Feature-Extraction-Based Inspection Algorithm for IC Solder Joints

Wu, Fupei; Zhang, Xianmin

doi:10.1109/tcpmt.2011.2118208

Cited by 42 publications

(11 citation statements)

References 14 publications

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“…To validate our proposed method, we compare it with some state-of-the-art inspection methods, which are SAM (Xie et al, 2009), SDL (Wu and Zhang, 2011), ViBe-based (Cai et al, 2015) and RPCA-based (Cai et al, 2017;Song et al, 2019;and Wu and Xu, 2018). The comparison results are shown in Table 2.…”

Section: Comparisons With the State-of-the-art Inspection Methodsmentioning

confidence: 99%

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Chen

Cai

Wang

et al. 2020

SSMT

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Purpose Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects. Design/methodology/approach In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance. Findings Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods. Originality/value The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

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Section: Comparisons With the State-of-the-art Inspection Methodsmentioning

confidence: 99%

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Chen

Cai

Wang

et al. 2020

SSMT

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“…In the actual market, there are many kinds of PCB circuit boards and various characteristics. Different PCBs have different complex wiring design rules [10][11][12], and the defect areas that need to be detected usually only occupy a small part of the PCB circuit board, which makes manual detection very difficult.…”

Section: Introduction Pcb (Printed Circuit Boardmentioning

confidence: 99%

A Novel PCB Defect Detection Method Based on Digital Image Processing

Zhang

Cao

2023

J. Phys.: Conf. Ser.

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Printed circuit board (PCB) has a wide range of applications, and its automated quality inspection is a vital part of economic production. The time overhead of the current PCB defect detection method will increase with the increase in the number of detection targets. To ensure the stability of detection performance, this paper proposes a novel PCB detection method based on color threshold segmentation. Experiments show that this method can effectively detect and analyse various information about solder pads and solder pastes, among which the detection accuracy rate of solder pads is 99.4%, the missed detection rate is 0.4%, and the detection accuracy rate of solder paste is 99.3%, The missed detection rate is 0.03%. At the same time, the method in this paper has better robustness and stability than other PCB defect detection methods, and can better meet the needs of industrial production.

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“…In recent years, machine vision technology has been widely used in solder joint defect detection. Wu and Zhang [6] designed a series of correlation rules between different segmentation regions and solder joint types to detect defects based on the geometric features of IC solder joint images, but most of the thresholds of these rules are empirically determined, and the generalization ability needs to be improved. Song et al [7] further divided the solder joint image into detailed areas according to the structure of the PCB component to extract the average pixel value and highlight value features of the red, green and blue channels for defect recognition [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic compact camera module solder joint inspection method based on machine vision

Peng¹,

Yan²,

Chen³

et al. 2022

Meas. Sci. Technol.

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Aiming at the contradiction between the high efficiency and high quality production requirements of CCM and the low efficiency and poor accuracy of traditional solder joint inspection methods, an automatic inspection method of CCM solder joint based on machine vision is proposed. After optimizing the imaging parameters according to the CCM inspection process, the region of interest (ROI) is dynamically identified based on feature matching and image enhancement methods and remove background interference; on this basis, an improved particle swarm optimization is used to optimize the kernel extreme learning machine (APSO-KELM) to automatically classify the solder joint defects. Experimental results showed that the CCM surface solder joint defect detection and classification method based on machine vision can effectively solve the challenges of low efficiency and high cost of the current CCM solder joint defect detection technology with low latency, high precision and robustness.

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Feature-Extraction-Based Inspection Algorithm for IC Solder Joints

Cited by 42 publications

References 14 publications

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

A Novel PCB Defect Detection Method Based on Digital Image Processing

Automatic compact camera module solder joint inspection method based on machine vision

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