Automatic Classification of C-SAM Voids for Root Cause Identification of Bonding Yield Degradation

Wafer-level direct bonding technology is a key process for the production of backside illuminated (BSI) CMOS image sensor (CIS). Usually, constant-depth mode scanning acoustic microscope (C-SAM) 300mm wafer images are acquired and defect size distribution is provided to monitor defects that degrade bonding yield. Current solutions are not providing information detailed enough to identify the root cause of this degradation. In this paper, we propose a rule-based method for the classification of the defects and automatic segmentation of the defects to extract precise measurements depending on the type of defect. All these information will allow to reduce the time to analyze the images and improve the precision and consistency of the analysis.