Optimization of preventive maintenance scheduling for semiconductor manufacturing systems: models and implementation

In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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Integrated Maintenance Decision-Making and Product Sequencing in Flexible Manufacturing Systems

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4030301 ◽

2015 ◽

Vol 137 (4) ◽

Cited By ~ 11

Author(s):

Merve Celen ◽

Dragan Djurdjanovic

Keyword(s):

Decision Making ◽

Flexible Manufacturing ◽

Semiconductor Manufacturing ◽

Manufacturing Systems ◽

Discrete Event ◽

Maintenance Scheduling ◽

Dynamic Interactions ◽

Discrete Event Simulations ◽

Degradation Models ◽

Event Simulations

In highly flexible and integrated manufacturing systems, such as semiconductor manufacturing, the strong dynamic interactions between the equipment condition, operations executed on the equipment, and the resulting product quality necessitate a methodology that integrates the decision-making process across the domains of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper, we devise an integrated decision-making policy for maintenance scheduling and production sequencing, with the objective of optimizing a customizable objective function, while taking into account operation-dependent degradation models and a production target. Optimization was achieved using a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently and often significantly outperforms the current practice of making these decisions separately.

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A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Global Journal of Computer Science and Technology ◽

10.34257/gjcstgvol20is3pg13 ◽

2020 ◽

pp. 13-29

Author(s):

Da-Yin Liao

Keyword(s):

Semiconductor Manufacturing ◽

Manufacturing Systems ◽

Edge Computing ◽

Legacy Systems ◽

Software Framework ◽

Computer Integration ◽

Computing Architecture ◽

Disruptive Technologies ◽

Distributed Ledger ◽

Operational Structure

Contemporary 300mm semiconductor manufacturing systems have highly automated and digitalized cyber-physical integration. They suffer from the profound problems of integrating large, centralized legacy systems with small islands of automation. With the recent advances in disruptive technologies, semiconductor manufacturing has faced dramatic pressures to reengineer its automation and computer integrated systems. This paper proposes a Distributed-Ledger, Edge-Computing Architecture (DLECA) for automation and computer integration in semiconductor manufacturing. Based on distributed ledger and edge computing technologies, DLECA establishes a decentralized software framework where manufacturing data are stored in distributed ledgers and processed locally by executing smart contracts at the edge nodes. We adopt an important topic of automation and computer integration for semiconductor research &development (R&D) operations as the study vehicle to illustrate the operational structure and functionality, applications, and feasibility of the proposed DLECA software framework.

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