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Taschenbuch. Condizione: Neu. Modeling of Transport Properties of Carbon Nano Tubes (CNTs) | Sandeep Dhariwal (u. a.) | Taschenbuch | 168 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139933334 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Paperback. Condizione: Brand New. 168 pages. 8.66x5.91x0.38 inches. In Stock.

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Hardcover. Condizione: Brand New. 240 pages. 9.18x6.12x9.45 inches. In Stock.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book contributes to the description of electronic structure and transport in metallic CNTs with electrode contacts. DFT coupled with NEGF theoretical approach has been applied to different electrode materials. Ballistic transport calculations are based on the nonequilibrium Green's function formalism combined with density functional theory (DFT). A systematic investigation of different contact materials is carried out using suitable atomistic metal-CNT-metal structures, optimized in an appropriate way. Based on the models, electronic transport calculations are carried out, which further can be extended to large systems by applying the DFT calculator. Transmission spectrum and differential conductance are useful results to investigate the CNT-metal interaction and its influences on the transport. Horizontal, vertical and angular configurations are compared that may be suitable for future on-chip interconnect applications. Antenna and sensors are designed and fabricated based on the application of carbon nano-materials. With a development and an increasing interest in flexible electronics, the design of a patch antenna is presented using CNT-polymer ink on fabrics. 168 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book contributes to the description of electronic structure and transport in metallic CNTs with electrode contacts. DFT coupled with NEGF theoretical approach has been applied to different electrode materials. Ballistic transport calculations are based on the nonequilibrium Green's function formalism combined with density functional theory (DFT). A systematic investigation of different contact materials is carried out using suitable atomistic metal-CNT-metal structures, optimized in an appropriate way. Based on the models, electronic transport calculations are carried out, which further can be extended to large systems by applying the DFT calculator. Transmission spectrum and differential conductance are useful results to investigate the CNT-metal interaction and its influences on the transport. Horizontal, vertical and angular configurations are compared that may be suitable for future on-chip interconnect applications. Antenna and sensors are designed and fabricated based on the application of carbon nano-materials. With a development and an increasing interest in flexible electronics, the design of a patch antenna is presented using CNT-polymer ink on fabrics.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch.

Hardcover. Condizione: 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.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book contributes to the description of electronic structure and transport in metallic CNTs with electrode contacts. DFT coupled with NEGF theoretical approach has been applied to different electrode materials. Ballistic transport calculations are based on the nonequilibrium Green's function formalism combined with density functional theory (DFT). A systematic investigation of different contact materials is carried out using suitable atomistic metal-CNT-metal structures, optimized in an appropriate way. Based on the models, electronic transport calculations are carried out, which further can be extended to large systems by applying the DFT calculator. Transmission spectrum and differential conductance are useful results to investigate the CNT-metal interaction and its influences on the transport. Horizontal, vertical and angular configurations are compared that may be suitable for future on-chip interconnect applications. Antenna and sensors are designed and fabricated based on the application of carbon nano-materials. With a development and an increasing interest in flexible electronics, the design of a patch antenna is presented using CNT-polymer ink on fabrics.

Buch. Condizione: 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.

Hardcover. Condizione: 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.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. PRINT ON DEMAND.

Buch. Condizione: 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. Condizione: 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.