Statistical Process Monitoring System for SMT Industry Using Automatic Optical Inspection System

Abstract

As the demand of higher throughput in high volume surface mounting technology (SMT) industry, inspection and testing have been notably emphasized. To alleviate concerns associated with board level soldering inspection, automatic optical inspection (AOI) has been actively used in SMT industry [1]. In this paper, statistical quality control method has been applied for board level inspection to maximize the performance of a commercially available AOI system. Considering its complication of SMT assembled board, implementing the quality control scheme for the measured variable data is fairly expensive. However, the proposed system efficiently utilizes both attribute and variable data collected for the daily/weekly based production yield reports, and further utilize as a method for in-line diagnostics in SMT manufacturing process.

Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

“…With the development of cameras and computers, digital image quality is getting higher and higher, and computers are becoming more and more capable of image processing. Scholars have found that detection of welding defects by image processing can achieve higher accuracy and efficiency, thus an automatic optical inspection (AOI) system [4][5][6][7][8] was proposed. Since all solder joints on the printed circuit board (PCB) are fixed, with quite different solder color, brightness, and other features from the background plate, it is relatively easy to the corresponding algorithm to extract the solder joint features, so as to analyze the solder joint quality.…”