Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network

Abstract

The detection of conductive particle images is an important part of the chip on glass (COG) process and can be used to ensure the performance of electrical connections. The segmentation of conductive particles is essential but a difficult task, since the scale and edge of the conductive particles on the chip and the imaging effect are different. In recent years, methods based on deep learning have become the representative method of image segmentation. However, the currently existing methods cannot fully consider the characteristics of conductive particles and have high model complexity. In this article, a multi-frequency feature learning-based convolutional neural network (CNN) is proposed. The entire network structure consists of a basic U-Net module and multi-frequency module (MFM), which are used to enhance multi-frequency feature fusion of conductive particles and accelerate network training. At the same time, for the feature of particle shape, an active contour without edge (ACWE) loss function is designed to extract the fine contour feature of particles. Experimental results on three datasets show the superiority of the proposed method over the major existing mainstream methods with respect to the three performance indicators: recall, precision, and Intersection-over-Union (IoU).