Hybrid cross correlation and line-scan alignment strategy for CMOS chips electron-beam lithography processing
Bibliographic record
Abstract
In this paper, we show an alignment strategy based on a hybrid strategy using cross correlation and line-scan alignment to address the challenge for CMOS integrated circuit postprocessing using electron-beam lithography. Due to design rules imposed by the foundries at the 130 nm node and below, classical line-scan alignment is not possible, and marker shapes are limited. The shape of the marker is essential for cross-correlation alignment. By measuring accurately the alignment offset between two lithography steps with different marker shapes compatible with the design rules, we tested the influence of the marker shape in the performance of the cross-correlation alignment. We present a method based on a white noise generated array to design high-performance markers for cross correlation, compatible with CMOS technology, by increasing the sharpness of their autocorrelation peak. We show that the alignment performances can even be improved using a hybrid strategy with cross-correlation and line-scan alignment and reaches a mean offset of 5.2 nm on a CMOS substrate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".