Implementation of on‐chip multi‐channel focusing wavelength demultiplexer with regularized digital metamaterials
Bibliographic record
Abstract
Abstract Adiabatic waveguide taper and on‐chip wavelength demultiplexer are the key components of photonic integrated circuits. However, these two kinds of devices which were designed by the traditional semi‐analytic methods or the brute‐force search methods usually have large size. Here, based on the regularized digital metamaterials, a two‐channel focused wavelength demultiplexer with a footprint of 2.4 × 10 μm 2 has been proposed. The designed demultiplexer can directly connect to a grating coupler under the absence of a long adiabatic waveguide taper. The objective first method and the modified steepest descent method are used to design the demultiplexer which splits 1520 nm and 1580 nm light. Experimental results show that the insertion loss of the upper (lower) channel of the demultiplexer is −1.77 dB (−2.10 dB) and the crosstalk is −25.17 dB (−12.14 dB). Besides, the simulation results indicate that the fabrication tolerance of the device can reach ±20 nm in etching depth and ±10 nm in plane size changing. Benefitted from the extensibility of the design method, other types of ultra‐compact “focused” devices, like mode splitters, mode converters, and power splitters can also be designed. Most importantly, this design method can be used to design devices with more complicated functionalities, such as multi‐channel focused wavelength demultiplexers.
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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 | 0.000 |
| Open science | 0.001 | 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".