Litho-Friendly Decomposition Method for Self-Aligned Triple Patterning
Bibliographic record
Abstract
Multiple patterning lithography is the most likely manufacturing process for sub-32 nm technology nodes. Among different multiple patterning methods, self-aligned patterning has attracted much interest due to its robustness against overlay errors. However, self-aligned patterning compliance is subject to the litho-friendliness of the applied decomposition method. This brief establishes self-aligned triple patterning (SATP) decomposition requirements and proposes a litho-friendly layout decomposition method. First, the major SATP litho-friendliness requirements are explained. In-silico experiments on SATP process indicate that layout features printed by the structural spacers are the most accurate ones. Therefore, we propose an ILP-based decomposition which avoids decomposition conflicts and maximizes the use of structural spacers simultaneously. Experiments reveal that the proposed method improves overlay robustness and line-edge roughness of the attempted test cases.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".