Defect Detection of Gold-Plated Surfaces on PCBs Using Entropy Measures

Tsai, Du‐Ming; Lin, B.-T.

doi:10.1007/s001700200172

Cited by 21 publications

(16 citation statements)

References 15 publications

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“…Jiang et al in [154] considered bulged, coarse, and wet-tinned golden fingers for inspection. Tsai et al in [155] considered defects such as pinholes, copper exposure, and nicks and roughness on gold-plated surfaces. Similarly, many articles found in literature considered PCB traces' defects.…”

Section: Pcb Defectsmentioning

confidence: 99%

“…Golden fingers [153]- [155] Traces & Inner layers [156], [192]- [194] Marking inspection [145]- [147], [181] Cosmetic & small defects [31], [194] Scratches and improper etching [195] Glue quality [196] Flux Cutting [181] Alignment and position inspection of PCB [29], [197] PCB Overall quality [188], [198]- [200] Ball Grid Array (BGA) [136], [157], [188], [201]- [209] electronics visual displays due to the variety of their applications ranging from smartphones, tablets, computer monitors, televisions (TVs), to data projectors. LCDs as the name states are made from liquid crystals, which are considered non-emissive materials and therefore does not emit light.…”

Section: Othermentioning

confidence: 99%

“…This study was one of few which considered color defects in FPDs and they achieved an accuracy up to 95.61% in detecting colored defects. Tsai and Lin in [155] developed two entropy measures to detect gold-plated surface defects in PCBs. One entropy measure uses two chromatic features derived from the CIELUV color space to detect color anomalies such as copper exposure, and the other measure uses edge angles to detect structural defects such as roughness on gold-plated surfaces.…”

Section: ) Color Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: Pcb Defectsmentioning

confidence: 99%

Section: Othermentioning

confidence: 99%

Section: ) Color Analysismentioning

confidence: 99%

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…Recently, computer vision has been applied to automatic detection equipment [17, 18]. This technology completely depends on the computer for automatic detection, and has the advantages of high data processing ability and high recognition rate.…”

Section: Related Workmentioning

confidence: 99%

Defect detection of printed circuit board based on lightweight deep convolution network

Shen

Liu

Sun

2020

IET Image Processing

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With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD-PCB, which can perform real-time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR-PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD-PCB and CR-PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components.

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“…The second is error rate, which means mistaking eligible to ineligible. The third is missed rate, which means mistaking ineligible to eligible [13]. There are 232 grapefruit sample in total for test.50 of them are ineligible sample.…”

Section: Experiments Of Grapefruit Shape Inspectionmentioning

confidence: 99%

Inspection for Grapefruit’s Shape Based on Image Processing Technology

Yang¹,

Huang²,

Hu³

et al. 2017

dtcse

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Grapefruit's shape is a most important characteristic which effects Grapefruit's classification directly. In order to realize inspection for Grapefruit's shape, the algorithm of Sobel is firstly implemented in extracting Grapefruit's contour. And then calculate directional angle of all the pixel on contour. On the basis of that, the function of Grapefruit's shape difference is established to quantize shape's difference between Grapefruits. The ineligible Grapefruit's shape is distinguished through setting shape's difference threshold. The final experiment prove its effectiveness, Grapefruit's shape inspecting accuracy rate reach 92.24%. The algorithm not only has advantage of practicality, generality and convenience but also is significant for academic research.

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Defect Detection of Gold-Plated Surfaces on PCBs Using Entropy Measures

Cited by 21 publications

References 15 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Defect detection of printed circuit board based on lightweight deep convolution network

Inspection for Grapefruit’s Shape Based on Image Processing Technology

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