Factorial Hidden Markov Model analysis of Random Telegraph Noise in Resistive Random Access Memories

This paper presents a new technique to analyze the characteristics of multi-level random telegraph noise (RTN). RTN is defined as an abrupt switching of either the current or the voltage between discrete values as a result of trapping/de-trapping activity. RTN signal properties are deduced exploiting a factorial hidden Markov model (FHMM). The proposed method considers the measured multi-level RTN as a superposition of many two-levels RTNs, each represented by a Markov chain and associated to a single trap, and it is used to retrieve the statistical properties of each chain. These properties (i.e. dwell times and amplitude) are directly related to physical properties of each trap.