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-making
Covers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequency
Explores pertinent biomolecule detection methods
Reviews recent methods in the field of machine learning for semiconductor materials with real-life applications
Examines the 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 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-making
Covers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequency
Explores pertinent biomolecule detection methods
Reviews recent methods in the field of machine learning for semiconductor materials with real-life applications
Examines the 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.
This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from...
Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials...
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