Quantitative Evaluation of Line-Edge Roughness in Various FinFET Structures: Bayesian Neural Network With Automatic Model Selection

Abstract

To design a device that is robust to process-induced random variation, this study proposes a machine-learning-based predictive model that can simulate the electrical characteristics of FinFETs with process-induced line-edge roughness. This model, i.e., a Bayesian neural network (BNN) model with horseshoe priors (Horseshoe-BNN), can significantly reduce the simulation time (as compared to the conventional technology computer-aided design (TCAD) simulation method) in a sufficiently accurate manner. Moreover, this model can perform autonomous model selection over the most compact layer size, which is necessary when the amount of data must be limited. The mean absolute percentage error for the mean and standard deviation of the drain-to-source current $\left ({\mathrm {I}_{\mathrm {DS}} }\right)$ were ~0.5% and ~6%, respectively. By estimating the distribution of the current-voltage characteristics, the distributions of the other device metrics, such as off-state leakage current and threshold voltage, can be estimated as well.

Keywords

• Line edge roughness (LER)
• process-induced random variation
• Bayesian neural network
• automatic model selection