Enhancing Hybrid Nanodevice Fabrication Efficiency using Machine Learning

Gain a competitive edge in the semiconductor industry with this essential guide, which provides the practical insights and machine learning techniques needed to optimize the fabrication of hybrid nanodevices for integrated circuits.

Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning explores the intersection of advanced manufacturing techniques and machine learning applications in the field of nanotechnology, specifically focusing on hybrid nanodevices for integrated circuits. This book provides a comprehensive understanding of how machine learning algorithms and techniques can optimize the fabrication processes of hybrid nanodevices, improving their efficiency, reliability, and performance in integrated circuit applications. The book begins with an introduction to the fundamentals of hybrid nanodevice fabrication and the role of machine learning in enhancing these processes. It then delves into various machine learning algorithms and models used for process optimization, quality control, and predictive maintenance in integrated circuit fabrication. Case studies and practical examples illustrate real-world applications of machine learning in improving yield, reducing costs, and accelerating time-to-market for hybrid nanodevices. It also addresses the pressing need for a comprehensive guide on machine learning applications in nanodevice fabrication. It provides researchers, engineers, and industry professionals with practical insights for implementing machine learning techniques to tackle challenges such as variability reduction, defect detection, and process optimization. By bridging the gap between theory and practice, the book equips readers with the knowledge and tools necessary to leverage machine learning for a competitive advantage in the semiconductor industry.

Udit Mamodiya, PhD is an Associate Professor and Associate Dean of Research at Poornima University with more than 12 years of experience. He has authored ten books and more than 50 papers, published more than 50 utility patents, and holds 20 design patents and copyrights. His research interests include renewable energy sources, reliability analysis, expert systems, and decision support systems.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University with more than 22 years of experience in academics and research. She has authored and edited more than 30 books and published more than 140 research papers in international journals, conference proceedings, and e-books, 14 Indian patents, and four copyrights. Her areas of expertise include microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT and embedded system design.

Deepika Ghai, PhD is an Assistant Professor at Lovely Professional University with more than five years of experience in academics. She has published two books and more than 35 research papers in refereed journals and conferences. Her areas of expertise include signal and image processing, biomedical signal and image processing, AI and machine learning, and VLSI signal processing.

Deepak Kumar Jain, PhD is an Associate Professor and Senior Scientist in the School of Artificial Intelligence at Dalian University of Technology. He has presented several papers in peer-reviewed conferences and authored and coauthored numerous studies in internationally reputed journals. His research interests include deep learning, machine learning, pattern recognition, and computer vision.