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Deep neural network analysis models for complex random telegraph signals.

Marcel Robitaille^1,2, HeeBong Yang^1,2,3, Lu Wang²

¹Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.

Scientific Reports

|June 27, 2023

Summary

Analyzing complex random telegraph signals (RTSs) is challenging. This study introduces a novel three-step protocol using deep neural networks to accurately quantify multilevel RTSs, aiding in system analysis.

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Area of Science:

Physics
Chemistry
Biology
Data Science

Background:

Time-fluctuating signals, including random telegraph signals (RTSs), are common in natural and engineered systems.
Analyzing multilevel RTSs is complex and hinders understanding of underlying mechanisms.
Existing methods struggle with the quantitative analysis of intricate RTS patterns.

Purpose of the Study:

To develop a systematic and reliable protocol for analyzing complex multilevel random telegraph signals.
To leverage deep neural networks for direct quantification of raw temporal RTS data.
To provide researchers with tools for meaningful interpretations of device and system behavior.

Main Methods:

A three-step analysis protocol employing progressive knowledge transfer.
Utilization of three distinct deep neural network architectures for RTS quantification.
Extensive model validation using a diverse dataset with varying noise and amplitude.

Main Results:

The developed protocol accurately quantifies parameters of complex RTSs.
Deep neural network models effectively process raw temporal data for RTS analysis.
Demonstrated robustness across various noise types and amplitude variations.

Conclusions:

The proposed protocol offers a structured approach to analyzing complex RTSs.
This method facilitates deeper insights into device performance and system sensitivity.
Enables more accurate interpretations of physical, chemical, and biological systems exhibiting RTS behavior.