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Record W1887541559 · doi:10.1109/icsmc.2001.972958

An optimal scheduling technique for dual-arm cluster tools with buffer modules

2002· article· en· W1887541559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBuffer (optical fiber)Computer scienceScheduling (production processes)ScheduleCluster (spacecraft)WaferThroughputProcess (computing)Reliability engineeringDistributed computingEmbedded systemComputer networkOperating systemEngineeringElectrical engineeringOperations management

Abstract

fetched live from OpenAlex

In a cluster tool where process modules have residency constraints, an optimal schedule may need to buffer the partially processed wafers. In previous approaches, one of the active resources (i.e. the transport module) was used as a temporary buffer. In this approach we try to use a resource that is usually available in cluster tools and is only used for tool maintenance reasons. This passive module that we refer to as buffer module, can hold the partially processed wafers, and free the transport module (TM). Our experiment shows that in 57% of the cases, because the TM can perform other actions while the wafer is buffered in the buffer module, a schedule with better throughput was found.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.249
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.233
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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