Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors

Abstract

The semiconductor manufacturing processes have been evolved to improve the yield rate. Here, we studied a sequential and comprehensive algorithm that could be used for fault detection and classification (FDC) of the semiconductor chips. A statistical process control (SPC) method is employed for inspecting whether sensors used in the semiconductor manufacturing process become stable or not. When the sensors are individually stable, the algorithm conducts the relational inspection to identify the relationship between two sensors. The key factor here is the coefficient of determination (R2). If R2 is calculated as more than 0.7, their relationship is analyzed through the regression analysis, while the algorithm conducts the clustering analysis to the sensor pair with R2 less than 0.7. This analysis also provided the capability to determine whether the newly generated data are defective or defect-free. Therefore, this study is not only applied to the semiconductor manufacturing process but can also be to the various research fields where the big data are treated.

Keywords

• defect classification
• machine learning
• Voronoi diagram
• statistical process control