1.2 kV SiC Super Junction MOSFET Search Result 3
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Center for Semiconductor Technology Research
Reliability of Cu-to-Cu direct bonging: In order to realize the reliability of Cu-to-Cu direct bonding interconnects, we adopted nanotwinned Cu (nt-Cu) electroplating process to fabricate nt-Cu microbump for Cu-to-Cu direct bonding. Figure (a) is the photo of chip after bonding. Underfill (UF) dispensing was used to protect Cu microbumps. The cross-section SEM image of Cu microbump is shown in figure (b). Some chips underwent the reliability tests, including temperature cycling test (TCT) and electromigration (EM) test. To further study the failure mechanism, finite element analysis (FEA) was done to know the stress and current distribution during reliability test. This research could provide fundamental understanding for Cu-Cu joints with organic dielectric hybrid bonding.
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Embedded AI Deep Learning Technology for ADAS/Niche Self-Driving Applications
This project goes on developing embedded AI deep learning technology focus on the ADAS/Self-driving applications. We develop the technology from five aspects, including automatic object labeling toollabeled datasets, deep learning softwarehardware technology development, various ADAS/Self-driving object detectionbehavior prediction technology, self-driving control technology as well as virtual simulation environment establishment for ADAS/Self-driving applications.
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