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Unveil the Black Box for Performance Efficiency of OEE for Semiconductor Wafer Fabrication

机译:揭开用于半导体晶圆制造的OEE性能效率的黑匣子

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This study has demonstrated practical viability of the proposed approach employing datamining technics, Neural Networks (NNs), to estimate the productivity of individual process tool sets in a semiconductor factory, and to assess the efficiency loss by 15 related individual input factors, which included “process time”, “number of recipes”, “usable tool”, “Q-time constrain”, “standard deviation of lot size”, “batch size”, “sampling rate”, “hot lot ratio” and etc.. An empirical study was conducted by using the equipment data of a real fab. The results showed that the proposed approaches can define performance efficiency of Overall equipment efficiency (OEE) more reasonable, which discover underlying factors for efficiency loss, and help to improve performace efficiency from 91.23% to 94.03%.
机译:这项研究证明了采用数据挖掘技术,神经网络(NNs)来评估半导体工厂中单个工艺工具集的生产率以及通过15个相关的单个输入因素来评估效率损失的方法的实际可行性。处理时间”,“配方数量”,“可用工具”,“ Q时间约束”,“批量大小的标准偏差”,“批量大小”,“采样率”,“批量比率”等。通过使用真实晶圆厂的设备数据进行了实证研究。结果表明,所提出的方法可以更合理地定义设备总体效率(OEE)的性能效率,从而发现造成效率损失的根本因素,并将性能效率从91.23 \%提高到94.03 \%。

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