Integrated Maintenance Scheduling for Semiconductor Manufacturing

Davenport, Andrew

doi:10.1007/978-3-642-13520-0_12

Cited by 13 publications

(2 citation statements)

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“…We have developed a novel integrated approach, termed MSS, in which simulation is used to estimate the expected WIP in the fab. The schedule generation is carried out using a variety of techniques that include goal programming, constraint programming and mixed-integer programming (the detailed technical aspects of the MSS formulation are described in [18]).…”

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confidence: 99%

“…A similar approach is used to determine the best value of the objective for the separation constraints and the follow-up maintenance constraints. The objectives for production disruption and earliness-tardiness costs are solved using mixed-integer programming, for which we use a time-indexed formulation with some additional cuts as described in [18]. A single toolset may have up to 100 maintenance operations to be scheduled over a two week time horizon.…”

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Data analytics and stochastic modeling in a semiconductor fab

Bagchi

Baseman

Davenport

et al. 2010

Appl Stoch Models Bus & Ind

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The scale, scope and complexity of the manufacturing operations in a semiconductor fab lead to some unique challenges in ensuring product quality and production efficiency. We describe the use of various analytical techniques, based on data mining, process trace data analysis, stochastic simulation and production optimization, to address these manufacturing issues, motivated by the following two objectives. The first objective is to identify the sub-optimal process conditions or tool settings that potentially affect the process performance and product quality. The second objective is to improve the overall production efficiency through better planning and resource scheduling, in an environment where the product mix and process flow requirements are complex and constantly changing. Figure 5. Method for rule extraction and generalization ( denotes the empty rule; {Nodei} denotes the condition at the Nodei; the AND operator adds conditions to the rule).Figure 6. TDVs may reflect instabilities in various ways including gradual drifts and abrupt transitions.presumed tool stability issues. Alternatively, if the underlying analytics are designed to reveal TDV mismatches, then the corresponding heat map will highlight presumed tool matching issues. Assuming the effectiveness of the analytics layer, the TRACER chamber/process report then enables the supervising engineer to rapidly review thousands of underlying TDVs in a single view, in which the operationally significant signals are clearly highlighted. By clicking through the cells in the heat map, the associated detail reports for the relevant TDVs can be accessed, as illustrated in Figure 9.Although, as noted above, the TRACER framework typically uses only univariate analysis (since each TDV is analyzed independently), the heat-map representation implicitly reveals some of the data interactions. For example, if the detected problem is related to a particular process step, the

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confidence: 99%

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confidence: 99%