Re-Weighted Constrained Feature Characterization Method Study for Flip Chip Solder Joint Vibration Signals

Flip chip technology has been extensively adopted in integrated circuit (IC) packaging, and the integration of flip chip technology with solder joint interconnection methods has been utilized in the manufacturing of electronic devices universally. As the development of flip chip towards high density and ultra-fine pitch, the inspection of flip chips is confronted with great challenges. Aiming at the characteristics of the chip signal with strong interference noise and weak feature information, the sparse representation model under the reweighting constraint is proposed. We analyzed the low-rank prior of the signal feature matrix and explore the construction of a sparse representation dictionary to establish an adaptive singular value decomposition (SVD) dictionary for the variant features. Subsequently, we investigate the adaptive weight matrix that aligns with the time-varying structure of the noise and the distribution pattern of the feature singular values, and construct a sparse characterization model under the reweighting constraint. Simulation analysis shows that the proposed algorithm can effectively realize the filtering of time-varying noise in the signal, so as to reliably extract the weak defect features.