Silicon Nanophotonics Fabrication: An innovative graduate course
Bibliographic record
Abstract
Abstract—We report our recent successful experiences related to the development of a transcontinental course in silicon photon-ics offered by the University of British Columbia in collaboration with CMC Microsystems. The course is offered to students from across Canada and has attracted participants from nearly every Canadian university with an advanced photonics research program. The focus of the course is the rapidly developing field of silicon photonics. Its aim is to provide the students with a breadth of competencies in designing optical circuits and systems using a silicon-on-insulator platform. The students taking the course gain familiarity in design, fabrication, and testing in an area of photonics that is destined to play an increasingly important, and in the long run ubiquitous, role in optical circuitry, impacting on areas such as optical interconnects, communications systems, and sensor systems. The course is structured using a blended-learning pedagogical approach consisting of an on-site workshop followed by design-based e-learning. The students ’ designs are fabricated using IMEC’s passive photonic cSOI process, which is accessed through the European silicon photonics prototyping service ePIXfab. Student projects to date have included the design of integrated-optical circuits using combinations of ring resonators, waveg-uides, couplers, and photonic crystals for applications such as filters for WDM optical interconnects, demodulators for phase modulated signals, and lab-on-chip sensors. I.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".