The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Daniel E Rivera - One of the best experts on this subject based on the ideXlab platform.
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Model predictive control for tactical decision-making in Semiconductor Manufacturing supply chain management
IEEE Transactions on Control Systems Technology, 2008Co-Authors: Wenlin Wang, Daniel E RiveraAbstract:Supply chain management (SCM) in Semiconductor Manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of Semiconductor Manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for Semiconductor Manufacturing and similar forms of high-volume discrete-parts Manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
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model predictive control strategies for supply chain management in Semiconductor Manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in Semiconductor Manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a tactical decision policy that is an integral part of a comprehensive hierarchical decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in Semiconductor Manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of Semiconductor Manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing Semiconductor Manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a tactical decision module ispresented for supply chain management in the Semiconductor Manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These decisions areusually weekly or monthly. The MPC-based tactical decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily decisions on starts at the variousManufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor Manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of Semiconductor Manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of Manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in Semiconductor Manufacturing is demonstrated in each case study.
Wenlin Wang - One of the best experts on this subject based on the ideXlab platform.
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Model predictive control for tactical decision-making in Semiconductor Manufacturing supply chain management
IEEE Transactions on Control Systems Technology, 2008Co-Authors: Wenlin Wang, Daniel E RiveraAbstract:Supply chain management (SCM) in Semiconductor Manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of Semiconductor Manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for Semiconductor Manufacturing and similar forms of high-volume discrete-parts Manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
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model predictive control strategies for supply chain management in Semiconductor Manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in Semiconductor Manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a tactical decision policy that is an integral part of a comprehensive hierarchical decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in Semiconductor Manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of Semiconductor Manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing Semiconductor Manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a tactical decision module ispresented for supply chain management in the Semiconductor Manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These decisions areusually weekly or monthly. The MPC-based tactical decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily decisions on starts at the variousManufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor Manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of Semiconductor Manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of Manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in Semiconductor Manufacturing is demonstrated in each case study.
Karl G. Kempf - One of the best experts on this subject based on the ideXlab platform.
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model predictive control strategies for supply chain management in Semiconductor Manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in Semiconductor Manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a tactical decision policy that is an integral part of a comprehensive hierarchical decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in Semiconductor Manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of Semiconductor Manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing Semiconductor Manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a tactical decision module ispresented for supply chain management in the Semiconductor Manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These decisions areusually weekly or monthly. The MPC-based tactical decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily decisions on starts at the variousManufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor Manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of Semiconductor Manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of Manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in Semiconductor Manufacturing is demonstrated in each case study.
Chenfu Chien - One of the best experts on this subject based on the ideXlab platform.
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hybrid particle swarm optimization combined with genetic operators for flexible job shop scheduling under uncertain processing time for Semiconductor Manufacturing
IEEE Transactions on Semiconductor Manufacturing, 2018Co-Authors: Thitipong Jamrus, Chenfu Chien, Mitsuo Gen, Kanchana SethananAbstract:Semiconductor Manufacturing is a complicated flexible job-shop scheduling problem (FJSP) of combinatorial complexity. Because of the adoption of advanced process control and advanced equipment control, the processing time in advanced wafer fabs become uncertain. Existing approaches considering constant processing time may not be appropriate to address the present problem in a real setting. In practice, processing times can be represented as intervals with the most probable completion time somewhere near the middle of the interval. A fuzzy number that is a generalized interval can represent this processing time interval exactly and naturally. This paper developed a hybrid approach integrating a particle swarm optimization algorithm with a Cauchy distribution and genetic operators (HPSO+GA) for solving an FJSP by finding a job sequence that minimizes the makespan with uncertain processing time. In particular, the proposed hybridized HPSO+GA approach employs PSO for creating operation sequences and assigning the time and resources for each operation, and then uses genetic operators to update the particles for improving the solution. To estimate the validity of the proposed approaches, experiments were conducted to compare the proposed approach with conventional approaches. The results show the practical viability of this approach. This paper concludes with discussions of contributions and recommends directions for future research.
