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 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

MULTIRIDGELETS FOR TEXTURE ANALYSIS

Directional wavelets have orientation selectivity and thus are able to efficiently represent highly anisotropic elements such as line segments and edges. Ridgelet transform is a kind of directional multi-resolution transform and has been successful in many image processing and texture analysis applications. The objective of this research is to develop multi-ridgelet transform by applying multiwavelet transform to the Radon transform so as to attain attractive improvements. By adapting the cardinal orthogonal multiwavelets to the ridgelet transform, it is shown that the proposed cardinal multiridgelet transform (CMRT) possesses cardinality, approximate translation invariance, and approximate rotation invariance simultaneously, whereas no single ridgelet transform can hold all these properties at the same time. These properties are beneficial to image texture analysis. This is demonstrated in three studies of texture analysis applications. Firstly a texture database retrieval study taking a portion of the Brodatz texture album as an example has demonstrated that the CMRT-based texture representation for database retrieval performed better than other directional wavelet methods. Secondly the study of the LCD mura defect detection was based upon the classification of simulated abnormalities with a linear support vector machine classifier, the CMRT-based analysis of defects were shown to provide efficient features for superior detection performance than other competitive methods. Lastly and the most importantly, a study on the prostate cancer tissue image classification was conducted. With the CMRT-based texture extraction, Gaussian kernel support vector machines have been developed to discriminate prostate cancer Gleason grade 3 versus grade 4. Based on a limited database of prostate specimens, one classifier was trained to have remarkable test performance. This approach is unquestionably promising and is worthy to be fully developed

Microstructure Analysis and Surface Planarization of Excimer-laser Annealed Si Thin Films

The excimer-laser annealed (ELA) polycrystalline silicon (p-Si or polysilicon) thin film, which influences more than 100-billion-dollar display market, is the backplane material of the modern advanced LCD and OLED products. The microstructure (i.e. ELA microstructure) and surface morphology of an ELA p-Si thin film are the two main factors determining the material properties, and they significantly affect the performance of the subsequently fabricated thin film transistors (TFTs). The microstructure is the result of a rather complex crystallization process during the ELA which is characterized as far-from-equilibrium, multiple-pulse-per-area and processing-parameter dependent. Studies of the ELA microstructure and the surface morphology closely related to the device performance as well as the microstructure evolution during the ELA process are long-termly demanded by both the scientific research and the industrial applications, but unfortunately have not been thoroughly performed in the past. The main device-performance-related characteristics of the ELA microstructure are generally considered to be the grain size and the presence of the dense grain boundaries. In the work of this thesis, an image-processing-based program (referred to as the GB extraction program) is developed to extract the grain boundary map (GB map) out of the transmission electron microscope (TEM) images of the ELA microstructure. The grain sizes are straightforwardly calculated from the GB map and statistically analyzed. More importantly, based on the GB maps, we propose and perform a rigorous scheme that we call the local-microstructure analysis (LMA) to quantitatively and systematically analyze the spatial distribution of the grain boundaries. The “local area” is mainly defined by the geometry and the location of a TFT. The successful extraction of the GB map and the subsequent LMA are permitted by our unique TEM skills to produce high-resolution TEM micrographs containing statistically significant number of grains for sensible quantitative analysis. The LMA unprecedentedly enables quantitative and rigorous analysis of spatial characteristics of the microstructure, especially the device geometry- and location-related characteristics. Additionally, we present and highlight the benefits of the LMA approach over the traditional statistical grain-size analysis of the ELA microstructure. From the grain-size analysis, we find that grain size across a statistically significant number of grains generally follows the same distribution as in the stochastic grain growth scenario at the beginning of the ELA process when the laser pulse (i.e. shot) number is small. As the shot number increases, the overall grain size monotonically increases while the distribution profile becomes broader. When the scan number reaches the ELA threshold (several tens of laser shots), the distribution profile substantially deviates from the stochastic profile and shows two sharp peaks in grain size around 300nm and 450nm, which is consistent with the previously proposed theory of energy coupling and nonuniform energy deposition during ELA. From the LMA, local nonuniformity of grain boundary density (GB density) at the device length scales and regions of high grain boundary periodicity are identified. More importantly, we find that the local nonuniformity is much more pronounced when p-Si film exhibits some level of spatial ordering, but less pronounced for a random grain arrangement. It is worth noting that the devices of different sizes and orientation have different sensitivity to the local nonuniformity of the ELA-generated p-Si thin film. In addition, based on the analysis results, the connection between the microstructure evolution and the partial melting and resolidification process of the Si film is discussed. Aside from the microstructure, the surface morphology of the ELA films, featuring pronounced surface protrusions, is characterized via an atomic force microscope (AFM). Attempts to planarize those surface protrusions detrimental to the subsequent device performance are conducted. In the attempts, the as-is (oxide-capped) ELA films and the BHF-treated ELA films are subjected to single shots of excimer irradiation. When the results are compared, an anisotropic melting phenomenon of the p-Si grains is identified, which appears to be strongly affected by the presence of the surface oxide capping layer. Conceptual models are developed and numerical simulations are employed to explain the observation of the anisotropic melting phenomenon and the effect of the surface oxide layer. Eventually, 41.8% reduction of root mean square (RMS) surface roughness is achieved for BHF-treated ELA films. The results gained in the systematic analysis of the ELA microstructure and the attempt of surface planarization further our understanding about (1) the device performance-related material microstructure of the ELA p-Si thin films, (2) the microstructure evolution occurring during multiple shots of the ELA process, and (3) the fundamental phase transformations in the far-from-equilibrium melt-mediated excimer-laser annealing processing of p-Si thin films. Such understanding could help engineers when designing the microelectronic devices and the ELA manufacturing process, as well as provide scientific researchers with insights on the melting and solidification of general polycrystalline materials, thus profoundly contributing to both the related scientific society and the technological community. The GB extraction program and the LMA scheme developed and demonstrated in the thesis, as another contribution to the related research filed, could also be generalized to the microstructural study of other polycrystalline materials where grain geometry and arrangement are of concern

