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Artificial Neural Network Surrogate Models for Efficient Design Space Exploration of 14-nm FinFETs
Surila Guglani
, Avirup Dasgupta
, Ming Yen Kao
,
Chenming Hu
, Sourajeet Roy
智慧半導體奈米系統技術研究中心
國際半導體產業學院
研究成果
:
Conference contribution
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同行評審
8
引文 斯高帕斯(Scopus)
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Keyphrases
Fin Field-effect Transistor (FinFET)
100%
Artificial Neural Network
100%
Computer-aided Design Tools
100%
Design Space Exploration
100%
Technology Computer-aided Design
100%
Neural Network Surrogate Model
100%
Prediction Accuracy
33%
Neural Network
33%
Mathematical Model
33%
Technology Node
33%
Circuit Design
33%
Compact Model
33%
Computational Efficiency
33%
Design Optimization
33%
Device Physics
33%
Proper Design
33%
Computationally Expensive
33%
Device Optimization
33%
Technology Optimization
33%
High Computational Cost
33%
Predictive Ability
33%
Novel Methodology
33%
Electrostatic Control
33%
Surrogate Model
33%
Contemporary Technologies
33%
Numerical Efficiency
33%
Parametric Sweep
33%
Engineering
Field-Effect Transistor
100%
Design Space
100%
Surrogate Model
100%
Computer Aided Design
100%
Artificial Neural Network
100%
Design Tool
100%
Nodes
33%
Circuit Design
33%
Computational Cost
33%
Design Optimization
33%
Proper Design
33%
Computational Efficiency
33%
Predictive Ability
33%
Numerical Efficiency
33%
Parametric Sweep
33%
Mathematical Model
33%
Material Science
Field Effect Transistor
100%
Tool Design
100%
Electronic Circuit
33%
Chemical Engineering
Neural Network
100%