Operational time variability is one of the key parameters determining the average cycle time of lots. Many different sources of variability can be identified such as machine breakdowns, setup, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as overall equipment effectiveness (OEE) used in the semiconductor industry are entirely based on mean value analysis and do not include variances. The main contribution of this paper is the development of a new algorithm that enables estimation of the mean effective process time and the coefficient of variation 2 of a multiple machine workstation from real fab data. The algorithm formalizes the effective process time definitions as known in the literature. The algorithm quantifies the claims of machine capacity by lots, which include time losses due to down time, setup time, and other irregularities. The estimated and 2 values can be interpreted in accordance with the well-known queueing relations. Some test examples as well as an elaborate case from the semiconductor industry show the potential of the new effective process time algorithm for cycle time reduction programs. Index Terms-Capacity and cycle time losses, data extraction, equipment modeling, factory dynamics, manufacturing line performance. I. INTRODUCTION E QUIPMENT in semiconductor manufacturing is subject to many sources of variability. An important source is machine down time, which occurs due to highly complex and technologically advanced semiconductor manufacturing processes [1]. Many other corrupting operational influences are also present, such as batching, hot lots, rework, setup, and operator availability. All together, they introduce a substantial amount of variability in the interarrival and operational times of the lots during their flow through the fab. Queue times are mainly influenced by variability and utilization. High utilization is necessary in the semiconductor industry in order to maximize productivity and minimize costs. In combination with large variability, high utilization leads to large cycle
Operational time variability is one of the key parameters determining the average cycle time of lots. Many different sources of variability can be identified such as equipment breakdowns, setup, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as the Overall Equipment Efficiency (OEE) in semiconductor industry are entirely based on mean value analysis and do not include variances.The main contribution of this paper is the development of a new algorithm that enables to estimate the mean effective process time te and the coefficient of variation c,' of a multiple machine equipment family from real fab data. The algorithm formalizes the effective process time definitions as given by Hopp and Spearman [I], and Sattler [2]. The algorithm quantifies the claims of machine capacity by lots, which includes time losses due to down time, setup time, or other irregularities. The estimated te and c,' values can be interpreted in accordance with the well-known G / G / m queueing relations. A test example as well as an elaborate case from semiconductor industry show the potential of the new effective process time (EPT) algorithm for cycle time reduction programs.
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