Machine Learning-Assisted Statistical Variation Analysis of Ferroelectric Transistor: From Experimental Metrology to Adaptive Modeling
Author:
Affiliation:
1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
2. Fraunhofer IPMS, Dresden, Germany
Funder
Taiwan Semiconductor Manufacturing Company (TSMC) and EMD Electronics
PRISM Center of the Semiconductor Research Corporation (SRC)/Defense Advanced Research Projects Agency (DARPA) JUMP 2.0 Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
Link
http://xplorestaging.ieee.org/ielx7/16/10081247/10053634.pdf?arnumber=10053634
Reference18 articles.
1. Transfer Learning for Wireless Networks: A Comprehensive Survey
2. Variability Study of Ferroelectric Field-Effect Transistors Towards 7nm Technology Node
3. On the Origin of Wake‐Up and Antiferroelectric‐Like Behavior in Ferroelectric Hafnium Oxide
4. Machine Learning Assisted Statistical Variation Analysis of Ferroelectric Transistors: From Experimental Metrology to Predictive Modeling
5. Statistical Variability in Fully Depleted SOI MOSFETs Due to Random Dopant Fluctuations in the Source and Drain Extensions
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