Automated Optical Inspection Of Multilayer Printed Circuit Boards

1980 ◽  
Author(s):  
William A. Bentley
Keyword(s):  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Optical Inspection ◽  
Automated Optical Inspection

Fast Precise Positioning of Circular Mark in Automatic Optical Inspection

2013 ◽  
Vol 325-326 ◽  
pp. 1614-1618
Author(s):  
Guang Jie Xiong ◽  
Yu Fei Liu ◽  
Rui Zhen Liu
Keyword(s):  
Printed Circuit Boards ◽  
Positioning Error ◽  
Circuit Boards ◽  
Precise Positioning ◽  
Automatic Optical Inspection ◽  
Running Time ◽  
Printed Circuit ◽  
Optical Inspection ◽  
Positioning Method ◽  
The Difference

Captured circular marks are deformed sometimes when Automatic Optical Inspection (AOI) is used to detect various defects on Printed Circuit Boards (PCB), which may affect the precision of inspection. A new accurate positioning method of circular marks is proposed to solve the problem by obtaining the center of the most round ellipse based on the criterion that the ratio of the difference between the length and width of its circumscribed rectangle and the width of the rectangle is less than 0.1. The simulation tests show that, if the mark has much more deformations, the center positioning error of the proposed algorithm is about 0.013 pixels, and the running time is less than 40ms. Therefore, the proposed method provides good characteristics such as speediness, strong anti-interference ability and robustness.

scholarly journals A FPGA implementation of solder paste deposit on printed circuit boards error detector based in a bright and contrast algorithm

2007 ◽  
Vol 5 (02) ◽  
Author(s):  
A. De Luca-Pennacchia ◽  
M. Á. Sánchez-Martí­nez
Keyword(s):  
Printed Circuit Boards ◽  
Computational Time ◽  
Inspection System ◽  
Solder Paste ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Optical Inspection ◽  
Error Detector ◽  
Time Requirements ◽  
Computational Time Cost

Solder paste deposit on printed circuit boards (PCB) is a critical stage. It is known that about 60% of functionality defects in this type of boards are due to poor solder paste printing. These defects can be diminished by means of automatic optical inspection of this printing. Actually, this process is implemented by image processing software with its inherent high computational time cost. In this paper we propose to implement a high parallel degree image comparison algorithm suitable to be implemented on FPGA, which could be incorporated to an automatic inspection system. The hardware implementation of the algorithm allows us to fulfill time requirements demanded by industry.

Optimal Camera Path Planning for the Inspection of Printed Circuit Boards Using a Two Stepped Optimization Approach

2010 ◽  
Author(s):  
Zainab Hermes ◽  
Ashraf O. Nassef ◽  
Lotfi K. Gaafar
Keyword(s):  
Printed Circuit Boards ◽  
Acquisition Time ◽  
Optimization Approach ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Clustering Problem ◽  
Field Of Vision ◽  
Automated Optical Inspection ◽  
Minimum Number ◽  
Work Breaks

Automated Optical Inspection (AOI) systems are rapidly replacing slow and tedious manual inspections of Printed Circuit Boards (PCBs). In an AOI system, a minicamera traverses the PCB in a pre-defined travel path, snapping shots of all the PCB components or nodes, at pre-defined locations. The images are then processed and information about the different nodes is extracted and compared against ideal standards stored in the AOI system. This way, a flawed board is detected. Minimizing both the number of images required to scan all the PCB nodes, and the path through which the camera must travel to achieve this, will minimize the image acquisition time and the traveling time, and thus the overall time of inspection. This consequently both reduces costs and increases production rate. This work breaks down this problem into two sub-problems: The first is a clustering problem; the second a travelling salesman sequencing problem. In the clustering problem, it is required to divide all the nodes of a PCB into the minimum number of clusters. The cluster size is constrained by the given dimensions of the camera’s scope or Field of Vision (FOV). These dimensions determine the dimension of the inspection windows. It is thus required to find the minimum number of inspection windows that will scan all the nodes of a PCB, and their locations. Genetic algorithms are applied in a two-step approach with special operators suited for the problem. A continuous Genetic Algorithm (GA) is applied to find the optimum inspection window locations that cover one node and as many other nodes as possible. A discrete GA is then applied to eliminate redundant inspection windows leaving the minimum number of windows that cover all nodes throughout the PCB. In the second sub-problem, an Ant Colony Optimization (ACO) method is used to find the optimum path between the selected inspection windows. The method proposed in this paper is compared against relevant published work, and it is shown to yield better results.

