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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]

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. • An intelligent system is developed to inspect the solder joints of flip chip. • The SAM image is decomposed into four different frequency components. • High resolution SAM image is reconstructed with the wavelet-based method. • The solder joints are segmented from the SR-SAM image. • The SR-SAM image has high accuracy for defect recognition. [ABSTRACT FROM AUTHOR]

The flip-chip (FC) technology is widely used in microelectronic packaging. As the dimensions and pitch of the chip solder bumps are getting smaller, defect inspection becomes increasingly challenging. In this paper, scanning acoustic microscopy (SAM) was used for the non-destructive inspection of FC packages. However, the detection accuracy may be limited by a low resolution of the SAM image. Therefore, the image super-resolution (SR) method was employed to enhance the SAM image quality. The modified very-deep SR (VDSR) algorithm, based on a convolutional neural network (CNN), was investigated to reconstruct high resolution acoustic micrographs. In addition, a CNN-based classification model was designed for the classification of the solder joints. The results obtained in the present study demonstrate that the improved VDSR combined with the CNN classification model provides excellent performance for improving the SAM detection accuracy of the inspection of flip chip packages. [ABSTRACT FROM AUTHOR]

With the development of chip integration, scanning acoustic microscopy (SAM) technology is widely used in the intelligent inspection of chips. However, only a few SAM images of the chip are acquired for the SAM test, which will lead to a lack of sufficient data for intelligent recognition of the chip defects. To solve this problem, a generative adversarial network with the addition of a residual attention module, RA-GAN, was proposed and applied to a small sample. Firstly, the original generative adversarial network (GAN) was constructed and the residual attention module was added to GAN. Secondly, the part of the generator network was improved for the SAM images of flip chip, which increases the perceptual field and enriches the details of the generated images. Then, the appropriate learning rate was adjusted during the network training phase, which can prevent the overfitting of the model. Finally, an enhanced dataset with improved image quality and quantity was obtained. To evaluate the effectiveness of the reconstructed dataset, deep learning algorithms were used for SAM image classification. The results validate the effectiveness and advancement of combining RA-GAN with traditional image data enhancement, which can be applied for small sample classification in engineering applications. • The scanning acoustic microscopy (SAM) is an effective non-destructive testing technique for flip chip. • An intelligent method is used to enhance the SAM dataset of flip chip, which is called RA-GAN. • A variety of data enhancement techniques are used to compare the effectiveness of dataset enhancement. • Multiple classification algorithms are used for diagnostic classification. [ABSTRACT FROM AUTHOR]

Flip chip technology has been extensively used in high density electronic packaging over the past decades. With the decrease of solder bumps in dimension and pitch, defect inspection of solder bumps becomes more and more challenging. In this paper, an intelligent diagnosis system using the scanning acoustic microscopy (SAM) is investigated, and the fuzzy support vector machine (F-SVM) algorithm is developed for solder bump recognition. In the F-SVM algorithm, we apply a fuzzy membership to input feature data so that the different input features can make different contributions to the learning procedure of the network. It solves the problem of feature data aliasing in the traditional SVM. The SAM image of flip chip is captured by using an ultrasonic transducer of 230 MHz. Then the segmentation of solder bumps is based on the gradient matrix of the original image, and the statistical features corresponding to every solder bump are extracted and adopted to the F-SVM network for solder bump classification and recognition. The experiment results show a high accuracy of solder defect recognition, therefore, the diagnosis system using the F-SVM algorithm is effective and feasible for solder bump defect inspection. [ABSTRACT FROM AUTHOR]

In the past few decades, Flip chip (FC) has been one of the mainstream technologies in IC packaging. But the size and spacing of the solder bumps are decreasing gradually, defect detection is getting more and more difficult. It is necessary to explore an efficient and accurate detection method. In this paper, the scanning acoustic microscopy (SAM) and the general regression neural network (GRNN) were investigated for inspecting the solder defect. The SAM machine of D9500 was utilized to test the samples. The solder bumps, as the region of interest, were separated from the achieved image, and transformed into the binary images. The representative features of each solder joint were extracted, and then fed to the GRNN for solder joints classification. The recognition accuracy reached 97.79%. The GRNN, combined with the SAM technology, has been validated to be an effective method for intelligent diagnosis of flip chips. Unlabelled Image • An intelligent system is developed for diagnosis of solder bumps. • SAM image segmentation is conducted using the correlation coefficient. • Four statistical features are extracted for each solder bump. • The well trained GRNN is used for classifying the solders. • SAM combined with GRNN achieved high recognition accuracy. [ABSTRACT FROM AUTHOR]

Micro solder bump has been widely used in electronic packaging. Currently a number of flip-chip products are developing towards miniaturization with more I/Os at finer pitch, and defect inspection of the high density package is increasingly challenging. In this paper, the Levenberg-Marquardt back-propagation network (LM-BP) combined with the scanning acoustic microscopy technology was investigated for intelligent diagnosis of solder defect. The flip chips were detected by using a 230 MHz ultrasonic transducer. Solder bumps were segmented from the SAM image. The statistical features were extracted and fed into the LM-BP networks for bump classification. The results demonstrate that LM-BP algorithm reached a high recognition accuracy, and is effective for defect inspection of the micro solder bump. [ABSTRACT FROM AUTHOR]