Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms

A Neural Network Approach for Non-contact Defect Inspection of Flat Panel Displays

AbstractThis paper proposes a neural network-based approach for the inspection of electrical defects on thin film transistor lines of flat panel displays. The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor-based non-contact sensor by scanning over thin film transistor lines on the surface of the mother glass of flat panels. The sudden deep falls (open circuits) or sharp rises (short circuits) on the captured noisy waveform are classified and detected by employing a four-layer feed-forward neural network with two hidden layers. The topology of the network comprises an input layer with two units, two hidden layers with two and three units, and an output layer with one unit; a standard sigmoid function as the activation function for each unit. The network is trained with a fast adaptive back-propagation algorithm to find an optimal set of associated weights of neurons by feeding a known set of input data. The ambiguity of the threshold definition does not arise in this method because it uses only local features of waveform data at and around selected candidate points as inputs to the network, unlike the existing thresholding-based method, which is inherently prone to missed detections and false detections

Design and development of a AOI device control system for TFT-LCD electrode line defect detection

近年来，伴随着电子信息产业的蓬勃发展，薄膜场效应管液晶显示屏（ThinFilmTransistor-LiquidCrystalDisplay，TFT-LCD）的工艺技术也不断进步和提升，使得液晶外围电极线路的尺寸越来越小，已达到微米数量级，相应的缺陷尺寸缩小至亚微米量级。为满足TFT-LCD高质量高效率的生产要求，AOI（AutomaticOpticInspection）技术已开始用于TFT-LCD缺陷检测的相关工艺中，用来取代传统人工目视检测。而国内用于液晶屏电极线路检测的AOI研究尚未见报道，AOI设备主要依赖国外进口。因此弥补TFT-LCD外围电极线路缺陷检测技术的缺失，实现TFT-LC...In recent years, with the rapid development of electronic information industry, TFT-LCD (Thin Film Transistor-Liquid Crystal Display) technology continues to progress and improve, making the size of LCD-defect down to sub micron level. In order to meet the requirements of high quality and high efficiency of TFT-LCD, AOI (Automatic Optic Inspection) technology has been used in the process of TFT-LC...学位：工学硕士院系专业：物理科学与技术学院_工程硕士(电子与通信工程)学号：1982014115298

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

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, micro-electronics, 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.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Automated industrial inspection workbench for human machine interface (HMI) consoles

The actual moment of the industrial production is changing the way of production. Now the systems are adaptable to produce different items in the same production line with a very reduced time to setup the systems. In the same way, the quality control systems must be more adaptable and intelligent possible. The present work propose the creation of intelligent and adaptable inspection cell to inspect Human Machine Interface (HMI) consoles of different types. This cell is composed by an image acquisition system with controlled illumination, a force sensor installed on the robot tool to verify the buttons’ functionality. The force tests are processed and classified using decision three, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classification method. Besides, the Thin-Film Transistor (TFT) display uses Normalized Cross-Correlation (NCC) and Correlation Coefficients (CC) to check the display’s regions. To Liquid Cristal Display (LCD) is used the same method and also be used a Neural Network Classification (NNC). In the experimental tests, four different types of consoles prototypes are tested, one of them has a TFT display and buttons, others two have only buttons and one has only a LCD display. In the inspection workbench is created, all the hardware necessary to execute the inspection was installed successfully. Moreover, the inspection methods obtained a precision higher than 90% to the buttons and display inspection.O momento atual produção industrial está mudando a forma de produzir. Agora os sistemas são adaptativos para produzir diferentes itens na mesma linha de produção com tempo de mudança ou customização muito reduzido. No mesmo sentido, os sistemas de controle de qualidade devem ser o mais adaptativo e inteligente possível. O presente trabalho propõe o desenvolvimento de célula de inspeção inteligente e adaptativa para inspectionar consoles de Human Machine Interface (HMI) de diferentes tipos. Esta célula é composta por um sistema de aquisição de imagem com iluminação controlada, um sensor de força instalado na ferramenta de um manipulador para verificar a funcionalidade dos botões. Os testes de força são processados e classificados usando métodos de aprendizagem de máquina, nomeadamente, decision tree, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Além disso,é utilizada a Nomalized Cross-Correlation e Correlation Coefficients para checar as regiões do display do tipo Thin-Film Transistor (TFT). Em displays do tipo Cristal Líquido (LCD) é utilizado o mesmo metodo, sendo também utilizada a classificação usando Rede Neurais. Nos testes experimentais, foram testados quatro tipos de consoles HMI, sendo que um deles possui um display de TFT e botões, outros dois possuem somente botões e um tem somente um display de LCD. Na bancada de inspeção criada, foi devidamente instalado todo o hardware necessário para execução da inspeção. Além do mais, obteve-se precisão acima de 90% para os métodos de inspeção dos botões e displays

Testing of displays of protection and control relays with machine vision

Human-machine interface is the link between a user and a device. In protection and control relays the local human machine interface consist of a display, buttons, light-emitted diode indicators and communication ports. Human-machine interfaces are tested before assembly with visual inspection to ensure quality of LCDs and LEDs. The visual inspection test system of HMIs consists of a camera and lens, a light emitted diode analyser, software and a computer. Machine vision operations, such as corner detection and template matching, are used to process and analyse captured images. Original camera and measurement device set-up have been used several years, and it should be upgraded. New camera and lens were installed in the system, and the aim of the thesis was to evaluate and improve the testing set-up and software to support each other, to get better images, and further, to improve the first pass yield. Camera position and settings were adjusted to capture images with good quality. Features of upgraded set-up and software were tested, and development ideas are given for further improvement. Changes in the set-up and software show promising results by giving more accurate test results from production.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format