Machine Learning Assisted Statistical Variation Analysis of Ferroelectric Transistors: From Experimental Metrology to Predictive Modeling

We proposed a novel machine learning (ML)-assisted methodology to analyze the variability of ferroelectric field-effect transistor (FeFET) with raw data from the metrology. Transmission Kikuchi diffraction (TKD) measurement was performed on grown Si-doped HfO 2 (Si:HfO 2 ) thin film. An experimental...

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Bibliographic Details
Main Authors: Choe, Gihun, Ravindran, Prasanna Venkatesan, Lu, Anni, Hur, Jae, Lederer, Maximilian, Reck, Andre, Lombardo, Sarah, Afroze, Nashrah, Kacher, Josh, Khan, Asif Islam, Yu, Shimeng
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
FeFET
Ferroelectrics
Machine learning
Metrology
neural network
Neural networks
Predictive models
Si:HfO 2
TCAD
TKD metrology
Training
variation
Very large scale integration
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Summary:We proposed a novel machine learning (ML)-assisted methodology to analyze the variability of ferroelectric field-effect transistor (FeFET) with raw data from the metrology. Transmission Kikuchi diffraction (TKD) measurement was performed on grown Si-doped HfO 2 (Si:HfO 2 ) thin film. An experimentally acquired polarization map was employed to generate the polarization variation of a ferroelectric gate stack. FeFETs with the multi-domains are simulated in TCAD to generate the training dataset. We trained a neural network using the polarization maps as inputs and the high/low threshold voltage, on-state current, and subthreshold slope as outputs. The trained model with 3,000 data points shows >98% of accuracy and is more than 10 6 times faster than performing TCAD to obtain statistics for 10,000 test samples.
ISSN:2158-9682
DOI:10.1109/VLSITechnologyandCir46769.2022.9830392