Efficient Neural Network-Based Compact Modeling for Novel Device Structures Using a Multi-Fidelity Model and Active Learning

Abstract

Neural network (NN)-based compact modeling methodologies are gaining attention due to the challenges of device complexity, narrow model coverage, and SPICE simulation speed in advanced semiconductor technology nodes. As device complexity increases, the number of process and structural variables also increases, which significantly increases the amount of technology computer-aided design (TCAD) simulation data required for NN-based compact modeling. This study proposes a multi-fidelity model and active learning approach to predict global and local variations of nanosheet FETs (NSFETs) with less than 1.5% error, significantly reducing the number of required TCAD simulations by more than half compared with conventional modeling techniques. In addition, the simplified NN model with a smaller training dataset significantly reduces the SPICE simulation time.