A Statistical Look At Device Reliability
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A Statistical Look at Device Reliability
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-07-12
**A Statistical Look at Device Reliability** offers a comprehensive exploration of the statistical underpinnings of device reliability, providing a roadmap for engineers, researchers, and students to navigate the complexities of statistical modeling and credibility assessment. Delving into the fundamental concepts of statistical tools and techniques, the book provides a solid foundation for understanding the intricacies of reliability analysis. It examines various types of reliability models, guiding readers through the processes of model selection, parameter estimation, and validation. Moving beyond theoretical frameworks, the book delves into the practical applications of reliability modeling and analysis. It illustrates how these methods can be applied to optimize design, improve manufacturing processes, and ensure the long-term performance of devices and systems. Case studies drawn from diverse industries, including electronics, automotive, aerospace, and medical devices, offer valuable insights into the challenges and successes of implementing reliability engineering principles. Recognizing the rapidly evolving landscape of technology, the book also explores emerging trends and technologies that are shaping the future of reliability engineering. It examines the potential of artificial intelligence, machine learning, and the Internet of Things (IoT) to revolutionize the way we assess and manage device reliability. Written in an accessible and engaging style, **A Statistical Look at Device Reliability** is an essential resource for anyone seeking a deeper understanding of device reliability. Its comprehensive coverage, practical examples, and forward-looking perspective make it an indispensable guide for navigating the complexities of statistical modeling and credibility assessment in this critical field. If you like this book, write a review!
Reliable Machine Learning
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine: What ML is: how it functions and what it relies on Conceptual frameworks for understanding how ML "loops" work How effective productionization can make your ML systems easily monitorable, deployable, and operable Why ML systems make production troubleshooting more difficult, and how to compensate accordingly How ML, product, and production teams can communicate effectively
Reliability Prediction for Microelectronics
Author: Joseph B. Bernstein
language: en
Publisher: John Wiley & Sons
Release Date: 2024-02-13
RELIABILITY PREDICTION FOR MICROELECTRONICS Wiley Series in Quality & Reliability Engineering REVOLUTIONIZE YOUR APPROACH TO RELIABILITY ASSESSMENT WITH THIS GROUNDBREAKING BOOK Reliability evaluation is a critical aspect of engineering, without which safe performance within desired parameters over the lifespan of machines cannot be guaranteed. With microelectronics in particular, the challenges to evaluating reliability are considerable, and statistical methods for creating microelectronic reliability standards are complex. With nano-scale microelectronic devices increasingly prominent in modern life, it has never been more important to understand the tools available to evaluate reliability. Reliability Prediction for Microelectronics meets this need with a cluster of tools built around principles of reliability physics and the concept of remaining useful life (RUL). It takes as its core subject the ‘physics of failure’, combining a thorough understanding of conventional approaches to reliability evaluation with a keen knowledge of their blind spots. It equips engineers and researchers with the capacity to overcome decades of errant reliability physics and place their work on a sound engineering footing. Reliability Prediction for Microelectronics readers will also find: Focus on the tools required to perform reliability assessments in real operating conditions Detailed discussion of topics including failure foundation, reliability testing, acceleration factor calculation, and more New multi-physics of failure on DSM technologies, including TDDB, EM, HCI, and BTI Reliability Prediction for Microelectronics is ideal for reliability and quality engineers, design engineers, and advanced engineering students looking to understand this crucial area of product design and testing.