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The semiconductor industry is increasingly challenged to improve manufacturing productivity while keeping pace with technology advances and maintaining cost per function goals to meet the demands of their served markets. The task of improving factory productivity is influenced by shrinking device geometries and manufacturing methods which reduce overall cycle time. Given the complexity of semiconductor manufacturing, production ramp time and the required capital investment; the decision of which path to pursue must be balanced against the business objectives of each device manufacturer. This paper focuses on productivity improvements applicable to existing factory architectures and more specifically the reduction of the move batch size, more commonly known as the transport lot size, and its effect on the overall factory cycle time. Multi-variable, discrete-event factory simulations are used to illustrate the relationship between transport lot size and the overall cycle time using a conventional 300mm factory architecture with batch tools.

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Increasing factory throughput is a critical issue in the semiconductor industry, and a quick transition of material to the next location in the automation system plays a significant role in increasing throughput. A dynamic pathfinding algorithm for a vehicle-based automated material handling system (AMHS) is discussed in this paper. The dynamic path-finding algorithm uses distance between nodes, node penalties, and the number of vehicles queued to calculate the total cost of a path. This paper introduces the use of historical data from the AMHS and discusses how to effectively utilize such data in critical situations to improve overall AMHS performance.