Effect of lithography on SOI, grating-based devices for sensor and telecommunications applications
Bibliographic record
Abstract
We demonstrate how lithography smoothing and proximity effects affect the performance of silicon-on-insulator devices that include grating-based, contra-directional couplers (contra-DCs). Using lithography models developed for CMOS-compatible, deep ultraviolet lithography processes, we predict and verify the spectral responses of fabricated contra-DC test structures. These verified models are then used to simulate and analyze the effects of lithography on the performance of a microring resonator with an integrated contra-DC, as regards the device 3-dB bandwidth (BW), insertion loss (IL), and adjacent side mode suppression ratio (SMSR). We demonstrate how the corrugation profile of the contra-DC is affected by smoothing and the inner and outer corrugation depths are reduced due to proximity effects. We show that, if the effects of lithography are not taken in account during device design flow, large discrepancies result between the predicted "as-fabricated" and "as-designed" device performance. Specifically, we demonstrate how the BW is reduced from 47 GHz to 21 GHz, how the IL is increased from 0.5 dB to 5.8 dB, and how the adjacent SMSR is reduced to 26 dB. We also establish that it is possible to use the lithography models to compensate for lithographic effects during device design flow and layout and to design a contra-DC in which the as-fabricated device performance metrics matches the target/expected as-designed performance metrics.
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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 | 0.001 |
| 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".