Cluster based analytical method for the lot delivery forecast in semiconductor fab with wide product range

Mosinski,; Noack,; Pappert,; Rose, Martin; Scholl,

doi:10.1109/wsc.2011.6147897

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Another similar research work was done by Mosinski et al [10]. In this research, the authors focused on the daily delivery predictions and a bottleneck early warning system for several machine groups in the Fab of their project partner.…”

Section: Literature Reviewmentioning

confidence: 92%

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Tin

Chiew

Phang

et al. 2019

Computational Intelligence and Neuroscience

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Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.

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Section: Literature Reviewmentioning

confidence: 92%

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Tin

Chiew

Phang

et al. 2019

Computational Intelligence and Neuroscience

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“…Completely based on statistics extracted from historical lot data traces, Mosinski et al (2011) forecasted delivery times of wafers. In this work, the time estimation is based on a statistical evaluation of the duration between start and target operations from historical data.…”

Section: Statistic Approachesmentioning

confidence: 99%

Event-based recognition and source identification of transient tailbacks in manufacturing plants

Schwenke

Wagner

Gellrich

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Automated material handling systems in complex manufacturing plants oftentimes exhibit, possibly transient, tailback phenomena, which reduce a production line's throughput. In order to identify origins and causes of observed tailbacks, historic event log data of loads passing certain waypoints has to be inspected. This paper introduces an approach to automatically carry out transient tailback recognition and cause identification. The approach is based on analysis of holding times and capacities of transport segments. As a result, complete lists of tailbacks and affected segments are provided. Each tailback consists of a reconstructed queue of loads waiting for preceding loads. For each tailback an initial cause event is determined. Additionally, identified tailbacks can be ranked by length or by impact on the transport delays. The developed demonstrator frees the user from time consuming visual inspection of log files by providing clearly represented complete tailback information instead.

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Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network

Gallina

Lingitz

Breitschopf

et al. 2021

Procedia Manufacturing

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Cluster based analytical method for the lot delivery forecast in semiconductor fab with wide product range

Cited by 3 publications

References 4 publications

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Event-based recognition and source identification of transient tailbacks in manufacturing plants

Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network

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