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 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 | 0.001 |
| 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".