Focus characterization using end of line metrology
Bibliographic record
Abstract
A new method is introduced to measure relative focus using conventional optical overlay instruments. Optical end of line metrology (OELM), is based on patterning a wide frame in which adjacent sides are constructed of submicron sized lines that run perpendicular to the center opening. Because truncation is size dependent, line and space features exhibit significantly more line shortening effects than the solid sections. When measured with a conventional optical overlay tool, the difference in line shortening between the solid and line and space sections are measured as an alignment offset, which is a relative measure of actual line shortening. Reproducibility for measuring the apparent misalignment of these modified box-in-box structures was 3 nm (3/spl sigma//sup est/), which is similar to the repeatability for measuring conventional resist box in box alignment boxes. Truncation is sensitive to focus and the utility for using OELM toward characterizing focus-dependent lens parameters was investigated. Estimated 3/spl sigma/ for calculating best focus and astigmatism were 0.04 /spl mu/m and 0.01 /spl mu/m, respectively. Focus corrections were accurate to 0.05 /spl mu/m within /spl plusmn/0.3 /spl mu/m of best focus. Additionally, a general method is presented to estimate the error in the calculated best focus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".