The 2nd edition of defect oriented testing has been extensively updated. New chapters on Functional, Parametric Defect Models and Inductive fault Analysis and Yield Engineering have been added to provide a link between defect sources and yield. The chapter on RAM testing has been updated with focus on parametric and SRAM stability testing. Similarly, newer material has been incorporated in digital fault modeling and analog testing chapters. The strength of Defect Oriented Testing for nano-Metric CMOS VLSIs lies in its industrial relevance.

The 2nd edition of defect oriented testing has been extensively updated. New chapters on Functional, Parametric Defect Models and Inductive fault Analysis and Yield Engineering have been added to provide a link between defect sources and yield. The chapter on RAM testing has been updated with focus on parametric and SRAM stability testing. Similarly, newer material has been incorporated in digital fault modeling and analog testing chapters. The strength of Defect Oriented Testing for nano-Metric CMOS VLSIs lies in its industrial relevance.

The monograph will be dedicated to SRAM (memory) design and test issues in nano-scaled technologies by adapting the cell design and chip design considerations to the growing process variations with associated test issues. Purpose: provide process-aware solutions for SRAM design and test challenges.

The proceeding is a collection of research papers presented, at the 9th International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP 2016), by researchers, scientists, engineers, academicians as well as industrial professionals from all around the globe to present their research results and development activities for oral or poster presentations. The topics of interest are as follows but are not limited to: • Robotics, Control, Mechatronics and Automation • Vision, Image, and Signal Processing • Artificial Intelligence and Computer Applications • Electronic Design and Applications • Telecommunication Systems and Applications • Power System and Industrial Applications • Engineering Education

Emerging Nanotechnologies: Test, Defect Tolerance and Reliability covers various technologies that have been developing over the last decades such as chemically assembled electronic nanotechnology, Quantum-dot Cellular Automata (QCA), and nanowires and carbon nanotubes. Each of these technologies offers various advantages and disadvantages. Some suffer from high power, some work in very low temperatures and some others need indeterministic bottom-up assembly. These emerging technologies are not considered as a direct replacement for CMOS technology and may require a completely new architecture to achieve their functionality. Emerging Nanotechnologies: Test, Defect Tolerance and Reliability brings all of these issues together in one place for readers and researchers who are interested in this rapidly changing field.

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.

Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.