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\n\n \n \n \n \n \n Determination of Wafer Start Mix in Semiconductor Manufacturing During New Technology Ramp-Up: Model, Solution Method, and an Empirical Study.\n \n \n \n\n\n \n Chang, K.; and Hsieh, L. Y.\n\n\n \n\n\n\n
IEEE Transactions on Systems, Man and Cybernetics: Systems, 46(2): 294-302. 2016.\n
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@article{Hsieh2016IEEESMC,\r\n author = {Kuo-Hao Chang and Liam Y. Hsieh},\r\n journal = {IEEE Transactions on Systems, Man and Cybernetics: Systems},\r\n number = {2},\r\n pages = {294-302},\r\n title = {Determination of Wafer Start Mix in Semiconductor Manufacturing During New Technology Ramp-Up: Model, Solution Method, and an Empirical Study.},\r\n doi = {10.1109/TSMC.2015.2426174},\r\n volume = {46},\r\n publisher={IEEE},\r\n abstract={In semiconductor manufacturing, production ramp-up is a necessary phase before a new technology is introduced because it affects not only the time-to-market, but also the time-to-volume (the time to reach mass production). In production ramp-up, engineering lots are implemented in order to collect data for product and equipment qualification, and they are expected to achieve short cycle times (CTs) and high throughputs. On one hand, the increment of the amount of engineering lots will disrupt the smoothness of the manufacturing flow and cause work-in-process (WIP) bubbles. On the other hand, to ensure the feasibility of the production plan, some throughput and CT targets must be achieved. There is a need to determine an appropriate wafer start mix to strike a balance between the conflicting objectives. However, the complexity and stochasticity existing in the semiconductor manufacturing process makes it a very difficult task. This paper modeled the wafer start mix determination problem and proposed an efficient methodology, called progressive simulation optimization (PSO), based on the simulation metamodeling techniques to handle this problem. The goal was to find the optimal wafer start mix that enables maximum throughput of engineering lots, while satisfying the requirement of CT of normal lots (NL). A numerical study was conducted to evaluate the performance of the proposed PSO, and an empirical study based on real data was conducted to validate the viability of the proposed methodology in practice.},\r\n year = {2016}\r\n}\r\n\r\n
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\n In semiconductor manufacturing, production ramp-up is a necessary phase before a new technology is introduced because it affects not only the time-to-market, but also the time-to-volume (the time to reach mass production). In production ramp-up, engineering lots are implemented in order to collect data for product and equipment qualification, and they are expected to achieve short cycle times (CTs) and high throughputs. On one hand, the increment of the amount of engineering lots will disrupt the smoothness of the manufacturing flow and cause work-in-process (WIP) bubbles. On the other hand, to ensure the feasibility of the production plan, some throughput and CT targets must be achieved. There is a need to determine an appropriate wafer start mix to strike a balance between the conflicting objectives. However, the complexity and stochasticity existing in the semiconductor manufacturing process makes it a very difficult task. This paper modeled the wafer start mix determination problem and proposed an efficient methodology, called progressive simulation optimization (PSO), based on the simulation metamodeling techniques to handle this problem. The goal was to find the optimal wafer start mix that enables maximum throughput of engineering lots, while satisfying the requirement of CT of normal lots (NL). A numerical study was conducted to evaluate the performance of the proposed PSO, and an empirical study based on real data was conducted to validate the viability of the proposed methodology in practice.\n
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\n\n \n \n \n \n \n Application of multi-fidelity simulation modelling to integrated circuit packaging.\n \n \n \n\n\n \n Hsieh, L. Y.; Huang, E.; Chen, C.; Zhang, S.; and Chang, K.\n\n\n \n\n\n\n
International Journal of Simulation and Process Modelling, 11(3-4): 259-269. 2016.\n
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@article{Hsieh2016IJSPM,\r\n title={Application of multi-fidelity simulation modelling to integrated circuit packaging},\r\n author={Liam Y. Hsieh and Edward Huang and Chun-Hung Chen and Si Zhang and Kuo-Hao Chang},\r\n journal={International Journal of Simulation and Process Modelling},\r\n volume={11},\r\n number={3-4},\r\n pages={259-269},\r\n year={2016},\r\n doi={10.1504/IJSPM.2016.078525},\r\n abstract={In semiconductor manufacturing, time-to-market is critical to maintain a competitive advantage through achieving customer satisfaction. Inefficient ways of utilising production resources will lead to a long cycle time. Therefore, machine allocation becomes an essential production decision in many practical manufacturing systems, especially for integrated circuit (IC) packaging. IC packaging is the process of encasing the finished die in a package in order to prevent corrosion and physical damage. Advanced IC packaging techniques add even more complexity into the production system, so reliable average cycle time of this complex system becomes difficult to obtain. We propose a new simulation optimisation framework with multi-fidelity models to study an IC packaging case of the machine allocation problem to pursue a minimum average cycle time. This framework consists of two methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT first employs the low-fidelity model to fast observe all designs, and extracts insightful information from this model by transforming the original design space into an ordinal space. It follows that OS efficiently allocates the computing budget for searching the best design via high-fidelity simulations. An empirical study based on real data was conducted to validate the practical viability of the proposed framework.