Stitching error reduction in electron beam lithography with <i>in-situ</i> feedback using self-developing resist
Bibliographic record
Abstract
In electron beam lithography (EBL), a large area pattern is divided into smaller writing fields, which are then stitched together by stage movement to generate the large area pattern. Precise stage movement is essential to minimize the stitching error, and this can be achieved by using laser interferometer-controlled stage. In addition, electron beam deflection must be adjusted to match the stage movement, which is referred to as “writing field alignment.” To expose large area nanostructures, a large writing field must be used; otherwise, the stage movement time would be impractically long. However, writing field alignment accuracy decreases with a larger writing field owing to its low magnification. Here, the authors report that self-developing resist (for which the pattern shows up immediately after exposure, thus eliminating the need for ex-situ development) can provide in-situ feedback for writing field alignment accuracy, which in turn can be used to optimize the alignment. After several iterations using the exposed test pattern in nitrocellulose (self-developing) resist as feedback, the authors reproducibly achieved nearly perfect (<50 nm stitching error) alignment with writing field of 1 mm2 using a Raith EBL system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".