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bayesian inference for mining Semiconductor Manufacturing big data for yield enhancement and smart production to empower industry 4 0
Applied Soft Computing, 2017Co-Authors: Marzieh Khakifirooz, Chenfu Chien, Yingjen ChenAbstract:Abstract Big data analytics have been employed to extract useful information and derive effective Manufacturing intelligence for yield management in Semiconductor Manufacturing that is one of the most complex Manufacturing processes due to tightly constrained production processes, reentrant process flows, sophisticated equipment, volatile demands, and complicated product mix. Indeed, the increasing adoption of multimode sensors, intelligent equipment, and robotics have enabled the Internet of Things (IOT) and big data analytics for Semiconductor Manufacturing. Although the processing tool, chamber set, and recipe are selected according to product design and previous experiences, domain knowledge has become less efficient for defect diagnosis and fault detection. To fill the gaps, this study aims to develop a framework based on Bayesian inference and Gibbs sampling to investigate the intricate Semiconductor Manufacturing data for fault detection to empower intelligent Manufacturing. In addition, Cohen’s kappa coefficient was used to eliminate the influence of extraneous variables. The proposed approach was validated through an empirical study and simulation. The results have shown the practical viability of the proposed approach.
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hybrid estimation of distribution algorithm with multiple subpopulations for Semiconductor Manufacturing scheduling problem with limited waiting time constraint
Conference on Automation Science and Engineering, 2014Co-Authors: Hungkai Wang, Chenfu Chien, Mitsuo GenAbstract:This paper considers a Semiconductor Manufacturing scheduling problem (SMSP), subjected to all the practical constraints such as limited waiting time, machine status, different process time on different machines, setup time and arrival time in wafer fabrication facilities (fabs) of Semiconductor Manufacturing industry. A hybrid estimation of distribution algorithm with multiple subpopulations (HEDA-MS) is proposed to solve SMSP effectively within several specified minutes for an online scheduling requirement. An empirical study simulates eight scenarios from practical data to compare the performance of HEDA-MS and GA, not only to minimize the makespan, but to make total exceeded of limited waiting time into zero. For all the scenarios, the proposed HEDA-MS obtains a smaller makespan than GA with less total exceeded limited waiting time.
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an intelligent system for wafer bin map defect diagnosis an empirical study for Semiconductor Manufacturing
Engineering Applications of Artificial Intelligence, 2013Co-Authors: Chenfu ChienAbstract:Wafer bin maps (WBMs) that show specific spatial patterns can provide clue to identify process failures in the Semiconductor Manufacturing. In practice, most companies rely on experienced engineers to visually find the specific WBM patterns. However, as wafer size is enlarged and integrated circuit (IC) feature size is continuously shrinking, WBM patterns become complicated due to the differences of die size, wafer rotation, the density of failed dies and thus human judgments become inconsistent and unreliable. To fill the gaps, this study aims to develop a knowledge-based intelligent system for WBMs defect diagnosis for yield enhancement in wafer fabrication. The proposed system consisted of three parts: graphical user interface, the WBM clustering solution, and the knowledge database. In particular, the developed WBM clustering approach integrates spatial statistics test, cellular neural network (CNN), adaptive resonance theory (ART) neural network, and moment invariant (MI) to cluster different patterns effectively. In addition, an interactive converse interface is developed to present the possible root causes in the order of similarity matching and record the diagnosis know-how from the domain experts into the knowledge database. To validate the proposed WBM clustering solution, twelve different WBM patterns collected in real settings are used to demonstrate the performance of the proposed method in terms of purity, diversity, specificity, and efficiency. The results have shown the validity and practical viability of the proposed system. Indeed, the developed solution has been implemented in a leading Semiconductor Manufacturing company in Taiwan. The proposed WBM intelligent system can recognize specific failure patterns efficiently and also record the assignable root causes verified by the domain experts to enhance troubleshooting effectively.
