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Control system monitoring and diagnosis are important to ensure that plant performance is close to optimal and that process variable data used in control algorithms is accurate. We are researching performance monitoring of feedback control algorithms for linear processes, and have developed methods that can handle single loop PID, model predictive control and multiloop control with or without constraints.We are also investigating the monitoring of process and sensor faults when variations in duration of batch steps occur. Both data-driven and physical models are being employed. Applications to semiconductor manufacturing and bioreactors are being studied.
Microelectronics manufacturing is an area where process modeling and control are receiving increased attention. We are carrying out a number of projects in cooperation with semiconductor companies. In semiconductor fabs run-to-run behavior can be influenced by reactor aging, first wafer effects, and other non-uniform processing conditions. In one project we are modeling the run-to-run behavior of these processes and utilize that information for improved control. To deal with multiple product/multiple tool control, sequential parameter estimation techniques are used to update the models and perform model-based control, with application to “high-mix” fabs with more than 20 products. Optimal sampling strategies are also being investigated.
Control system monitoring and diagnosis are important to ensure that plant performance is close to optimal and that process variable data used in control algorithms is accurate. We are researching performance monitoring of feedback control algorithms for linear processes, and have developed methods that can handle single loop PID, model predictive control and multiloop control with or without constraints.We are also investigating the monitoring of process and sensor faults when variations in duration of batch steps occur. Both data-driven and physical models are being employed. Applications to semiconductor manufacturing and bioreactors are being studied.
Microelectronics manufacturing is an area where process modeling and control are receiving increased attention. We are carrying out a number of projects in cooperation with semiconductor companies. In semiconductor fabs run-to-run behavior can be influenced by reactor aging, first wafer effects, and other non-uniform processing conditions. In one project we are modeling the run-to-run behavior of these processes and utilize that information for improved control. To deal with multiple product/multiple tool control, sequential parameter estimation techniques are used to update the models and perform model-based control, with application to “high-mix” fabs with more than 20 products. Optimal sampling strategies are also being investigated.
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COMPUTERS & CHEMICAL ENGINEERING (2024): 108459-108459
ACS SUSTAINABLE CHEMISTRY & ENGINEERINGno. 2 (2023): 706-715
Kyeongjun Seo, Adhika P. Retnanto,Jorge L. Martorell,Thomas F. Edgar,Mark A. Stadtherr,Michael Baldea
COMPUTERS & CHEMICAL ENGINEERING (2023): 108344-108344
CURRENT OPINION IN CHEMICAL ENGINEERING (2023): 100978-100978
Computer Aided Chemical Engineering 14th International Symposium on Process Systems Engineeringpp.157-162, (2022)
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