Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.
Bibliographical noteFunding Information:
This work was supported by the Brain Korea 21 program in 2006–2011, Seoul R&D Program (TR080589M0209722), and Mid-career Researcher Program funded by the NRF (National Research Foundation) and MEST (No. 400-20110010 ). This work was also supported by the Engineering Research Institute of SNU and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( 2011-0021893 ).
- Dimensionality reduction
- Faulty wafer detection
- Novelty detection
- Semiconductor manufacturing
- Virtual metrology