A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future

Enhancing Scheduling Performance for a Wafer Fabrication Factory: The Biobjective Slack-Diversifying Nonlinear Fluctuation-Smoothing Rule

A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future

A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Designing a robust production system for erratic demand environments.

Production systems must have the right type of material in the right quantities when required for production. They must minimize the work in progress while ensuring no stock-outstock-out occurs. While these twin opposing goals are achievable when demand is stable, they are difficult to realize under an erratic demand pattern. This dissertation aims to develop a production system that can meet erratic demands with minimal costs or errors. After a detailed introduction to the problem considered, we review the relevant literature. We then conduct a numerical analysis of current production systems, identify their deficiencies, and then present our solution to address these deficiencies via the ARK (Automated Replenishment System) technique. This technique is applied to a real-world problem at Methode Engineering ©. We conclude by detailing the scientific benefit of our technique and proposing ideas for future research

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Aeronautical engineering: A continuing bibliography with indexes (supplement 254)

This bibliography lists 538 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Structuring postponement strategies in the supply chain by analytical modeling

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