Intelligent diagnosis of flip chip solder joints with resolution enhanced SAM image

Xiangning Lu; Zhenzhi He; Hector Gutierrez; Guanglan Liao; Tielin Shi

doi:10.1016/j.isatra.2023.02.014

Intelligent diagnosis of flip chip solder joints with resolution enhanced SAM image

ISA Trans. 2023 Jul:138:603-610. doi: 10.1016/j.isatra.2023.02.014. Epub 2023 Feb 17.

Authors

Xiangning Lu¹, Zhenzhi He², Hector Gutierrez³, Guanglan Liao⁴, Tielin Shi⁴

Affiliations

¹ School of Mechanical & Electrical Engineering, Jiangsu Normal University, Xuzhou, 221116, China.
² School of Mechanical & Electrical Engineering, Jiangsu Normal University, Xuzhou, 221116, China. Electronic address: zzhezz@jsnu.edu.cn.
³ Department of Mechanical and Aerospace Engineering, Florida Institute of Technology, Melbourne 32901 FL, USA.
⁴ State Key Laboratory of Digital Manufacturing Equipment and Technology, Huangzhong University of Science and Technology, Wuhan 430074, China.

PMID: 36841720
DOI: 10.1016/j.isatra.2023.02.014

Abstract

Scanning acoustic microscopy (SAM) technique has been applied to defect inspection in electronic devices. With the increase of packaging density, detection of the micro-defects in high density devices becomes more and more challenging. The SAM test is suffering from sacrificing the spatial resolution to reach a certain penetration depth of the ultrasonic waves. So it is necessary to enhance the resolution level of the SAM image. In this paper, a wavelet based resolution enhancement technique was investigated to reconstruct a high quality image for SAM test of the flip chip packages. The stationary wavelet transform was adopted to decompose the captured SAM image into four frequency subbands, and the high frequency subbands were enhanced by adding the difference matrix in the intermediate stage, and a super resolution SAM image was derived from combining all the subbands by using the Inverse Discrete Wavelet Transform. Then the solder joints segmented from the SR-SAM image were classified by using the SVM algorithm. The results validated that the proposed technique is effective to improve the detection accuracy of SAM test.

Keywords: Defect inspection; Flip chip; SAM test; Solder joint; Wavelet Transform.