The intelligent detection method for flip chips using CBN-S-Net algorithm with SAM images

• Published in: Journal of manufacturing processes Vol. 83; pp. 60 - 67
• Main Authors: Sha, Yuhua, He, Zhenzhi, Gutierrez, Hector, Du, Jiawei, Yang, Weiwei, Lu, Xiangning
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022
• Subjects: CBN-S-Net; Classification; Defect inspection; Flip chip; SAM Technology; CBN-S-Net; Flip chip; SAM Technology; Defect inspection; Classification
• ISSN: 1526-6125; 2212-4616
• DOI: 10.1016/j.jmapro.2022.08.058

Flip chip has become one of the mainstream technologies in microelectronic packaging. Solder bumps play an important role in the interconnection of flip chips packages. The scanning acoustic microscopy (SAM) technology and a new network model were investigated for intelligent detection of flip chips. A new network model called CBN-S-Net was proposed based on a deep convolution network CBN and an optimized Siamese network. The CBN convolution network was used to extract the deep fusion features of solder bumps, and new triplet sample pairs were designed to measure the similarity between solder bumps. With the strategy of triplet sample pairs, the SAM images of the flip chip were used to verify the effectiveness of the designed network model. The results showed that the improved network has a high detection accuracy of 98.73%, and the proposed method is effective for the intelligent detection of solder bumps in high-density electronic packages.