An Optimal Residency-Aware Scheduling Technique for Cluster Tools With Buffer Module
Bibliographic record
Abstract
Cluster tools provide a flexible, reconfigurable, and efficient environment for several manufacturing processes (e.g., semiconductor manufacturing). A new timing constraint (distinct from a simple deadline), referred to as residency constraint, puts a timing limit on the time that a wafer can stay in a processing module in a cluster tool. The authors demonstrate that a solution that does not address residency constraints can be found easily. However, when residency constraints are added to the model, the problem becomes complex and a scheduling technique may spend a long time searching for a good solution. Also, in some cases, one may need to decrease throughput to satisfy residency constraints. The authors introduce a new technique to address cluster tool scheduling in the presence of residency constraints. The proposed technique uses a buffer resource for temporarily holding wafers to release other resources such as the robot arm. This resource is usually available in the tool for maintenance reasons. A tradeoff is discussed in using the buffer resource and a scheduling algorithm is presented that will use this resource when it can help to increase throughput under residency constraints. The experiments show that in many cases that are common in semiconductor manufacturing, use of their proposed technique can improve throughput.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".