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

Amorphous In-Ga-Zn-O Thin-Film Transistors for Next Generation Ultra-High Definition Active-Matrix Liquid Crystal Displays.

Next generation ultra-high definition (UHD) active-matrix flat-panel displays have resolutions of 3840x2160 (4K) or 7680x4320 (8K) pixels shown at 120 Hz. The UHD display is expected to bring about immersive viewing experiences and perceived realness. The amorphous In-Ga-Zn-O (a-IGZO) thin-film transistor (TFT) is a prime candidate to be the backplane technology for UHD active-matrix liquid crystal displays (AM-LCDs) because it simultaneously fulfills two critical requirements: (i) sufficiently high field-effect mobility and (ii) uniform deposition in the amorphous phase over a large area. We have developed a robust a-IGZO density of states (DOS) model based on a combination of experimental results and information available in the literature. The impact of oxygen partial pressure during a-IGZO deposition on TFT electrical properties/instability is studied. Photoluminescence (PL) spectra are measured for a IGZO thin films of different processing conditions to identify the most likely electron-hole recombination. For the first time, we report the PL spectra measured within the a IGZO TFT channel region, and differences before/after bias-temperature stress (BTS) are compared. To evaluate the reliability of a-IGZO TFTs for UHD AM-LCD backplane, we have studied its ac BTS instability using a comprehensive set of conditions including unipolar/bipolar pulses, frequency, duty cycle, and drain biases. The TFT dynamic response, including charging characteristics and feedthrough voltage, are studied within the context of 4K and 8K UHD AM-LCD and are compared with hydrogenated amorphous silicon technology. We show that the a-IGZO TFT is fully capable of supporting 8K UHD at 480 Hz. In addition, it is feasible to reduce a-IGZO TFT feedthrough voltage by controlling for non-abrupt TFT switch-off.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111526/1/ekyu_1.pd

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Calibration Methods of Characterization Lens for Head Mounted Displays

This thesis concerns the calibration, characterization and utilization of the HMD Eye, OptoFidelity’s eye-mimicking optical camera system designed for the HMD IQ, a complete test station for near eye displays which are implemented in virtual and augmented reality systems. Its optical architecture provides a 120 degree field of view with high imaging performance and linear radial distortion, ideal for analysis of all possible object fields. HMD Eye has an external, mechanical entrance pupil that is of the same size as the human entrance pupil. Spatial frequency response (the modulation transfer function) has been used to develop sensor focus calibration methods and automation system plans. Geometrical distortion and its relation to the angular mapping function and imaging quality of the system are also considered. The nature of the user interface for human eyes, called the eyebox, and the optical properties of head mounted displays are reviewed. Head mounted displays consist usually of two near eye displays amongst other components, such as position tracking units. The HMD Eye enables looking inside the device from the eyebox and collecting optical signals (i.e. the virtual image) from the complete field of view of the device under test with a single image. The HMD Eye under inspection in this thesis is one of the ’zero’ batch, i.e. a test unit. The outcome of the calibration was that the HMD Eye unit in this thesis is focused to 1.6 m with an approximate error margin of ±10 cm. The drop of contrast reaches 50% approximately at angular frequency of 11 cycles/degree which is about 40% of the simulated values, prompting improvements in the mechanical design. Geometrical distortion results show that radial distortion is very linear (maximum error of 1%) and that tangential distortion has a diminishable effect (0.04 degrees of azimuth deviation at most) within the measurement region