Parveen Singla – författare

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.

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.

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.

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.

Advanced materials are essential for economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security, and human welfare. Yet, it can take years to move a material to the market after its initial discovery. Computational techniques have accelerated the exploration and development of materials, offering the chance to move new materials to the market quickly. Computational Technologies in Materials Science addresses topics related to AI, machine learning, deep learning, and cloud computing in materials science. It explores characterization and fabrication of materials, machine-learning-based models, and computational intelligence for the synthesis and identification of materials. This book

• Covers material testing and development using computational intelligence

• Highlights the technologies to integrate computational intelligence and materials science

• 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 materials.

This book encourages material researchers and academics to develop novel theories and sustainable computational techniques and explores the potential for computational intelligence to replace traditional materials research.

Advanced materials are essential for economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security, and human welfare. Yet, it can take years to move a material to the market after its initial discovery. Computational techniques have accelerated the exploration and development of materials, offering the chance to move new materials to the market quickly. Computational Technologies in Materials Science addresses topics related to AI, machine learning, deep learning, and cloud computing in materials science. It explores characterization and fabrication of materials, machine-learning-based models, and computational intelligence for the synthesis and identification of materials. This book

• Covers material testing and development using computational intelligence

• Highlights the technologies to integrate computational intelligence and materials science

• 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 materials.

This book encourages material researchers and academics to develop novel theories and sustainable computational techniques and explores the potential for computational intelligence to replace traditional materials research.

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

This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.