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Photomask Technology 2024

作者： Fagart, Olivier Lecarpentier, Laurent Wu, Dongmei Lakkapragada, Suresh Hu, Changqing Wang, Yuehui Xie, Li Li, Derui Jiao, Jing Lei, Zeyu Polli, Marco Tolani, Vikram STMicroelectronics Crolles France KLA Corporation No. 79-80 Lane 887 Zuchongzhi Road Pudong District Shanghai China KLA Corporation Three Technology Drive MilpitasCA United States KLA Corporation Agrate Brianza20864 Italy

ISBN: (纸本)9781510681576

In advanced manufacturing fabs, mask inspection plays a pivotal role in securing the mask quality and wafer yield. A single defect on a mask can potentially impact thousands of wafers, underscoring the criticality of accurate defect disposition. While operators can confidently classify approximately 90% of defects manually, the remaining 10% are often misclassified, directly affecting production yield. As technology nodes shrink, the adoption of advanced techniques such as Optical Proximity Correction (OPC) and Sub-Resolution Assist Feature (SRAF) in mask manufacturing poses additional challenges for operators in determining defect dispositions. Additionally, the time-consuming online defect review process hampers production efficiency. To address these challenges, STMicroelectronics has adopted an intelligent reticle disposition system known as Reticle AnalyzerTM (RA). RA integrates three essential applications. First, Automated Defect Classification (ADC) ensures precise defect classification, minimizing misclassifications. By leveraging defect type, topology, and severity as outputted from advanced image processing algorithms, ADC significantly improves defect classification accuracy. Second, Defect Progressing Monitor (DPM) continuously monitors defect behavior over time. It alerts operators when defects deteriorate, enabling timely intervention to prevent yield loss. Additionally, DPM provides information on the total defect count per mask based on inspection time, allowing for early haze detection and monitoring. Third, Lithography Printability Review (LPR) assesses the potential impact of defects based on lithographic printability predictions. It simulates the aerial image on the wafer and provides comprehensive analytics to assist operators in evaluating the risks of printing. In this paper, we present the workflow of ADC, LPR, and DPM products. We discuss the qualification process during production and highlight typical production cases that showcase the ad

关键词： inspection

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A Deep Learning Based Strategies for Scene-Text VQA System used in Industrial applications: A Critical Analysis 1

A Deep Learning Based Strategies for Scene-Text VQA System u...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Agrawal, Mayank Singh Jalal, Anand Sharma, Himanshu Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350384246

Scene-Text Visual Question Answering (Scene-Text VQA) is an emerging research area that combines computer vision, natural language processing, and scene understanding. The goal of Scene-Text VQA is to develop models and algorithms that can comprehend scene text and accurately answer questions based on textual and visual information present in images. Scene-Text VQA models used in different areas such as quality control and inspection, equipment maintenance, safety compliance, supply chain visibility, industrial automation, industry 4.0, assistance for the visually impaired, document analysis, autonomous vehicles, e-commerce, smart cities, cultural heritage preservation, surveillance, and security. Scene-Text VQA models face unique challenges, including accurate text detection and recognition, understanding contextual cues, handling language complexities, fusing text and image features effectively, limited training data, biases in datasets, generalization to unseen text and scenes, computational complexity, and appropriate evaluation metrics. In this paper we will explain different methods and algorithms for Scene-Text VQA models that can comprehend scene text and accurately answer questions based on textual and visual information present in images. The authors will distinguish all the techniques of Scene-Text VQA for effectively process and comprehend the textual information present in images and show how can one technique is conflicting then the other as well. © 2024 IEEE.

