Short, broadband, and polarization-insensitive adiabatic Y-junction power splitters
Bibliographic record
Abstract
Adiabatic Y-junction power splitters have low loss, large bandwidth, high polarization insensitivity, and high tolerance to fabrication errors. However, the adiabatic transition lengths required are generally much longer than other power splitters. Using a nonlinear taper profile can considerably shorten the device length. Here, we introduce a taper profile optimization algorithm based on polynomial functions, which significantly reduces the lengths of the adiabatic power splitters without increasing losses. We experimentally demonstrate the performance of the adiabatic power splitters for minimum feature sizes of 80 nm, 120 nm, and 160 nm on the 220 nm silicon-on-insulator (SOI) platform. Our best device has a minimum feature size of 120 nm and a length of 14 µm, with measured losses of 0.25 dB and 0.23 dB for the transverse electric (TE) and transverse magnetic (TM) modes, respectively, in the 1500-1600 nm region. This device has an average transmission of -3 ± 0.5 dB in the 1500-1600 nm region, indicating highly balanced splitting over a large spectral range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".