ASCENT Theme 4 / Machine Learning Assisted Statistical Variation Analysis of Ferroelectric Transistors: From Experimental Metrology to Predictive Modeling

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

Presenter: Gihun Choe (Prof. Shimeng Yu's Group, Georgia Tech)

Time: 4:00pm Eastern

Abstract: 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 HfO₂ (Si:HfO₂) 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 10⁶ times faster than performing TCAD to obtain statistics for 10,000 test samples.

Gihun Choe received the B.S. and M.S. degree in electrical and computer engineering from University of Seoul, Seoul, Korea in 2017 and in 2019, respectively. He is currently working towards the Ph.D. degree in electrical and computer engineering at Georgia Institute of Technology, Atlanta, GA, USA. 
His research interest involves emerging non-volatile memory device/circuit and machine learning assisted device/circuit modeling.

Theme 4 Tasks:
2776.038: Ferroelectric Synapse 
2776.046: Device-to-System Evaluation Framework for Cognitive Microsystems

This meeting is only available to the JUMP research community, such as Principal Investigators, Postdoc researchers, Students, and Industry/Government liaisons.