Experimental characterization of an ultra-broadband dual-mode symmetric Y–junction based on metamaterial waveguides
Bibliographic record
Abstract
Silicon photonic integrated circuits routinely require 3-dB optical power dividers with minimal losses, small footprints, ultra-wide bandwidths, and relaxed manufacturing tolerances to distribute light across the chip and as a key building block to form more complex devices. Symmetric Y–junctions stand out among other power splitting devices owing to their wavelength-independent response and a straightforward design. Yet, the limited resolution of current fabrication methods results in a minimum feature size (MFS) at the tip between the two Y–junction arms that leads to significant losses for the fundamental mode. Here we propose to circumvent this limitation by leveraging subwavelength metamaterials in a new type of ultra-broadband and fabrication-tolerant Y–junction. An exhaustive experimental study over a 260 nm bandwidth (1420 nm – 1680 nm) shows excess loss below 0.3 dB for the fundamental transverse-electric mode (TE0) for a high-resolution lithographic process (MFS ∼ 50 nm) and < 0.5 dB for a fabrication resolution of 100 nm. Subwavelength Y–junctions with deterministically induced errors of ± 10 nm further demonstrated robust fabrication tolerances. Moreover, the splitter exhibits excess loss lower than 1 dB for the first-order transverse-electric mode (TE1) within a 100 nm bandwidth (1475 nm – 1575 nm), using high-resolution lithography.
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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 | 0.001 | 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".