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Georgia Tech's H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and Intel Corporation collaborated in developing an Interactive Supply Chain Game, which is a computerized simulation that mimics [...]

THE 2019 INFORMS CONFERENCE ON Business Analytics and Operations Research, recently named one of the top analytics conferences to attend this year by Solutions Review, will be held April 14-16 [...]

In the design of run-to-run controllers one is usually faced with the problem of selecting a model structure that best explains the variability in the data. The variable selection problem often becomes more complex when there are large numbers of candidate variables and the usual regression modeling assumptions are not satisfied. This paper proposes a model selection approach that uses ideas from the statistical linear models and stepwise regression literature to identify the context variables that contribute most to the autocorrelation and to the offsets in the data. A simulation example and an application to lithography alignment control are presented to illustrate the approach. Index Terms--Autoregressive-integrated-moving average (ARIMA) time series models, analysis of variance, context selection, run-to-run (R2R) control.

An estimate of cycle time for a product in a factory is critical to semiconductor manufacturers (and in other industries) to assess customer due dates, schedule resources and actions for anticipated job completions, and to monitor the operation. Historical data can be used to learn a predictive model for cycle time based on measured and calculated process metrics (such as work-in-progress at specific operations, lot priority, product type, and so forth). Such a method is relatively easy to develop and maintain. Modern data mining algorithms are used to develop nonlinear predictors applicable to the majority of process lots, and three methods are compared here. They are compared with respect to performance in actual manufacturing data (to predict times for both final and intermediate steps) and for the feasibility to maintain and rebuild the model. Index Terms--Due date, scheduling, statistical models, work-in-progress (WIP).

Intel factories are being challenged by an increasingly diverse product and technology mix. Semiconductor manufacturers have successfully leveraged advanced process control (APC) for overlay and critical dimension (CD) in the lithography module. Trends are continuously corrected through in-line metrology feedback, keeping the process on target. Applying an APC system suitable for litho control in high-volume manufacturing has resulted in significant improvement in registration capability and potential for better exposure focus and CD control. Index Terms--Advanced process control (APC), critical dimension (CD), exposure focus CD control, overlay, run-to-run control, scanner registration control.

We investigate multivariate process adjustment policies in the presence of fixed adjustment costs. A fixed adjustment cost leads to a trade-off between adjusting a process continuously and allowing the process to drift until a bound is violated. The problem of determining the best bounded adjustment policy and the optimal bounded adjustment parameter is addressed. Although the univariate bounded adjustment problem has received interest in the past, there has been little focus on the multivariate case in the literature. It has been shown that in the univariate case, the shape of the optimal adjustment policy is a deadband. Determining the shape of the optimal dead region and the parameter that defines this region in the multivariate case is a difficult problem. A State-Space model is formulated for the multivariate bounded process adjustment problem and a Kalman filter-based controller is used. With the help of simulation, the optimal bounded adjustment parameter is computed for two specific dead subspace shapes of practical applicability. An investigation of the performance of the simulation-optimization approach is included as the dimensionality of the observations increases. Validation of this approach with the only two analytic results available for deadbands (univariate and bivariate) confirms the accuracy of the optimal solutions found. An illustration in a semiconductor manufacturing process is presented and MATLAB code that implements the methods is developed and made available. [ABSTRACT FROM AUTHOR]

The 2006 INFORMS Combined Colloquia will be held Nov. 3-4, the Friday and Saturday immediately preceding the INFORMS Annual Meeting in Pittsburgh. This year, the Combined Colloquia will feature the […]