This comprehensive compendium describes the basic modeling techniques for silicon-based semiconductor devices, introduces the basic concepts of silicon-based passive and active devices, and provides its state-of-the-art modeling and equivalent circuit parameter extraction methods.The unique reference text benefits practicing engineers, technicians, senior undergraduate and first-year graduate students working in the areas of RF, microwave and solid-state device, and integrated circuit design.

This book is an introduction to microwave and RF signal modeling and measurement techniques for field effect transistors. It assumes only a basic course in electronic circuits and prerequisite knowledge for readers to apply the techniques and improve the performance of integrated circuits, reduce design cycles and increase their chance at first time success. The first chapters offer a general overview and discussion of microwave signal and noise matrices, and microwave measurement techniques. The following chapters address modeling techniques for field effect transistors and cover models such as: small signal, large signal, noise, and the artificial neural network based.

This book focuses on optimal control and systems engineering in the big data era. It examines the scientific innovations in optimization, control and resilience management that can be applied to further success. In both business operations and engineering applications, there are huge amounts of data that can overwhelm computing resources of large-scale systems. This "big data" provides new opportunities to improve decision making and addresses risk for individuals as well in organizations. While utilizing data smartly can enhance decision making, how to use and incorporate data into the decision making framework remains a challenging topic. Ultimately the chapters in this book present new models and frameworks to help overcome this obstacle. Optimization and Control for Systems in the Big-Data Era: Theory and Applications is divided into five parts. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications. Part III provides novel insights and new findings in the area of financial optimization analysis. The chapters in Part IV deal with operations analysis, covering flow-shop operations and quick response systems. The book concludes with final remarks and a look to the future of big data related optimization and control problems.

This book focuses on optimal control and systems engineering in the big data era. It examines the scientific innovations in optimization, control and resilience management that can be applied to further success. In both business operations and engineering applications, there are huge amounts of data that can overwhelm computing resources of large-scale systems. This "big data" provides new opportunities to improve decision making and addresses risk for individuals as well in organizations. While utilizing data smartly can enhance decision making, how to use and incorporate data into the decision making framework remains a challenging topic. Ultimately the chapters in this book present new models and frameworks to help overcome this obstacle. Optimization and Control for Systems in the Big-Data Era: Theory and Applications is divided into five parts. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications. Part III provides novel insights and new findings in the area of financial optimization analysis. The chapters in Part IV deal with operations analysis, covering flow-shop operations and quick response systems. The book concludes with final remarks and a look to the future of big data related optimization and control problems.