Artificial intelligence deep learning for 3D IC reliability prediction

Po Ning Hsu, Kai Cheng Shie, Kuan Peng Chen, Jing Chen Tu, Cheng Che Wu, Nien Ti Tsou*, Yu Chieh Lo, Nan Yow Chen, Yong Fen Hsieh, Mia Wu, Chih Chen, King-Ning Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore’s law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human’s judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the “Good” or “Failure” condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.

Original languageEnglish
Article number6711
JournalScientific reports
Issue number1
StatePublished - Dec 2022


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