• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.12-21
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• 2003

• Journal of Radiation Industry
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• v.9 no.3
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• pp.153-159
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• 2015

Reliability of printed circuit board (PCB), which is based on high integrated circuit technology, is having been important because of development of electric and self-driving car. In order to answer these demand, automated X-ray inspection (AXI) is best solution for PCB non-destructive test. PCB is consist of plastic, copper, and, lead, which have low to high Z-number materials. By using dual energy X-ray imaging, these materials can be inspected accurately and efficiently. Dual energy X-ray imaging, that have the advantage of separating materials, however, need some solution such as energy separation method and enhancing efficiency because PCB has materials that has wide range of Z-number. In this work, we found out several things by analysis of X-ray energy spectrum. Separating between lead and combination of plastic and copper is only possible with energy range not dose. On the other hand, separating between plastic and copper is only with dose not energy range. Moreover the copper filter of high energy part of dual X-ray imaging and 50 kVp of low energy part of dual X-ray imaging is best for efficiency.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.126-130
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• 2021

The ball grid array is one of the packaging methods that used in high density printed circuit board. Solder void defects caused by voids in the solder ball during the BGA process do not directly affect the reliability of the product, but it may accelerate the aging of the device on the PCB layer or interface surface depending on its size or location. Void inspection is important because it is related in yields with products. The most important process in the optical inspection of solder void is the segmentation process of solder and void. However, there are several segmentation algorithms for the vision inspection, it is impossible to inspect all of images ideally. When X-Ray images with poor contrast and high level of noise become difficult to perform image processing for vision inspection in terms of software programming. This paper suggests the solution to deal with the suggested problem by means of using Mask R-CNN instead of digital image processing algorithm. Mask R-CNN model can be trained with images pre-processed to increase contrast or alleviate noises. With this process, it provides more efficient system about complex object segmentation than conventional system.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.202-209
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• 2004

Quality control is essential to the final product in BGA-type PCB fabrication. So, many automatic vision systems have been developed to achieve speedy, low cost and high quality inspection. A multiple threshold selection algorithm is a very important technique for machine vision based inspection. In this paper, an inspected image is modeled by using fuzzy sets and then the parameters of specified membership functions are estimated to be in maximum fuzzy entropy with the probability of the fuzzy sets, using the exhausted search method. Fuzzy c-partitions with the estimated parameters are automatically generated, and then multiple thresholds are selected as the crossover points of the fuzzy sets that form the estimated fuzzy partitions. Several experiments related to flip chip BGA images show that the proposed algorithm outperforms previous ones using both entropy and variance, and also can be successfully applied to AVI systems.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.982-987
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• 2015

The inspection machine in SMT (Surface Mount Technology) line detects the assembly defects such as missing, misalignment, loosing, or tombstone. We propose a new method to classify the defect types of chip components by processing the image of PCB. Two original images are obtained from horizontal lighting and vertical lighting. The image of the component is divided into two soldering regions and one packaging region. The features are extracted by appling the PCA (Principle Component Analysis) to each region. The MLP (Multilayer Perceptron) and SVM (Support Vector Machine) are then used to classify the defect types by learning. The experimental results are presented to show the usefulness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.384-391
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• 1998

When a device is mounted on the PCB, it is impossible to have zero defects due to many unpredictable problems. Among these problems, devices with bent corner leads due to mis-handling and which are not placed at a given point measured along the axis are principal problem in SMT(Surface Mounting Technology). It is obvious that given the complexity of the inspection task, the efficiency of a human inspection is questionable. Thus, new technologies for inspection of SMD(Surface Mounting Device) should be explored. An example of such technologies is the Automated Visual Inspection(AVI), wherein the vision system plays a key role to correct this problem. In implementing vision system, high-speed and high-precision are indispensable for practical purposes. In this paper, a new algorithm based on the Radon transform which uses a projection technique to inspect the FIC(Flat Integrated Circuit) device is proposed. The proposed algorithm is compared with other algorithms by measuring the position error(center and angle) and the processing time for the device image, characterized by line scan camera.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.916-921
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• 2009

The technology to inspect and measure an inner structure of micro parts has become an important tool in the semi-conductor industrial field with the development of automation and precision manufacturing. Especially, the inspection skill on the inside of highly integrated electronic device becomes a key role in detecting defects of a completely assembled product. X-ray inspection technology has been focused as a main method to inspect the inside structure. However, there has been insufficient research done on the customized inspection technology for the flip-chip assembly due to the interior connecting part of flip chip which connects the die and PCB electrically through balls positioned on the die. In this study, therefore, it is implemented to detect shape error of flip chip bonding without damaging chips using an x-ray inspection system. At this time, it is able to monitor the solder bump shape by introducing an edge-extracting algorithm (exponential approximation function) according to the attenuating characteristic and detect shape error compared with CAD data. Additionally, the bonding error of solder bumps is automatically detectable by acquiring numerical size information at the extracted solder bump edges.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.695-702
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• 2010

This paper presents an image alignment algorithm for application of AOI (Automatic Optical Inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures called the CCFMR (Cascade Classifier for False Matching Reduction) After reduced the false matching, rotation and translation are estimated by point selection method. Experimental results show that the proposed method has fewer fail matching in comparison to commercial software MIL 8.0, and specially, less than twice with the well-controlled environment’s data sets (such as AOI system). The rotation and translation accuracy is robust than MIL in the noise data sets, but the errors are higher than in a rotation variation data sets although that also meaningful result in the practical system. In addition to, the computational time consumed by the proposed method is four times shorter than that by MIL which increases linearly according to noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.20-27
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• 2004

We propose the character recognition system for Gerber files. The Gerber file is the vector-formatted drawing file for PCB manufacturing, which includes various symbols, figures and characters. Also, the characters are written in horizontal, vertical, and reverse-vortical directions. In this paper, we newly propose the Gerber-character recognition system to recognize all of component names located in PCB. To improve the performance, we develop the multiple recognizers by neural networks and the verifier considering the structural features. The developed system has been installed to the auto-programming software for PCB assembly and inspection machines.