Big data analytics for forecasting cycle time in semiconductor wafer fabrication system
针对半导体晶圆制造系统，设计了一种大数据分析方法，通过数据采集、预处理、特征选择和并行预测模型来预测晶圆批次的周期时间，在大规模数据集上比线性回归和反向传播网络更准确。
In order to improve the prompt delivery reliability of the semiconductor wafer fabrication system, a big data analytics (BDA) is designed to predict wafer lots’ cycle time (CT), which is composed by four parts: data acquisition, data pre-processing, data analysing and data prediction. Firstly, the candidate feature set is constructed to collecting all features by analysing the material flow of wafer foundry. Subsequently, a data pre-processing technique is designed to extract, transform and load data from wafer lot transactions data-set. In addition, a conditional mutual information-based feature selection process is proposed to select key feature subset to reduce the dimension of data-set through data analysing without pre-knowledge. To handle the large volumes of data, a concurrent forecasting model is designed to predict the CT of wafer lots in parallel as well. According to the numerical analysis, the predict accuracy of the presented BDA improves clearly with the increase in data size. And, in the large-scale data-set, the BDA has higher accuracy than linear regression and back-propagation network in CT forecasting.