Automatic detection of defects in electronic plastic packaging using deep convolutional neural networks

Abstract

As the mainstream chip packaging technology, plastic-encapsulated chips (PEC) suffer from process defects such as delamination and voids, which seriously impact the chip's reliability. Therefore, it is urgent to detect defects promptly and accurately. However, the current manual detection methods cannot meet the application's requirements, as they are both inaccurate and inefficient. This study utilized the deep convolutional neural network (DCNN) technique to analyze PEC's scanning acoustic microscope (SAM) images and identify their internal defects. First, the SAM technology was used to collect and set up datasets of seven typical PEC defects. Then, according to the characteristics of densely packed PEC and an incredibly tiny size ratio in SAM, a PECNet network was established to detect PEC based on the traditional RetinaNet network, combining the CoTNet50 backbone network and the feature pyramid network structure. Furthermore, a PEDNet was designed to classify PEC defects based on the MobileNetV2 network, integrating cross-local connections and progressive classifiers. The experimental results demonstrated that the PECNet network's chip recognition accuracy reaches 98.6%, and its speed of a single image requires only nine milliseconds. Meanwhile, the PEDNet network's average defect classification accuracy is 97.8%, and the recognition speed of a single image is only 0.0021 s. This method provides a precise and efficient technique for defect detection in PEC.