关键词： inspection

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Application of artificial intelligence techniques in meat processing: A review

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JOURNAL OF FOOD PROCESS ENGINEERING 2024年第3期47卷 e14590-e14590页

作者： Wang, Mingyu Li, Xinxing China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Nanchang Inst Technol Nanchang Peoples R China China Agr Univ Beijing 100083 Peoples R China

The field of meat processing plays a critical role in the food industry and has seen increasing adoption of artificial intelligence (AI) technology with rapid technological advancements. AI technology has tremendous potential for enhancing production efficiency and product quality in meat processing. However, further research and exploration are necessary to tackle the challenges posed by the use of AI technology. This article details the implementation of AI technology in meat processing, focusing on carcass classification, automation and intelligent processing, and meat-quality detection. We aim to provide inspiration to researchers and industry professionals and promote the advancement of AI technology in the meat processing *** applicationsOur review article showcases the potential industrial applications of artificial intelligence (AI) techniques in the meat processing industry. AI technology can greatly improve production efficiency and product quality in meat processing. By implementing AI algorithms, meat processors can accurately classify carcasses, automate various processing tasks, and detect meat quality with higher accuracy. These advancements can lead to increased profitability and improved food safety in the industry. We hope to provide valuable insights for researchers and industry professionals, encouraging them to further explore and adopt AI technology in the meat processing sector. Our study delves into the application of AI technology in pivotal areas, including carcass categorization, automation, intelligent processing, and the detection of meat quality. Our study is expected to inspire further research on AI technology in the meat processing industry and provide a reference for researchers. image

关键词： artificial intelligence technology automation carcass classification meat processing quality inspection

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automation and Computational Intelligence for Road Maintenance and Management: Advances and applications 1

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2022年

作者： Hamzeh Zakeri Fereidoon Moghadas Nejad Amir H. Gandomi

ISBN: (数字)9781119800668;9781119800651

ISBN: (纸本)9781119800644

automation and Computational Intelligence for Road Maintenance and Management A comprehensive computational intelligence toolbox for solving problems in infrastructure management In automation and Computational Intelligence for Road Maintenance and Management, a team of accomplished researchers delivers an incisive reference that covers the latest developments in computer technology infrastructure management. The book contains an overview of foundational and emerging technologies and methods in both automation and computational intelligence, as well as detailed presentations of specific methodologies. The distinguished authors emphasize the most recent advances in the maintenance and management of infrastructure robotics, automated inspection, remote sensing, and the applications of new and emerging computing technologies, including artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, knowledge discovery and engineering, and more. automation and Computational Intelligence for Road Maintenance and Management explores a universal synthesis of the cutting edge in parameters and indices to evaluate models. It also includes:

Thorough introductions to management science and the latest methods of automation and the structure and framework of automation and computing intelligence
Comprehensive explorations of advanced image processing techniques, recent advances in fuzzy, and diagnosis automation
Practical discussions of segmentation and fragmentation and different types of features and feature extraction methods
In-depth examinations of methods of classification along with various developed methodologies and models of quantification, evaluation, and indexing in automation

Perfect for postgraduate students in road and transportation engineering, evaluation, and assessment, automation and Computational Intelligence for Road Maintenance and Management will also earn a place in the libraries of researc

关键词： Systems Engineering \u0026 Management

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on image and Signal processing and their applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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A Review of Machine Vision applications in Aerospace Manufacturing Quality inspection

A Review of Machine Vision Applications in Aerospace Manufac...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Li Deng Gang Liu Yilian Zhang School of Mechanical and Automotive Engineering Shanghai University Of Engineering Science Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. Machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. This paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Drilling Technological innovation Reviews Machine vision inspection Market research Sensor systems

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Spectroscopy in Textile inspection: A Review

Spectroscopy in Textile Inspection: A Review

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Rongchang Jiang Wanzhi Wen Song Chen Dandan Qu School of Information Engineering Jiangsu College of Engineering and Technology Nantong China School of Aviation and Transportation Jiangsu College of Engineering and Technology Nantong China Personnel Department Jiangsu College of Engineering and Technology Nantong China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The textile industry demands rigorous quality control and material analysis to meet stringent standards and consumer expectations. Spectroscopic techniques have emerged as indispensable tools in this domain, offering rapid, nondestructive, and increasingly sophisticated methods for textile inspection. This paper provides a comprehensive review of the evolution and application of spectroscopic techniques in textile inspection. We begin by tracing the historical progression from manual inspection methods to the adoption of spectroscopic techniques. Subsequently, we delve into the principles, advantages, and limitations of prominent spectroscopic techniques, including NIR, UV-Vis, Raman, FTIR, highlighting their specific applications in fiber identification, dye and pigment analysis, fabric structure and performance assessment, and defect detection. Furthermore, we explore the transformative impact of integrating artificial intelligence (AI) with spectroscopic methods, enabling advanced data analysis and automation for enhanced accuracy and efficiency. Finally, we address the challenges and future directions of spectroscopic techniques in textile inspection, emphasizing the ongoing pursuit of enhanced sensitivity, portability, and eco-friendly solutions to meet the evolving demands of the textile industry.

