Predictive Technology Model for Robust Nanoelectronic Design [recurso electrónico] / by Yu Cao.
Tipo de material: TextoSeries Integrated Circuits and SystemsEditor: Boston, MA : Springer US, 2011Descripción: XV, 173 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781461404453Tema(s): Engineering | Operating systems (Computers) | Electronics | Systems engineering | Nanotechnology | Engineering | Circuits and Systems | Electronics and Microelectronics, Instrumentation | Nanotechnology | Performance and ReliabilityFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 621.3815 Clasificación LoC:TK7888.4Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | TK7888.4 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 372427-2001 |
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1. Introduction -- 2. Predictive Technology Model of Conventional CMOS Devices -- 3. Predictive Technology Model of Enhanced CMOS Devices -- 4. Statistical Extraction and Modeling of CMOS Variability -- 5. Modeling of Temporal Reliability Degradation -- 6. Modeling of Interconnect Parasitics -- 7. Design Benchmark with Predictive Technology Model -- 8. Predictive Process Design Kits -- 9. Predictive Modeling of Carbon Nanotube Devices -- 10. Predictive Technology Model for Future Nanoelectronic Design.
Predictive Technology Model for Robust Nanoelectronic Design explains many of the technical mysteries behind the Predictive Technology Model (PTM) that has been adopted worldwide in explorative design research. Through physical derivation and technology extrapolation, PTM is the de-factor device model used in electronic design. This work explains the systematic model development and provides a guide to robust design practice in the presence of variability and reliability issues. Having interacted with multiple leading semiconductor companies and university research teams, the author brings a state-of-the-art perspective on technology scaling to this work and shares insights gained in the practices of device modeling.
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