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Könyv Predictive Technology Model for Robust Nanoelectronic Design Yu Cao

Predictive Technology Model for Robust Nanoelectronic Design

Szerző: Yu Cao
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer-Verlag New York Inc.
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 11-15 napon belül
39 933 Ft
Predictive Technology Model for Robust Nanoelectronic Design explains many of the technical mysterie...

Információk a könyvről

Szerző
Yu Cao
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2011
oldal
173
EAN
9781461404446
ISBN
1461404444
Enbook ID
01426762
Kiadó
Springer-Verlag New York Inc.
Súly
450
Méretek
155 x 235 x 18

Teljes leírás

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.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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