Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher, eds USING SIMULATION AND HYBRID SEQUENCING OPTIMIZATION FOR MAKESPAN REDUCTION AT A WET TOOL Anna Rotondo John Geraghty Paul Young Enterprise Process Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University Dublin 9, Ireland ABSTRACT When rigid scheduling rules apply to wet tools, the development of Cycle Time (CT) optimization strate- gies becomes a relevant challenge. The impact of sequencing optimization on makespan performance at a wet tool is investigated here by means of a hybrid optimization model that combines an exact optimiza- tion approach, based on an efficient permutation concept, and a heuristics, based on Genetic Algorithms (GAs). The model also includes a scheduling module that reproduces the control logics governing wet tools operating in a real semiconductor manufacturing plant and proves effective in generating efficient and detailed schedules in short computational times. The realistic assumptions on which the scheduling module is based allow the simulation of different tool configurations. The results obtained show that sig- nificant makespan reductions can be achieved by means of a mere sequencing optimization as parallel processing within the wet tools is better exploited. 1 INTRODUCTION In manufacturing systems characterized by complex interactions between their components, small varia- tions of operating factors, implemented at any production step, may have a significant impact on the over- all system performance. As a result, Cycle Time (CT) reductions obtained at critical production steps could generate considerable productivity improvements and eventually lead to a capacity increase at no investment cost (Quek et al. 2007; Aydt et al. 2008). Hence, CT improvements also become strategic tar- gets for companies that want to maintain competitive advantages, especially when they operate in highly dynamic industries, such as the semiconductor industry (Kuo, Chien, and Chen 2011). As the overall cleaning process constitutes almost 10% of the operations in a semiconductor wafer manufacturing plant (Kabak, Heavey, and Corbett 2010), wet stations certainly represent critical produc- tion steps for the semiconductor wafer manufacturing process. Automated wet tools can be classified as batch chamber tools; they include several chambers, or tanks, each of which can accommodate a batch of wafers, usually made of one or two lots. Due to the inherent complexities and the peculiar scheduling constraints, simulation approaches are usually preferred to model wet tools (Govind and Fronckowiak 2003; Noack et al. 2008; Aydt et al. 2008); simulation models support investigations on the impact of op- erational settings variations on wet tools performance (Noack et al. 2008). Using an optimization frame- work for the metallization process, Noack et al. (2008) optimize dispatching rules and virtual queue ca- pacity in front of a wet tool. The effects of various dispatching rules and recipe dedication schemes on wet stations capacity is analyzed by Quek et al. (2007). Recipe dedication optimization at wet tools is also investigated by Aydt et al. (2008); a symbiotic simulation control system is created to dynamically adjust the recipe dedication settings at the available tools with respect to the current WIP level. Govind and 978-1-4673-4781-5/12/$31.00 ©2012 IEEE