Systematic Construction and Performance Analysis of Cluster Tools Using Timed Petri Net Models
Bibliographic record
Abstract
A cluster tool is an integrated, envi- \nronmentally isolated manufacturing system consisting \nof process, transport, and cassette modules, mechan- \nically linked together, that is used in manufacturing \nof semiconductor chips. Because of high throughput \nrequirements, cluster tools perform a number of activ- \nities concurrently. Petri nets are formal models devel- \noped specifically for representation of concurrent ac- \ntivities and for their coordination. In timed nets, the \ndurations of modeled activities are represented by oc- \ncurrence times associated with transitions, and this \nallows to study the performance characteristics of the \nmodeled systems. \nSince cluster tools can be quite complex, a system- \natic approach to generating net models is proposed. \nNet models derived in such a way have modular struc- \nture, which is used to determine model’s steady–state \nperformance on the basis of net invariants, without the \nexhaustive reachability analysis. Performance charac- \nteristics are obtained in symbolic form, in terms of \nmodeling parameters, so different variants of cluster \ntools can be evaluated and compared very efficiently, \nwithout repetitive model analyses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".