Regularized Machine Learning Methods in Semiconductor Foundry Data Analysis
|關鍵字:||機器學習;製程資料分析;machine learning;foundry data analysis|
During wafer fabrication, volumes of data were recorded from monitoring through multi-stage and multi-step of manufacturing process. Yield quality is expected to be increased by discovering the information from the recorded data. For example, a drift in the value may cause a failure, or a certain combination of parameters can lead to better wafer performance. However, the great volume of recorded data may lead to difficulties in identifying the data. To identify the root causes of defects and process parameters that are linked to yield performance, this study aims to develop a regularized machine learning method to reduce the dimension of parameters and indicate the key features which lead to the lower quality in yield. Data for this research were obtained from two semiconductor foundry companies in Taiwan. LASSO regression and penalty SVM are introduced to model the data in this thesis. The results show that the drop of yield quality is truly affected by some parameters and the regularized method is effective in shrinking the model to the root causes. Besides, a penalty SVM in high dimension implementation is introduced and realized in this work.
|Appears in Collections:||Thesis|