# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 1 Introduction  
Surface mount technology (SMT) is a manufacturing method used to produce electronic circuits by assembling components onto the surface of a printed circuit board (PCB). Using SMT, solder paste is applied to the surface of a circuit board, a surface mount component (SMC) is installed, and the solder is then melted to produce the PCB. The automated optical inspection (AOI) machine of an SMT line inspects for assembly defects in the PCB, using a camera and classifies defects according to type. After classification, defects are used for maintenance tasks to identify root causes in the assembly and to improve the process sequence. In recent years, 3D AOI has become widespread, although 2D AOI remains commonly used due to its high economic efficiency. Figure 1 shows a defect in a SMT assembly line obtained using a light-emitting diode (LED) light system. SMT assembly defects can occur either in the package or in the solder region. The defect islabeled as missing when the chip component is not in place according to the PCB design. The Manhattan defect occurs when the chip component is erected horizontally, and the Tombstone defect occurs when the chip component stands vertically on one solder, with the other side being separated from the solder. An over-soldering defect occurs when the amount of solder is higher than normal; an under-soldering defect occurs when the solder is less than a normal defect. The missing component, Manhattan defect and Tombstone defect can be considered package defects, while over-soldering and under-soldering are defects in the solder region. Two-dimensional AOI-based inspection methods include printed circuit board inspection, component detection, and defect classification [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. In this paper, the defect classification method in the SMT line can be divided into non-learning-based and learning-based methods. Non-learning-based defect classification compares the threshold values of the template, features, and so on [4; 5; 6; 7; 8; 9]. For instance, [PERSON] [4] proposed a defect classification method using the difference in images between the input image and the reference image. First, the variance of gray \(\sigma_{i}\) of each defect is obtained from the training image dataset. Then, differences between a reference image for each defect and the input image are obtained. [PERSON] [4] classified defects by comparing the differences between images and the variance of gray \(\sigma_{i}\) of each defect. [PERSON] [5] proposed a method for discriminating defects by extracting a chip component region. They segmented the chip electrode region by applying the Otsu binarization algorithm to the red color channel of the chip component image. They first extracted the vertical projection and horizontal projection histograms from the binarized image, and then extracted the location of the chip component using the gradient of the vertical and horizontal projection histograms. Defect identification is performed by comparing the extracted chip component's location with the PCB design. [PERSON] [6] proposed a method for classifying defects in IC chip leads. They divided the IC chip leads into subregions and obtained the ratio and center of gravity of the highlight regions from each subregion. A highlight region is a set of pixels above a certain pixel threshold in a subregion. Each subregion and its features are defined as 'logical features'. A decision tree based on logical features is then used to classify defects. Learning-based methods classify defects through features such as the average pixel value of each red, green and blue (RGB) channel and the highlight value of each RGB channel [10; 11; 15; 16], and normalized-cross-correlation (NCC) [10; 11] extracted from massive chip component defect datasets obtained using an LED light system. The model analyzes and learns these features then classifies the new input data [10; 11; 12; 13; 14; 15; 16].