Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

• Main Authors: Jaehyuk Lim, Changhwan Shin
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Line edge roughness; process-induced random variation; FinFET; machine learning; artificial neural network
• Online Access: https://ieeexplore.ieee.org/document/9179808/
• ISSN: 2169-3536

ML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(Δ), correlation length along x-direction (A_X), and correlation length along y-direction (A_Y), the metrics for device performance such as on-state drive current, off-state leakage current, threshold voltage, and subthreshold swing can be computing-efficiently estimated with the ANN model.