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.

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.