Machine Learning Semiconductor Materials (19 results)

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Hardcover. Condition: new. Hardcover. 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 item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 226 pp. Englisch.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Hardcover. Condition: new. Hardcover. 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 item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. Machine Learning for Semiconductor Materials | Neeraj Gupta (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | CRC Press | EAN 9781032796888 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu Print on Demand.

Hardcover. Condition: new. Hardcover. 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 item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.