Design of a 33:67 beam splitter via inverse optimization on a symmetric SOI platform
Bibliographic record
Abstract
Beam splitters (BSs) on a silicon-on-insulator (SOI) platform are essential for implementing quantum logic gates in quantum information processing (QIP). In particular, 50:50 and 33:67 BSs are used in constructing Hadamard and CNOT gates. Achieving compact, low-loss asymmetric BSs with high fabrication tolerance remains a challenge. We propose an optimized asymmetric BS with a 33:67 splitting ratio using particle swarm optimization (PSO) combined with full-wave electromagnetic simulation. The structure includes parallel silicon waveguides connected by bent sections and a central inverse-designed region. The silicon waveguides, with a width of 500 nm and height of 220 nm, are embedded in silicon dioxide cladding for strong optical confinement. Simulations confirm that the design achieves a target 1:2 splitting ratio with minimal insertion loss. The overall layout is compact, symmetric, and robust to fabrication errors, making it suitable for integration in quantum photonic circuits. The design operates in the C-band and supports smooth mode transitions with negligible reflection. The proposed approach can be extended to design other asymmetric beam splitters with custom ratios and broadband operation.
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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 | 0.000 |
| 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 | 0.001 |
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".