Abstract
Yield enhancement is a critical factor to maintain competitive ability in semiconductor manufacturing. Early identification of the yield-loss causes for ramp-up stage from data analysis in early stage is the key to shorten the time to market. However, the high col-linearity characterized by the complicated re-work flow of manufacturing process, and complicated interactions between the factors due to the characteristic of advanced process make the analysis more difficult. In addition, number of factors in ramp-up stage is larger than the sample size（p>>n）, the yield analysis is a great challenge. This study focuses on troubleshooting in the ramp-up stage, and aims to construct a manufacturing intelligence framework for failure detection of data mining. Three main steps as following：（1）key factors screening：to narrow the possible factor by integrating Kruskal–Wallis test and Random Forest. A suitable dimensional reduction to insure the efficient and effective quality of analysis.（2）interaction factors detection：to detect the possible combined factors with high explanation of responses by weighted least square regression.（3）model construction：construct a model to explain the relationship between the factors and the responses. Form the extracted information we can provide the hint of root causes and the suggestion with priority of trouble shooting. At least, research simulates the data based on the real data, collected from a semiconductor foundry company in Taiwan, to validate the proposed data mining framework.