Simulation and Experimental Verification of Edge Blurring Phenomenon in Microdefect Inspection Based on High-Frequency Ultrasound

Su, Lei; Yu, Xiaonan; Li, Ké; Yao, Xingyan; Pecht, Michael

doi:10.1109/access.2019.2892231

Cited by 18 publications

(5 citation statements)

References 16 publications

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“…5(a), X1 is 1D star centroid and τ st represents the maximum intensity of star. The 1D light intensity distribution of the nearby celestial body in the gradient direction satisfies blurred edge model [33]- [35]. In Fig.…”

Section: Sub-pixel Feature Extraction Based On 1d Energy Deviationmentioning

confidence: 76%

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

et al. 2019

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Celestial body features are important navigation information in deep space exploration. This study provides a synchronous high-precision extraction algorithm for star centroid and nearby celestial body edges for a miniaturized independent optical navigation sensor, which combines the functions of a star tracker and a navigation camera. The image is filtered by a ring filter template to eliminate the interference information of background and improve the contrast between the target and the background. The secondorder directional derivative and specific area characteristic method aim to roughly extract and distinguish the features (star centroid and the nearby celestial body edge). In local area template where feature points are located, the 1D energy deviation effect is proposed to extract the features of the two different light intensity distribution models. The accuracy and robustness of our algorithm are verified by simulation and groundbased experiments. The algorithm has certain reference significance for other types of dim target and edge detections, such as infrared detection, medical image, target measurement, and machine vision. INDEX TERMS Optical autonomous navigation, high robustness and accuracy, star centroid, nearby celestial body edge, 1D energy deviation effect. GUANGJUN ZHANG received the Ph.D. degree from the

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Section: Sub-pixel Feature Extraction Based On 1d Energy Deviationmentioning

confidence: 76%

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

et al. 2019

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“…Sub-images that correctly identified a missing bump are marked with '□' (blue box), while incorrect classifications are marked with '×' (red cross). Due to blurring effects occurring during acoustic imaging [29], the solder joints predicted incorrectly are mostly concentrated on the edges of the chip. There is a large difference in the distribution of gray values between the edge solder joints and the others.…”

Section: Cnn-based Classification Of Solder Jointsmentioning

confidence: 99%

Using convolutional neural network for intelligent SAM inspection of flip chips

Wang¹,

Lu²,

He³

et al. 2021

Meas. Sci. Technol.

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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.

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“…However, an ultrasonic transducer with medium frequency band is adopted in the SAM test to ensure the ultrasound can penetrate inside the package, which decreases the detection resolution and accuracy. The edge effect blurs the periphery of SAM image and thus deteriorates the case further [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Coefficients Fusion Strategy for Enhancement of SAM Image in Solder Joints Detection

Wang

et al. 2023

Preprint

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IC packaging technology becomes more complex with the increase of package density, and defect diagnosis of IC devices is getting more challenging. Scanning acoustic microscopy (SAM) is widely used in electronic industry. But the detection resolution is limited by the penetration depth of ultrasound. So it is of great necessity finding a way to improve the resolution and accuracy. A new strategy of multi-scale decomposition and fusion based on the wavelet transform has been 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 a low frequency band (cAd1 and cAd2) and three high frequency bands (cHd1, cVd1, cDd1, and cHd2, cVd2, cDd2), which were then fused respectively based on the local area energy. The derived new coefficients were used to reconstruct a high resolution SAM image. A genetic algorithm modified back propagation network (GA-BP) was used for classifying the solder joints. The proposed scheme achieved highest recognition accuracy (97.16%) compared with other methods. The new strategy of image enhancement provides a stable and effective solution for SAM inspection of electronic devices.

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Simulation and Experimental Verification of Edge Blurring Phenomenon in Microdefect Inspection Based on High-Frequency Ultrasound

Cited by 18 publications

References 16 publications

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

Using convolutional neural network for intelligent SAM inspection of flip chips

Multi-scale Coefficients Fusion Strategy for Enhancement of SAM Image in Solder Joints Detection

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