithm to ensure solution stability for an extended range of nonlinearity. The resultant index distribution is used in a wide angle 3D BPM to simulate the optical field propagation in the structure. This allows accurate prediction of the structure performance under different fabrication and excitation conditions. Based on this simulation technique, 3 dB MMI splitter design with tapered access guides is optimized by both geometrical mask design and process parameter variations. The optimization shows that both the tapering and the use of annealing process can significantly improve the performance of the devices.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".