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Semiconductor Manufacturing intelligence and key factor control mechanism for managing production cycle time
International Symposium on Semiconductor Manufacturing, 2011Co-Authors: Chenfu Chien, Hungya Huang, Kuohao ChangAbstract:▓ Semiconductor wafer fabrication plays a central role in electronics supply chain. Two characteristics of electronic products contribute to making the demand volatile: high variety and short life cycle. Semiconductor wafer manufacturers confront the challenges of reducing the lengthy cycle times as a means to deal with the highly variable demand in the supply chain. ▓ Cycle time reduction thus becomes a critical issue for Semiconductor wafer fabrication companies to attain competitive advantages. ▓ Semiconductor Manufacturing has long time been recognized as one of the most complicated Manufacturing process owing to unrelated parallel machine environment, dynamic job arrival, general precedence constraint, and job re-circulation. These characteristics lead to longer mean and variance of cycle times. ▓ In order to manage cycle time, we developed a framework considering the factors that can be used to control production line status such as WIP, availability, utilization, etc, and analyzed the impacts of these factors on the production cycle time.
Fan-tien Cheng - One of the best experts on this subject based on the ideXlab platform.
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a novel virtual metrology scheme for predicting cvd thickness in Semiconductor Manufacturing
IEEE-ASME Transactions on Mechatronics, 2007Co-Authors: Min Hsiung Hung, Fan-tien Cheng, Tungho Lin, Rungchuan LinAbstract:In an advanced Semiconductor fab, online quality monitoring of wafers is required for maintaining high stability and yield of production equipment. The current practice of only measuring monitor wafers may not be able to timely detect the equipment-performance drift happening in-between the scheduled measurements. This may cause defects of production wafers and, thereby, raise the production cost. In this paper, a novel virtual metrology scheme (VMS) is proposed for overcoming this problem. The proposed VMS is capable of predicting the quality of each production wafer using parameters data from production equipment. Consequently, equipment-performance drift can be detected promptly. A radial basis function neural network is adopted to construct the virtual metrology model. Also, a model parameter coordinator is developed to effectively increase the prediction accuracy of the VMS. The chemical vapor deposition (CVD) process in Semiconductor Manufacturing is used to test and verify the effectiveness of the proposed VMS. Test results show that the proposed VMS demonstrates several advantages over the one based on back-propagation neural network and can achieve high prediction accuracy with mean absolute percentage error being 0.34% and maximum error being 1.15%. The proposed VMS is simple yet effective, and can be practically applied to construct the prediction models of Semiconductor CVD processes.
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A virtual metrology scheme for predicting CVD thickness in Semiconductor Manufacturing
Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006., 2006Co-Authors: Ming-hsiung Hung, Fan-tien ChengAbstract:For maintaining high stability and production yield of production equipment in a Semiconductor fab, on-line quality monitoring of wafers is required. In current practice, physical metrology is performed only on monitoring wafers that are periodically added in production equipment for processing with production wafers. Hence, equipment performance drift happening in-between the scheduled monitoring cannot be detected promptly. This may cause defects of production wafers and the production cost. In this paper, a novel virtual metrology scheme (VMS) that is based on a radial basis function neural network (RBFN) is proposed for overcoming this problem. The VMS is capable of predicting quality of production wafers using real-time sensor data from production equipment. Consequently, equipment performance abnormality or drift can be detected timely. Finally, the effectiveness of the proposed VMS is validated by tests on chemical vapor deposition (CVD) processes in practical Semiconductor Manufacturing. It is therefore proved that RBFN can be effectively used to construct prediction models for CVD processes
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Development of a web-services-based e-diagnostics framework for Semiconductor Manufacturing industry
IEEE Transactions on Semiconductor Manufacturing, 2005Co-Authors: Min Hsiung Hung, Fan-tien Cheng, Sze Chien YehAbstract:Recently, the emerging Web-Services technology has provided a new and excellent solution to the data integration among heterogeneous systems. A Web-Services-based e-diagnostics framework (WSDF) is proposed. It can achieve the automation of diagnostic processes and diagnostics information integration for Semiconductor equipment. First, the system framework and the system component model are designed. Then, the object-oriented analysis and design of system components are accomplished. In particular, for the purpose of code reuse, several common functions, such as simple object access protocol communication, universal description discovery and integration registration, security mechanism, data exchange mechanism, and local database access, are built into a generic component called Web-Service agent. By inheriting the Web-service agent, other system components can be constructed and have these common functions. In addition, a unified authentication-service mechanism and a safe network connection are also designed in the framework. WSDF is intended to support the e-diagnostics functions defined by International SEMATECH. It is believed that WSDF can be applied to construct e-diagnostics systems for the Semiconductor Manufacturing industry.
