Simulation Research on Sparse Reconstruction for Defect Signals of Flip Chip Based on High-Frequency Ultrasound

Abstract

Flip chip technology has been widely used in various fields. As the density of the solder balls in flip chip technology is increasing, the pitch among solder balls is narrowing, and the size effect is more significant. Therefore, the micro defects of the solder balls are more difficult to detect. In order to ensure the reliability of the flip chip, it is very important to detect and evaluate the micro defects of solder balls. High-frequency ultrasonic testing technology is an effective micro-defect detection method. In this paper, the interaction mechanism between high-frequency ultrasonic pulse and micro defects is analyzed by finite element simulation. A transient simulation model for the whole process of ultrasonic scanning of micro defects is established to simulate scanning in acoustic microscopy imaging. The acoustic propagation path map is obtained for analyzing acoustic energy transmission during detection, and the edge blurring effect in micro-defect imaging detection is clarified. The processing method of the time-domain signal and cross-section image signal of micro defects based on sparse reconstruction is studied, which can effectively improve the accuracy of detection and the signal-to-noise ratio.

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

“…However, the high dimensionality of images with rich details makes the corresponding Gabor dictionary too large, resulting in increased computational consumption. Yu et al (2020) achieved good results by using Orthogonal Matching Pursuit (OMP) algorithms to sparse decompose and denoise images based on the Gabor dictionary. However, there is an error between the analytical dictionary based on the functional model and the actual image, which makes it impossible to ensure that the sparse decomposition results of the image are always accurate.…”