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To realize the automated welding of mounted chips and solve the problems of low efficiency and poor yield rate of manual welding, the detection and localization algorithm of mounted chips and solder joints based on machine vision is proposed. Firstly, to improve the efficiency of the algorithm, we propose a region filtering algorithm with minimized interference to extract image regions of interest (ROI). Secondly, the region-connected domain method is proposed to obtain the set of weld ball centers of gravity and then calculate the radius of the welding ball and the number of empty points. After that, an improved linear fitting method is utilized to calculate the chip body size, average pitch of solder balls, and rotation angle information. Finally, the method proposed in this paper is validated by performing experimental tests on 12 different mounted chips. The experimental results show that the detection accuracy of this method can reach 5 µm for the offset amount and 20 µrad for the angle deviation amount, and the detection time of the unit welding ball is kept within 1.03 ms. Compared with the traditional algorithm, the detection speed is improved while guaranteeing detection accuracy. [ABSTRACT FROM AUTHOR]

Defect inspection of IC devices is getting more challenging with the increase of package density. Scanning acoustic microscopy (SAM) is widely used in electronic industry. The detection resolution is, however, limited by the penetration depth of ultrasound. It is necessary to find a way to improve the resolution and accuracy. A new strategy of multi-scale decomposition and fusion based on the wavelet transform was proposed to enhance the image resolution in SAM detection. The original SAM image was subjected to wavelet decomposition at different scales. Two recombined images A and B were decomposed into low frequency band (cAd1 and cAd2) and high frequency bands (cHd1, cVd1, cDd1, and cHd2, cVd2, cDd2), which were then merged respectively based on the local area energy. A high resolution SAM image was reconstructed by using the new coefficients. Back propagation network modified with genetic algorithm (GA-BP) was utilized to classify the solder joints. The proposed scheme achieved highest recognition accuracy (97.16%) compared with other methods. The new strategy provides an effective way to enhance the image quality and recognition accuracy in SAM detection of micro defect. [ABSTRACT FROM AUTHOR]

In this paper, the reliability of copper pillar micro-bump under random vibration is investigated. Three kinds of copper pillar solder joints with different morphologies were obtained by changing the hot press bonding process, and the random vibration fatigue simulation of flip-flop interconnect solder joints was carried out by using Ansys to obtain the stress–strain distribution law of the solder joints and predict the vibration fatigue life of the solder joints. It is found that the outermost solder joints of flip-flop bonding are most likely to fail; the fatigue life of copper pillar solder joints is predicted based on the three interval method, the Miner linear cumulative damage criterion and the high cycle fatigue formula; reliability experiments are conducted, and it is found that the drum solder joints are most prone to cracking, followed by hourglass and columnar shapes and that all cracks are caused by the substrate side of copper pillar. Cracks are sprouted from the contact surface between the copper pillar and solder on the substrate side and expand internally in the intermetallic compound (IMC) layer, while the presence of voids aggravates the generation of cracks. [ABSTRACT FROM AUTHOR]