Machine Learning-Based Universal Threshold Voltage Extraction of Transistors Using Convolutional Neural Networks

Abstract

The threshold voltage $(V_{th})$ enables us to measure the functionality of ultra-scaled field effect transistors (FETs) and plays a key role in the performance evaluation of devices. Although many $V_{th}$ extraction methods exist and are in use in the industry, selecting an optimized and universal method is still difficult. Additionally, these methods often rely on expert validation, which increases the time cost for researchers to optimize the extraction process. In this work, we propose a universal and autonomous machine learning model, specifically a convolutional neural network based $V_{th}$ extractor model. The novelty of this work lies in simultaneously processing gate, drain, source, and bulk currents combined with gate voltage to remove the dependency on setting boundaries for gate voltage. Additionally, the training dataset is composed of measurements coming from transistors of different technology nodes (Planar, MOSFET, FinFET, Gate-All-Around) to provide generalization. Our method produces significantly more accurate results than traditional ML algorithms by extracting $V_{th}$ in 3mV mean absolute error rate and is verified with different performance metrics.