Control Limits for Multi-Stage Manufacturing Processes with Binomial Yield (Single and Multiple Production Runs)

Barad, Miryam; Braha, Daniel

doi:10.2307/2584255

Cited by 2 publications

(3 citation statements)

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“…The Production Planning and Control department at the case study company is interested in predicting accurately the expected yield and flow time of every batch in the manufacturing process as early as possible. Since the direct cost of each fabrication step is nearly fixed, only batches with a high yield should continue their production, while batches expected to have a low yield should be stopped and scrapped immediately as being non-profitable (Barad andBraha 1996, Braha 1999). The batch flow time also affects profitability -slow batches may lose their designated customers, and thus turn into 'dead' inventory.…”

Section: Semiconductor Production Control Problemmentioning

confidence: 99%

“…Here, 'yield' is defined as the ratio between the number of integrated circuits (chips) that emanate from the production process and pass quality control tests and the maximal theoretical number of integrated circuits that can be produced from the same number of wafers. Accurate estimation of the yield per batch is an important parameter that affects control and production planning (Barad andBraha 1996, Braha 1999). Imprecise estimation of the yield may result in delayed product delivery, or inventory costs due to excess manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Theory of actionable data mining with application to semiconductor manufacturing control

Braha

Elovici

2007

International Journal of Production Research

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Accurate and timely prediction of a manufacturing process yield and flow times is often desired as a means of reducing overall production costs. To this end, this paper develops a new decision-theoretic classification framework and applies it to a real-world semiconductor wafer manufacturing line that suffers from constant variations in the characteristics of the chip-manufacturing process. The decisiontheoretic framework is based on a model for evaluating classifiers in terms of their value in decision-making. Recognizing that in many practical applications the values of the class probabilities as well as payoffs are neither static nor known exactly, a precise condition under which one classifier 'dominates' another classifier (i.e. achieves higher payoff ), regardless of payoff or class distribution information, is presented. Building on the decision-theoretic model, two robust ensemble classification methods are proposed that construct composite classifiers that are at least as good as any of the existing component classifiers for all possible payoff functions and class distributions. It is shown how these two robust ensemble classifiers are put into practice by developing decision rules for effectively monitoring and controlling the real-world semiconductor wafer fabrication line under study.

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Section: Semiconductor Production Control Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Theory of actionable data mining with application to semiconductor manufacturing control

Braha

Elovici

2007

International Journal of Production Research

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“…Several authors (e.g. [1][2][3]) consider multi-attribute stochastic yield models with product flow decisions (e.g. accept, reprocess, scrap).…”

Section: Introductionmentioning

confidence: 99%

Imperfect inspection of a multi‐attribute deteriorating production system—a continuous time model

Rabinowitz

Yahalom

2001

Quality & Reliability Eng

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SUMMARYThe reliability of a multi-attribute deteriorating production system is controlled using versatile identical inspection facilities. An attribute state is dichotomous (up designates proper function versus down). A product item is conforming if all the system attributes are up when it is produced. When a system attribute is detected as down it is restored back to an up state. Inspection of an attribute can rely on observations of the system, recently produced items, or both. Inspection policy determines the inspection capacity, frequency of inspecting each attribute and inspection schedule. These decisions involve a tradeoff between the cost of inspectors and the loss associated with the proportion of non-conforming items due to lack of adequate inspection. Three models are introduced, analyzed and solved. In the first model, inspection and restoration are perfect, product attribute is up (down) when the system attribute is up (down), and restoration is immediate. The assumptions of perfect inspection and restoration are relaxed in the second model. The third model relaxes in addition the assumption of immediate restoration. An efficient heuristic solution scheme is provided for solving these models. Sensitivity of the solution to system parameters is studied. Numerical experiments provide some insights regarding the combined effect of imperfect production, inspection and restoration, in various conditions of inspection and restoration durations.

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Control Limits for Multi-Stage Manufacturing Processes with Binomial Yield (Single and Multiple Production Runs)

Cited by 2 publications

References 0 publications

Theory of actionable data mining with application to semiconductor manufacturing control

Theory of actionable data mining with application to semiconductor manufacturing control

Imperfect inspection of a multi‐attribute deteriorating production system—a continuous time model

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