scholarly journals A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

2021 ◽  
Vol 7 ◽  
Author(s):  
Ameya Kulkarni ◽  
Chengying Xu
Keyword(s):  
Deep Learning ◽  
Supply Chains ◽  
Printed Circuit Boards ◽  
Learning Approach ◽  
Hardware Trojan ◽  
Circuit Boards ◽  
Hardware Trojans ◽  
Research Attention ◽  
Printed Circuit ◽  
Optical Inspection

Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipment. Security compromise or data theft can have severe impact and thus demand research attention. The advantage of the proposed method is that it can be implemented in a manufacturing environment with limited training data. It can also provide better coverage in detection of hardware trojans over traditional methods. Image recognition algorithms need to have deeper penetration inside the training layers for recognizing physical variations of image patches. However, traditional network architectures often face vanishing gradient problem when the network layers are added. This hampers the overall accuracy of the network. To solve this a Residual network with multiple layers is used in this article. The ResNet34 algorithm can identify manufacturing tolerances and can differentiate between a manufacturing defect and a hardware trojan. The ResNet operates on the fundamental principle of learning from the residual of the output of preceding layer. In the degradation issue, it is observed that, a shallower network performs better than deeper network. However, this is with the downside of lower accuracy. Thus, a skip connection is made to provide an alternative path for the gradient to skip forward the training of few layers and add in multiple repeating blocks to achieve higher accuracy and lower training times. Implementation of this method can bolster automated optical inspection setup used to detect manufacturing variances on a printed circuit board. The results show a 98.5% accuracy in optically detecting trojans by this method and can help cut down redundancy of physically testing each board. The research results also provide a new consideration of hardware trojan benchmarking and its effect on optical detection.

An Approach to Detection of Solder Joints on Printed Circuit Boards

2013 ◽  
Vol 712-715 ◽  
pp. 2364-2367
Author(s):  
Xiao Su Tong
Keyword(s):  
Euclidean Distance ◽  
Printed Circuit Boards ◽  
Printed Circuit Board ◽  
Solder Joints ◽  
Circuit Board ◽  
Image Similarity ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Optical Inspection ◽  
Different Types

To automate present Automatic Optical Inspection (AOI) systems, an intelligent method based on lightness analysis for solder joints detection is proposed in this paper. Firstly, the image taken from real printed circuit board (PCB) is preprocessed and segmented. Then, we compute the image similarity by calculating the Euclidean distance. If the similarity exceeds the threshold, we extract the lightness of solder joints by converting between RGB color mode and HLS color mode. Lastly, all the defects of solder joints are classified into several different types according to their lightness appearances. Experiment results show that the method deduced in this paper can detect accurately solder defects from different patches.

METHODS OF IMPROVING QUALITY OF PATTERN RECOGNITION BY OPTICAL INSPECTION SYSTEMS WHEN MOUNTING ELECTRONIC COMPONENTS

2019 ◽  
pp. 27-30
Author(s):  
A. A. Korneev ◽  
S. A. Glebov
Keyword(s):  
Printed Circuit Boards ◽  
The Body ◽  
Visual Images ◽  
Circuit Boards ◽  
Electronic Components ◽  
Printed Circuit ◽  
Optical Inspection ◽  
Inspection Systems ◽  
And Performance

The paper considers various systems for monitoring the installation of radio‑electronic devices, which includes both optical and combined systems. Also, the latest complexes of 3D automatic optical inspection (AOI) of the quality of installation of electronic components on printed circuit boards (PCB), which increase the accuracy and performance of the test and eliminate many defects during installation of components that are not available in the 2D inspection mode, are considered. Such defects include the height of the solder fillet, the angle of inclination of the body of the element, the height of the raising of the pins. The paper proposes a technique to improve the perception of visual images of components by applying several graphic images of the same component, presented in three additive color models. Improving the recognition of the outlines of components allows you to eliminate defects during the inspection at the stage of inspection of defects, while the electronic units can be repaired.

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