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

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.