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Modified nearest neighbor fuzzy classification (MNNFC) algorithm is proposed for the character of ship target high resolution range profile (HRRP). Ship length, dispersant, symmetry and central moments features are some stable features for ship HRRP and extracted accurately. Modified nearest neighbor fuzzy classification algorithm is designed for different features to contribute their predominance because the significance and stability of each feature are different. And the membership degree of each feature is modified differently. Experimental results with the actual measured data of 10 ships show that the proposed algorithm is very useful in ship target classification.

The formation of high-melting-point Cu6Sn5 interconnections is crucial to overcome the collapse of Sn-based micro-bumps and to produce reliable intermetallic interconnections in three-dimensional (3D) packages. However, because of multiple reflows in 3D package manufacturing, Cu6Sn5 interconnections will experience cyclic polymorphic transitions in the solid state. The repeated and abrupt changes in the Cu6Sn5 lattice due to the cyclic polymorphic transitions can cause extreme strain oscillations, producing damage at the surface and in the interior of the Cu6Sn5 matrix. Moreover, because of the polymorphic transition-induced grain splitting and superstructure phase formation, the reliability of Cu6Sn5 interconnections will thus face great challenges in 3D packages. In addition, the Cu6Sn5 polymorphic transition is structure-dependent, and the η′↔η polymorphic transition will occur at the surface while the η′↔ηs↔η polymorphic transition will occur in the deep matrix. This study can provide in-depth understanding of the structural evolution and damage mechanism of Cu6Sn5 interconnections in real 3D package manufacturing.

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