Analysis of random telegraph noise in resistive memories: The case of unstable filaments

TL;DR

This paper investigates unstable RTN signals in resistive memories with unstable filaments, proposing an adaptive filtering method to improve measurement accuracy of trap time constants and validating it with frequency domain analysis.

Contribution

It introduces an adaptive moving-average detrending technique to accurately analyze unstable RTN signals in resistive memory devices.

Findings

The proposed method effectively stabilizes RTN signals for analysis.

Accurate extraction of trap emission and capture time constants was achieved.

Frequency domain analysis validated the method's accuracy.

Abstract

Through Random Telegraph Noise (RTN) analysis, valuable information can be provided about the role of defect traps in fine tuning and reading of the state of a nanoelectronic device. However, time domain analysis techniques exhibit their limitations in case where unstable RTN signals occur. These instabilities are a common issue in Multi-Level Cells (MLC) of resistive memories (ReRAM), when the tunning protocol fails to find a perfectly stable resistance state, which in turn brings fluctuations to the RTN signal especially in long time measurements and cause severe errors in the estimation of the distribution of time constants of the observed telegraphic events, i.e., capture/emission of carriers from traps. In this work, we analyze the case of the unstable filaments in silicon nitride-based ReRAM devices and propose an adaptive filter implementing a moving-average detrending method in order to flatten unstable RTN signals and increase sufficiently the accuracy of the conducted measurements. The te and tc emission/capture time constants of the traps, respectively, are then calculated and a cross-validation through frequency domain analysis (Lorentzian fitting) was performed proving that the proposed method is accurate.