Quantifying operational time variability : the missing parameter for cycle time reduction

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, set-up, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as the overall equipment efficiency (OEE) 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 te and the coefficient of variation ce2 of a multiple machine equipment family from real fab data. The algorithm formalizes the effective process time definitions as given by Hopp and Spearman (2000), and Sattler (1996). The algorithm quantifies the claims of machine capacity by lots, which includes time losses due to down time, set-up time, or other irregularities. The estimated te and ce 2 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 the semiconductor industry show the potential of the new effective process time (EPT) algorithm for cycle time reduction program

Clustertool optimization through scheduling rules

Clustertools play an increasing important role in modern semiconductor fabs. These tools are complex and have a large impact on fab performance. In this paper, a structured approach is presented to give insight in the dynamic behavior of clustertools and to optimize their performance. The approach is applied to the metal area of a waferfab. Scheduling rules are defined for clustertools of this area. A dynamic simulation model is used to evaluate the rules. Experiments show that an average improvement of 8 percent on cycle time and throughput is achieved by usage of scheduling rule

Characterization of operational time variability using effective process times

Operational time variability is one of the key parameters determining theaverage cycle time of lots. Many different sources of variability can beidentified such as machine breakdowns, setup, and operator availability.However, an appropriate measure to quantify variability is missing. Measuressuch as Overall Equipment Effectiveness (OEE) used in the semiconductorindustry are entirely based on mean value analysis and do not includevariances. The main contribution of this paper is the development of a new algorithm thatenables estimation of the mean effective process time t_e and the coefficientof variation c_e^2 of a multiple machine workstation from real fab data. Thealgorithm formalizes the effective process time definitions as known in theliterature. The algorithm quantifies the claims of machine capacity by lots,which include time losses due to down time, setup time, and otherirregularities. The estimated t_e and c_e^2 values can be interpreted inaccordance with the well-known G/G/m queueing relations. Some test examples aswell as an elaborate case from the semiconductor industry show the potential ofthe new effective process time algorithm for cycle time reduction programs