Machine Learning Assisted Statistical Variation Analysis of Ferroelectric Transistors: From Experimental Metrology to Predictive Modeling
Gihun Choe, Prasanna Venkatesan Ravindran, Anni Lu, Jae Hur, Maximilian Lederer, André Reck, Sarah Lombardo, Nashrah Afroze, Josh Kacher, Asif Islam Khan, Shimeng Yu
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 HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> (Si:HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times faster than performing TCAD to obtain statistics for 10,000 test samples.