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Machine Learning for Automated Anomaly Detection in Semiconductor Manufacturing

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Release : 2019
Genre :
Kind : eBook
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Book Synopsis Machine Learning for Automated Anomaly Detection in Semiconductor Manufacturing by : Michael Daniel DeLaus

Download or read book Machine Learning for Automated Anomaly Detection in Semiconductor Manufacturing written by Michael Daniel DeLaus. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt: In the realm of semiconductor manufacturing, detecting anomalies during manufacturing processes is crucial. However, current methods of anomaly detection often rely on simple excursion detection methods, and manual inspection of machine sensor data to determine the cause of a problem. In order to improve semiconductor production line quality, machine learning tools can be developed for more thorough and accurate anomaly detection. Previous work on applying machine learning to anomaly detection focused on building reference cycles, and using clustering and time series forecasting to detect anomalous wafer cycles. We seek to improve upon these techniques and apply them to related domains of semiconductor manufacturing. The main focus is to develop a process for automated anomaly detection by combining the previously used methods of cluster analysis and time series forecasting and prediction. We also explore detecting anomalies across multiple semiconductor manufacturing machines and recipes.

Anomaly Detection of Semiconductor Manufacturing Based on Machine Learning

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Release : 2021
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Kind : eBook
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Book Synopsis Anomaly Detection of Semiconductor Manufacturing Based on Machine Learning by :

Download or read book Anomaly Detection of Semiconductor Manufacturing Based on Machine Learning written by . This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt:

Control Charts and Machine Learning for Anomaly Detection in Manufacturing

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Release : 2022
Genre :
Kind : eBook
Book Rating : 201/5 ( reviews)

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Book Synopsis Control Charts and Machine Learning for Anomaly Detection in Manufacturing by : Kim Phuc Tran

Download or read book Control Charts and Machine Learning for Anomaly Detection in Manufacturing written by Kim Phuc Tran. This book was released on 2022. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.

Artificial Intelligence for Digitising Industry – Applications

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Release : 2022-09-01
Genre : Medical
Kind : eBook
Book Rating : 318/5 ( reviews)

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Book Synopsis Artificial Intelligence for Digitising Industry – Applications by : Ovidiu Vermesan

Download or read book Artificial Intelligence for Digitising Industry – Applications written by Ovidiu Vermesan. This book was released on 2022-09-01. Available in PDF, EPUB and Kindle. Book excerpt: This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI project, including an overview of industrial use cases, research, technological innovation, validation, and deployment. This book’s sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation. The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0. Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimisation, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin. AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection. The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications. AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimisation and predictive maintenance for soybeans manufacturing. For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport. This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0. The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.

Network Anomaly Detection

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Release : 2013-06-18
Genre : Computers
Kind : eBook
Book Rating : 09X/5 ( reviews)

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Book Synopsis Network Anomaly Detection by : Dhruba Kumar Bhattacharyya

Download or read book Network Anomaly Detection written by Dhruba Kumar Bhattacharyya. This book was released on 2013-06-18. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi

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