Multi-trap RTN parameter extraction based on Bayesian inference

Abstract

This paper presents a new analysis method for estimating the statistical parameters of random telegraph noise (RTN). RTN is characterized by the time constants of carrier capture and emission, and associated changes of threshold voltage. Because trap activities are projected on to the threshold voltage, the separation of time constants and amplitude for each trap is an ill-posed problem. The proposed method solves this problem by statistical method that can reflect the physical generation process of RTN. By using Gibbs sampling algorithm developed in statistical machine learning community, we decompose the measured threshold voltage sequence to time constants and amplitude of each trap. We also demonstrate that the proposed method estimates time constants about 2.1 times more accurately than the existing work that uses hidden Markov model, which contributes to enhance the accuracy of reliability-aware circuit simulation.