Etch process optimization to improve line edge roughness using precise stochastic process metrology
Bibliographic record
Abstract
Optimizing etch processes for Self-Aligned Double Patterning (SADP) is complicated by the metrology challenge of keeping the two populations (core and gap) separate. If that metrology challenge can be solved, the goal is to optimize the SADP process to achieve 1 nm Line-Edge Roughness (LER) for both core and gap while maintaining acceptable CD balance between the two. MetroLER was used to automatically determine SADP populations without relying on CD-SEM stage precision, and to provide unbiased roughness measurements and other metrics of stochastics behavior. The resulting analysis found that line wiggling at the hardmask etch step was one of the problematic steps in the SADP patterning. After full optimization of the SADP process, LER for both core and gap was reduced as much as 25%, to 1.1 nm. Separation of SADP populations and advanced analysis of unbiased roughness, including edge-edge correlations and line/space wiggling, are critical to process improvements and reduction of the impact of stochastics on line/space patterning in advanced DRAM manufacturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".