Probabilistic process window analysis for EUV OPC model validation in advanced DRAM technology
Bibliographic record
Abstract
Extreme Ultraviolet (EUV) lithography has rapidly advanced to become a cornerstone in enabling further semiconductor scaling in DRAM manufacturing. When the design feature continues to be shrunk, critical dimension (CD) control becomes more challenging. However, the dominant metrology tool for this is CD-SEM, which has significant problems when measuring stochastic effects, which have become the biggest source of variation in semiconductor manufacturing. Traditional CD-SEM measurements, while widely used, are susceptible to measurement noise, which can obscure the subtle nuances of stochastic- induced variations. Fractilia offers computational metrology using a physical based model which is more reliable and provided enhanced stability and precision in CD stochastic analysis. Moreover, its automatic probabilistic process windowing methodology allows users to add multiple features with dozens measurement specification for a more granular understanding of stochastic behaviors impacts in EUV lithography. In this article, we will explore the comparative advantages of Fractilia’s MetroLER in addressing the critical issue of stochastics in OPC model validation including dataset from optical model and resist model, and showing a pathway to improve metrology and process optimization in the DRAM EUV adoption.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".