关键词： Spectroscopy Optical fiber sensors Data analysis Reviews Quality control inspection Pigments Fabrics Textile industry Defect detection

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Revolutionizing Raisin processing through Advanced Sorting and Visualization with a Deep Learning Based Support Vector Machine Model

Revolutionizing Raisin Processing through Advanced Sorting a...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Agrawal, Jhalak Singh Gill, Kanwarpartap Chauhan, Rahul Singh Pokhariya, Hemant Chythanya, Kanegonda Ravi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350361537

By using a Deep Learning-Based Support Vector Machine (SVM) model, this work presents a novel method for the sorting and visualisation of raisins. A vital component of the agricultural sector, the processing of raisins, has historically relied on labour-intensive, human error-prone manual sorting techniques. The suggested approach makes use of deep learning to improve this process' accuracy and efficiency. The Raisins dataset in the form of two classes namely Besni and Kecimen which is extracted from the Kaggle open source environment or platform, is pre-processed using the SVM model and the model is further designed accordingly so that it may predict and accuracy of 92 %. The cultural history of raisins is extensive and rich, going all the way back to antiquity. They are utilized in many different culinary applications, such as savory meals and classic desserts. Vitamins, minerals, and dietary fibre are just a few of the essential components that are concentrated in raisins. They can be used in many dishes as a healthier alternative to processed sugars because of their natural sweetness. In the context of food processing, this work investigates the use of a deep learning-based Support Vector Machine (SVM) model for sorting and displaying raisins. The manufacturing of raisins entails complex quality control procedures that have historically relied on labour-intensive techniques. This study automates the categorization of raisins according to quality factors in an effort to improve sorting accuracy and efficiency by utilizing SVMs. Known for its resilience in classification tasks, the SVM model is used to identify intricate patterns in raisin samples, which enhances sorting accuracy. Furthermore, the research delves into the visual element, illuminating the structural features of raisins and offering valuable perspectives on quality criteria. © 2024 IEEE.

关键词： Agricultural technology automation Deep learning Feature extraction image recognition Information display Models for machine learning Precision farming Preparing food Quality inspection Sorting algorithms Support Vector Machine (SVM)

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Guest Editorial: Smart Measurement in Machine Vision for Challenging applications

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2023年第8期26卷 3-3页

作者： Venkatesan, C. AI-Turjman, Fadi Pelusi, Danilo HKBK Coll Engn Dept ECE Bengaluru India Near East Univ Dept Artificial Engn Istanbul Turkiye Univ Teramo Dept CSE Teramo Italy

Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machine vision. For the measurements in these applications, sensors must be connected. Machine vision tries to creatively combine already existing technology and use them to address current issues. The term "measurement" is frequently used to refer to many tasks and is the cornerstone of industrial automation and security deployment. This Special Issue of Instrumentation & Measurement Magazine addresses some novel achievements in the measurement and instrumentation science and technology fields. It advances machine vision concerning production, application of smart materials, measurement and estimation techniques, etc. The variety of selected papers reflects the efforts made by the authors to focus either on methodological aspects or technical issues. In particular, three papers have been accepted for publication, reflecting several aspects of the abovementioned fields by covering machine vision and image processing technology.

关键词： Special issues and sections Smart devices Measurement techniques Machine vision

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Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

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OPTICS EXPRESS 2021年第12期29卷 17849-17874页

作者： Vo, Nghia T. Atwood, Robert C. Drakopoulos, Michael Connolley, Thomas Diamond Light Source Harwell Sci & Innovat Campus Didcot OX11 0DE Oxon England Brookhaven Natl Lab Natl Synchrotron Light Source 2 743 Brookhaven Ave Bldg 743 Upton NY 11973 USA

Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Digital image processing Fluorescence tomography image processing image registration Phase retrieval Phase shift

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