Rajkumar Sarma – författare

Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.

Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices, analog and Radio Frequency (RF) behaviour of semiconductor devices with different materials.

Features:

Focuses on the semiconductor materials and the use of machine learning to facilitate understanding and decision-making. Covers RF and noise analysis to formulate the frequency behaviour of semiconductor device at high frequency. Explores pertinent biomolecule detection method. Reviews recent methods in the field of machine learning for semiconductor materials with real-life application. Examines limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD software.

This book is aimed at researchers and graduate students in semiconductor materials, machine learning, and electrical engineering.

Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices, analog and Radio Frequency (RF) behaviour of semiconductor devices with different materials.

Features:

Focuses on the semiconductor materials and the use of machine learning to facilitate understanding and decision-making. Covers RF and noise analysis to formulate the frequency behaviour of semiconductor device at high frequency. Explores pertinent biomolecule detection method. Reviews recent methods in the field of machine learning for semiconductor materials with real-life application. Examines limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD software.

This book is aimed at researchers and graduate students in semiconductor materials, machine learning, and electrical engineering.

The book explores how quantum computing can protect AI systems from sophisticated adversarial threats. As AI applications grow in sectors like finance, healthcare, and critical infrastructure, vulnerabilities such as data poisoning and model inversion pose significant security risks. This book investigates how quantum methods can effectively counter these challenges.Distinct from existing literature, it combines theoretical insights with practical applications, covering quantum-enhanced cryptography, quantum machine learning for threat detection, and advanced algorithms tailored for AI. It provides actionable solutions for AI practitioners while also meeting the academic needs of researchers with rigorous mathematical treatments.The book is targeted at academic researchers, professionals, and graduate students in computer science interested in AI and quantum computing. It goes beyond mathematical concepts to bridge quantum theory and its practical applications across sciences and industries. By offering both foundational principles and advanced quantum defense applications, this book serves as a vital resource for those developing AI systems with robust, quantum-enhanced security.

The book explores how quantum computing can protect AI systems from sophisticated adversarial threats. As AI applications grow in sectors like finance, healthcare, and critical infrastructure, vulnerabilities such as data poisoning and model inversion pose significant security risks. This book investigates how quantum methods can effectively counter these challenges.Distinct from existing literature, it combines theoretical insights with practical applications, covering quantum-enhanced cryptography, quantum machine learning for threat detection, and advanced algorithms tailored for AI. It provides actionable solutions for AI practitioners while also meeting the academic needs of researchers with rigorous mathematical treatments.The book is targeted at academic researchers, professionals, and graduate students in computer science interested in AI and quantum computing. It goes beyond mathematical concepts to bridge quantum theory and its practical applications across sciences and industries. By offering both foundational principles and advanced quantum defense applications, this book serves as a vital resource for those developing AI systems with robust, quantum-enhanced security.

The book explores how quantum computing can protect AI systems from sophisticated adversarial threats. As AI applications grow in sectors like finance, healthcare, and critical infrastructure, vulnerabilities such as data poisoning and model inversion pose significant security risks. This book investigates how quantum methods can effectively counter these challenges.Distinct from existing literature, it combines theoretical insights with practical applications, covering quantum-enhanced cryptography, quantum machine learning for threat detection, and advanced algorithms tailored for AI. It provides actionable solutions for AI practitioners while also meeting the academic needs of researchers with rigorous mathematical treatments.The book is targeted at academic researchers, professionals, and graduate students in computer science interested in AI and quantum computing. It goes beyond mathematical concepts to bridge quantum theory and its practical applications across sciences and industries. By offering both foundational principles and advanced quantum defense applications, this book serves as a vital resource for those developing AI systems with robust, quantum-enhanced security.