Realizing "value-added" metrology
Bibliographic record
Abstract
The conventional premise that metrology is a "non-value-added necessary evil" is a misleading and dangerous assertion, which must be viewed as obsolete thinking. Many metrology applications are key enablers to traditionally labeled "value-added" processing steps in lithography and etch, such that they can be considered integral parts of the processes. Various key trends in modern, state-of-the-art processing such as optical proximity correction (OPC), design for manufacturability (DFM), and advanced process control (APC) are based, at their hearts, on the assumption of fine-tuned metrology, in terms of uncertainty and accuracy. These trends are vehicles where metrology thus has large opportunities to create value through the engineering of tight and targetable process distributions. Such distributions make possible predictability in speed-sorts and in other parameters, which results in high-end product. Additionally, significant reliance has also been placed on defect metrology to predict, improve, and reduce yield variability. The necessary quality metrology is strongly influenced by not only the choice of equipment, but also the quality application of these tools in a production environment. The ultimate value added by metrology is a result of quality tools run by a quality metrology team using quality practices. This paper will explore the relationships among present and future trends and challenges in metrology, including equipment, key applications, and metrology deployment in the manufacturing flow. Of key importance are metrology personnel, with their expertise, practices, and metrics in achieving and maintaining the required level of metrology performance, including where precision, matching, and accuracy fit into these considerations. The value of metrology will be demonstrated to have shifted to "key enabler of large revenues," debunking the out-of-date premise that metrology is "non-value-added." Examples used will be from critical dimension (CD) metrology, overlay, films, and defect metrology.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".