Machine Learning for Semiconductor Materials

<p><em>Machine Learning for Semiconductor Materials</em> 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 <i>K</i>-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.</p><p>Features:</p><ul> <li>Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-making</li> <li>Covers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequency</li> <li>Explores pertinent biomolecule detection methods</li> <li>Reviews recent methods in the field of machine learning for semiconductor materials with real-life applications</li> <li>Examines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD software</li> </ul><p>This book is aimed at researchers and graduate students in semiconductor materials machine learning and electrical engineering.</p>