Bibliographic record
Abstract
Mask layout design is an important part in silicon photonic device design flow; the space used and the quality of the mask directly affect the cost of fabrication and quality of the outcome. To effectively minimize time spent on drawing masks, fixing design violations, and reducing unused spacings between each structure, we use effective approaches in the mask design process to ensure the listed criteria are met. Using the PCell and the hierarchy drawing methods, GDS files that contain different device parameters can be generated efficiently. As a result, direct GDS modeling efficiency is improved. An experimental setup that is capable of obtaining high quality measurement data is critical to device measurement. The concept of an automated measurement station can effectively reduce work needed from the experimenter while providing quality results. With the implemented fiber-to-fiber and fiber array automated measurement station, multi-device measurement can be set up to run automatically in minutes whereas traditional manual measurement stations require one's presence and constant attention. In this thesis, we have illustrated several mask drawing approaches and showed the drawing steps of two masks in detail. We have described two automated experimental setups, fiber-to-fiber and fiber array, in detail and included various measurement results to show the capabilities of these two stations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".