Accelerated Accurate In-line Solder Joint Inspection Technique

This paper reviews the entire vision inspection cycle, encompassing image acquisition, image enhancement, Region of Interest (ROI) localization and segmentation, features extraction followed by defect detection and classification. The aim of the study is to identify potential image processing time saving. The investigation innovatively suggests that optimizing image enhancement and ROI localization processing time could significantly accelerate the overall inspection cycle time without negatively impacting inspection accuracy. In Automated Optical Inspection (AOI) machine, camera sensor is mounted on precision X-Y gantries. To acquire images for inspection, the gantries will accurately move the camera to the predetermined coordinate position as stipulated in the inspection program. The vision camera will then capture the desired image using specified Field of View (FOV). Only ROI which is the solder joint position will be extracted out from the FOV image for processing. Meanwhile, the designated solder joint positions (i.e. solder pad coordinates) for all electronic components mounted on the PCB are priory known extracted from the PCB fabrication file. These coordinates can be used directly for ROI localization without employing any algorithm, and yet accuracy is not compromised. Meanwhile, through leveraging the state-of-art vision hardware, namely high-resolution camera and adaptive lighting system, quality images can be acquired and used directly without the need for any enhancement. Comparison analysis based on industrial PCB having 1000 electronics components (with 3000 solder joints of size 140x70 pixels per joint), the processing time utilizing NVIDIA GeForce RTX 2060 series Graphic Processing Unit (GPU) and Template Matching Algorithm for ROI localization needs 2 seconds. whereas when using Multiscale Morphology Algorithm for image enhancement, time required is approximately 3 seconds. Benchmarking of a typical production line with bottleneck cycle time of 25 seconds, indicating that the proposed methodology effectively addresses the challenges faced while implementing real-time machine vision inspection system in the industry, aligned with Industrial 4.0 Smart Manufacturing initiatives.