Measurement methods for curvilinear patterns and application in lithography process development for silicon photonics
Bibliographic record
Abstract
In contrast to traditional logic devices, silicon photonics devices are characterized by a large number of curvilinear patterns. Conventional critical dimension scanning electron microscopes (CD-SEM) are generally only capable of measuring critical dimension (CD), line edge roughness (LER), and line width roughness (LWR) of Manhattan-type structures. When measuring curvilinear patterns on masks and wafers, these tools typically treat them as linear features, resulting in significant measurement inaccuracies that adversely affect lithography process performance. To address this issue, this paper proposes a CD measurement algorithm based on edge extraction. The method extracts the contours of curvilinear patterns from CDSEM images and outputs CD, LER, LWR, and power spectral density (PSD) curves. Due to the high sensitivity of silicon photonic devices to LER, the proposed method is employed to compare the LER performance of curvilinear masks fabricated by two different processes with the single-beam writer tool. The experimental results demonstrate that when the mask exposure shot size is reduced to a specific value, high-frequency fluctuations along the mask pattern edges are not transferred to the wafer. Therefore, without compromising wafer pattern quality, the exposure shot size during mask fabrication can be appropriately reduced, thereby shortening mask writing time, and lowering manufacturing costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".