Optimal Preventive Maintenance Scheduling in Semiconductor Manufacturing Systems: Software Tool and Simulation Case Studies

Ramírez-Hernández, J.A.; Crabtree, Jason Paul; Yao, Xiaodong; Fernandez, Emmanuel; Fu, Michael C.; Janakiram, Mani; Marcus, Solomon; O'Connor, Matilda; Patel, N

doi:10.1109/tsm.2010.2051731

Cited by 22 publications

(11 citation statements)

References 9 publications

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“…Furthermore, the duration between preventive maintenance tasks is reduced as the machine ages. To schedule PM, Ramírez-Hernandez et al [37] modelled a PM window constituting of warning date which is the earliest time a PM can be conducted, due date which is the suggested date for PM and late date which is the latest time to conduct PM.…”

Section: Maintenance Strategies and Policiesmentioning

confidence: 99%

State of the art in simulation-based optimisation for maintenance systems

Alrabghi

Tiwari

2015

Computers & Industrial Engineering

181

110

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Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on Preventive Maintenance and optimising Preventive Maintenance frequency that will lead to the minimum cost. Discrete Event Simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and Condition Based Maintenance especially in a production context.

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Section: Maintenance Strategies and Policiesmentioning

confidence: 99%

State of the art in simulation-based optimisation for maintenance systems

Alrabghi

Tiwari

2015

Computers & Industrial Engineering

181

110

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“…On the basis of the PM scheduling optimization algorithm suggested by Yao et al [15], Ramírez-Hernández et al [10] developed software called the preventive maintenance optimization software tool (PMOST) and conducted four complex simulation case studies, based on real industrial data from two separate fabs. PMOST receives as data inputs a set of tools, an initial PM schedule, a scheduling horizon, projected incoming WIP, cost parameters and available resources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…PMOST receives as data inputs a set of tools, an initial PM schedule, a scheduling horizon, projected incoming WIP, cost parameters and available resources. In this paper, we use the same input parameters and data used by the software designed by Ramírez-Hernández et al [10], but our objective function and set of constraints are different. Our proposed model, presented in the next section, is focused on planning a production and PM schedule for an upcoming 12-hour shift in a fab, with the objective of minimizing the cost associated with a given shift.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, if the tool's age reaches a given limit, it is taken down, and a pre-established maintenance procedure is performed (during this period the tool is unavailable for production). In semiconductor manufacturing, PM is not necessarily carried out exactly at the age recommended by the manufacturer; rather, it is customary to identify acceptable time intervals, or "time windows", in which PM can be performed (see [10], [15]). To accommodate this flexibility, the model proposed herein may consider "overtime" for a tool, implying that it works beyond the age recommended by the manufacturer but not beyond the time window.…”

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confidence: 99%

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Production Planning and WIP Assignment for Wafer Fabrication Tools With Availability Constraints

Perlman‬‏

Crispil

Kalir

2015

IEEE Trans. Semicond. Manufact.

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This paper deals with a short-term production plan of a single toolset during a shift at a semiconductor fabrication plant. We propose a cost-based optimization model that seeks to minimize the cost of the shift while meeting the following conditions: 1) the shift produces its required output, measured in work-in-process (WIP) levels; 2) preventive maintenance (PM) tasks are carried out in compliance with the manufacturer's recommended PM policy; and 3) the equipment undergoes cleaning operations, as recommended by the manufacturer, in a way that minimizes the production of blank wafers (a by-product of the cleaning process). A linear programming model is formulated and a practical procedure is provided to solve this production plan problem. Data from a real fab are used to demonstrate the optimality and applicability of the proposed approach. Results of various sensitivity analyzes further demonstrate how the model can be used to analyze the impact of changes in cost parameters, WIP levels and production rate parameters on the optimal solution.

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“…In semiconductor manufacturing, preventive maintenance (PM) is an activity that takes the entire tool offline to carry out prescribed maintenance activity in order to maintain or increase the operational efficiency and reliability of the tool and minimizes unanticipated failures due to faulty parts [1]. However, PM downtime can be costly because it takes significantly long hours.…”

Section: Introductionmentioning

confidence: 99%

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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Optimal Preventive Maintenance Scheduling in Semiconductor Manufacturing Systems: Software Tool and Simulation Case Studies

Cited by 22 publications

References 9 publications

State of the art in simulation-based optimisation for maintenance systems

State of the art in simulation-based optimisation for maintenance systems

Production Planning and WIP Assignment for Wafer Fabrication Tools With Availability Constraints

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

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