Emulation of deep-ultraviolet lithography using rapid-prototyping, electron-beam lithography for silicon photonics design
Bibliographic record
Abstract
We demonstrate a method to emulate the optical performance of silicon photonic devices fabricated using advanced deep-ultraviolet lithography (DUV) processes on a rapid-prototyping electron-beam lithography process. The method is enabled by a computational lithography predictive model generated by processing SEM image data of the DUV lithography process. We experimentally demonstrate the emulation method's accuracy on integrated silicon Bragg grating waveguides and grating-based, add-drop filter devices, two devices that are particularly susceptible to DUV lithography effects. The emulation method allows silicon photonic device and system designers to experimentally observe the effects of DUV lithography on device performance in a low-cost, rapid-prototyping, electron-beam lithography process to enable a first-time-right design flow.
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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 | 0.001 |
| 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".