Artificial Neural Network-Based Compact Modeling Methodology for Advanced Transistors

• Published in: IEEE transactions on electron devices Vol. 68; no. 3; pp. 1318 - 1325
• Main Authors: Wang, Jing, Kim, Yo-Han, Ryu, Jisu, Jeong, Changwook, Choi, Woosung, Kim, Daesin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural network (ANN); Artificial neural networks; circuit simulation; compact modeling; Data models; design-technology-cooptimization (DTCO); Electric potential; emerging devices; Field effect transistors; field-effect transistor (FET); Integrated circuit modeling; machine learning; Mathematical model; Methodology; Model accuracy; Modelling; Neural networks; pathfinding; Semiconductor device modeling; Semiconductor devices; SPICE; statistical modeling; Training; Voltage
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2020.3048918

The artificial neural network (ANN)-based compact modeling methodology is evaluated in the context of advanced field-effect transistor (FET) modeling for Design-Technology-Cooptimization (DTCO) and pathfinding activities. An ANN model architecture for FETs is introduced, and the results clearly show that by carefully choosing the conversion functions (i.e., from ANN outputs to device terminal currents or charges) and the loss functions for ANN training, ANN models can reproduce the current-voltage and charge-voltage characteristics of advanced FETs with excellent accuracy. A few key techniques are introduced in this work to enhance the capabilities of ANN models (e.g., model retargeting, variability modeling) and to improve ANN training efficiency and SPICE simulation turn-around-time (TAT). A systematical study on the impact of the ANN size on ANN model accuracy and SPICE simulation TAT is conducted, and an automated flow for generating optimum ANN models is proposed. The findings in this work suggest that the ANN-based methodology can be a promising compact modeling solution for advanced DTCO and pathfinding activities.