Online learning for DBC segmentation of new IGBT samples based on computed laminography imaging

Insulated gate bipolar transistor (IGBT) is a power semiconductor module .Voids may arise in its solder process when a contaminant or gas is absorbed into the solder joint. They heavily influence the heat exchange efficiency of IGBT, so void inspection is very important. The segmentation of solder region is a crucial step for automated defect detection of IGBT based on x-ray computed laminography (CL) system. In recent years, deep learning has made remarkable process in semantic segmentation and has been used for the segmentation of solder joint between the direct bonded copper (DBC) substrate and baseplate, which has been proved to be accurate and efficient. However, deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when new IGBT samples encountered. Hence, this paper proposes to use online learning techniques to continuously improve the learned model by feeding new IGBT samples without losing previously learned knowledge.