Thermo-optic-enabled topological optimization for tunable ultra-compact silicon photonic device design
Bibliographic record
Abstract
Incorporating the thermal dependency of silicon permittivity into topological inverse design methodologies opens the design space for generating novel compact optical devices with temperature actuation for dense photonic integrated circuitry. In this work, a thermo-optic-enabled topological optimization methodology for integrated photonic device design is proposed and experimentally validated through an ultra-compact optical switch. With a footprint of 10 µm by 6~µm, a tenth of the size of conventionally designed MZI-based switches, the device operates in the C-band region with comparable insertion loss (<2 dB) and ON/OFF extinction ratio (<14 dB) performance. At intermediate temperatures, the device operates as a tunable power splitter with an insertion loss below 2 dB over the wavelength range from 1532 to 1555 nm. The tunable splitter achieves various splitting ratios from 0:100 to 100:0 at 1543 nm with a 50:50 splitting ratio that can be maintained over a wavelength range of 23 nm with temperature changes.
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 | 0.001 | 0.001 |
| 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".