},\r\n publisher={Inderscience Publishers (IEL)}\r\n}\r\n\r\n
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\n In semiconductor manufacturing, time-to-market is critical to maintain a competitive advantage through achieving customer satisfaction. Inefficient ways of utilising production resources will lead to a long cycle time. Therefore, machine allocation becomes an essential production decision in many practical manufacturing systems, especially for integrated circuit (IC) packaging. IC packaging is the process of encasing the finished die in a package in order to prevent corrosion and physical damage. Advanced IC packaging techniques add even more complexity into the production system, so reliable average cycle time of this complex system becomes difficult to obtain. We propose a new simulation optimisation framework with multi-fidelity models to study an IC packaging case of the machine allocation problem to pursue a minimum average cycle time. This framework consists of two methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT first employs the low-fidelity model to fast observe all designs, and extracts insightful information from this model by transforming the original design space into an ordinal space. It follows that OS efficiently allocates the computing budget for searching the best design via high-fidelity simulations. An empirical study based on real data was conducted to validate the practical viability of the proposed framework.\n
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\n\n \n \n \n \n \n \n Simulation optimization in the era of Industrial 4.0 and the Industrial Internet.\n \n \n \n \n\n\n \n Xu, J.; Huang, E.; Hsieh, L.; Lee, L. H.; Jia, Q.; and Chen, C.\n\n\n \n\n\n\n
Journal of Simulation, 10(4): 310-320. 2016.\n
(best paper award)\n\n
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@article{Hsieh2016JOS,\r\n title={Simulation optimization in the era of Industrial 4.0 and the Industrial Internet},\r\n author={Jie Xu and Edward Huang and Liam Hsieh and Loo Hay Lee and Qing-Shan Jia and Chun-Hung Chen},\r\n journal={Journal of Simulation},\r\n volume={10},\r\n number={4},\r\n pages={310-320},\r\n year={2016},\r\n url={https://sites.google.com/site/liamhsieh/TocherMedal},\r\n doi={10.1057/s41273-016-0037-6},\r\n publisher={Palgrave Macmillan UK},\r\n abstract={Simulation is an established tool for predicting and evaluating the performance of complex stochastic systems that are analytically intractable. Recent research in simulation optimization and explosive growth in computing power have made it feasible to use simulations to optimize the design and operations of systems directly. Concurrently, ubiquitous sensing, pervasive computing, and unprecedented systems interconnectivity have ushered in a new era of industrialization (the so-called Industrial 4.0/Industrial Internet). In this article, we argue that simulation optimization is a decision-making tool that can be applied to many scenarios to tremendous effect. By capitalizing on an unprecedented integration of sensing, computing, and control, simulation optimization provides the “smart brain” required to drastically improve the efficiency of industrial systems. We explore the potential of simulation optimization and discuss how simulation optimization can be applied, with an emphasis on the recent development of multi-fidelity/multi-scale simulation optimization.},\r\n note ={(best paper award)}\r\n}\r\n\r\n
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\n Simulation is an established tool for predicting and evaluating the performance of complex stochastic systems that are analytically intractable. Recent research in simulation optimization and explosive growth in computing power have made it feasible to use simulations to optimize the design and operations of systems directly. Concurrently, ubiquitous sensing, pervasive computing, and unprecedented systems interconnectivity have ushered in a new era of industrialization (the so-called Industrial 4.0/Industrial Internet). In this article, we argue that simulation optimization is a decision-making tool that can be applied to many scenarios to tremendous effect. By capitalizing on an unprecedented integration of sensing, computing, and control, simulation optimization provides the “smart brain” required to drastically improve the efficiency of industrial systems. We explore the potential of simulation optimization and discuss how simulation optimization can be applied, with an emphasis on the recent development of multi-fidelity/multi-scale simulation optimization.\n
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