High-k Materials in Multi-Gate FET Devices focuses on high-k materials for advanced FET devices. It discusses emerging challenges in the engineering and applications and considers issues with associated technologies. It covers the various way of utilizing high-k dielectrics in multi-gate FETs for enhancing their performance at the device as well as circuit level. Provides basic knowledge about FET devices Presents the motivation behind multi-gate FETs, including current and future trends in transistor technologies Discusses fabrication and characterization of high-k materials Contains a comprehensive analysis of the impact of high-k dielectrics utilized in the gate-oxide and the gate-sidewall spacers on the GIDL of emerging multi-gate FET architectures Offers detailed application of high-k materials for advanced FET devices Considers future research directions This book is of value to researchers in materials science, electronics engineering, semiconductor device modeling, IT, and related disciplines studying nanodevices such as FinFET and Tunnel FET and device-circuit codesign issues.

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

* Covers material testing and development using computational intelligence * Highlights the technologies to integrate computational intelligence and materials sciences * Discusses how computational tools can generate new materials with advanced applications * Details case studies and detailed applications * Investigates challenges in developing and using computational intelligence in materials science * Analyzes historic changes that are taking place in designing of materials

High-k Materials in Multi-Gate FET Devices focuses on high-k materials for advanced FET devices. It discusses emerging challenges in the engineering and applications and considers issues with associated technologies. It covers the various way of utilizing high-k dielectrics in multi-gate FETs for enhancing their performance at the device as well as circuit level. Provides basic knowledge about FET devices Presents the motivation behind multi-gate FETs, including current and future trends in transistor technologies Discusses fabrication and characterization of high-k materials Contains a comprehensive analysis of the impact of high-k dielectrics utilized in the gate-oxide and the gate-sidewall spacers on the GIDL of emerging multi-gate FET architectures Offers detailed application of high-k materials for advanced FET devices Considers future research directions This book is of value to researchers in materials science, electronics engineering, semiconductor device modeling, IT, and related disciplines studying nanodevices such as FinFET and Tunnel FET and device-circuit codesign issues.