A simulated annealing technique for multi-route cluster tools
Bibliographic record
Abstract
We provide scheduling techniques to enable cluster tools to produce different kinds of wafers at the same time. In this model, the multi-route model, wafers can visit different processing modules in their path. Some of these processing modules may have a limit on how long they allow the wafer to stay after the process is finished. If none of the modules have this timing constraint, we provide a greedy algorithm to schedule the multi-route cluster tool. However, if some modules have a timing constraint, the scheduling problem becomes more complicated, and an exhaustive search in a very large search space must be performed to find the optimal schedule. The exhaustive search may take as long as an hour to come up with the answer, and is not practical. We provide a simulated annealing technique to find a near-optimal schedule. To evaluate each state in the simulated annealing we need to solve a linear programming system. Instead of solving that LP system with conventional methods, we provide a much faster method. This method that uses shortest path algorithm and binary search improves the performance of the simulated annealing significantly. Our experiments showed that we can find a near-optimal solution in less than 2 minutes with this method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".