Issue 5, 2023

Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller

Abstract

The development of the internet-of-things requires cheap, light, small and reliable true random number generator (TRNG) circuits to encrypt the data—generated by objects or humans—before transmitting them. However, all current solutions consume too much power and require a relatively large battery, hindering the integration of TRNG circuits on most objects. Here we fabricated a TRNG circuit by exploiting stable random telegraph noise (RTN) current signals produced by memristors made of two-dimensional (2D) multi-layered hexagonal boron nitride (h-BN) grown by chemical vapor deposition and coupled with inkjet-printed Ag electrodes. When biased at small constant voltages (≤70 mV), the Ag/h-BN/Ag memristors exhibit RTN signals with very low power consumption (∼5.25 nW) and a relatively high current on/off ratio (∼2) for long periods (>1 hour). We constructed TRNG circuits connecting an h-BN memristor to a small, light and cheap commercial microcontroller, producing a highly-stochastic, high-throughput signal (up to 7.8 Mbit s−1) even if the RTN at the input gets interrupted for long times up to 20 s, and if the stochasticity of the RTN signal is reduced. Our study presents the first full hardware implementation of 2D-material-based TRNGs, enabled by the unique stability and figures of merit of the RTN signals in h-BN based memristors.

Graphical abstract: Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller

Supplementary files

Article information

Article type
Paper
Submitted
07 Nov. 2022
Accepted
26 Dec. 2022
First published
29 Dec. 2022
This article is Open Access
Creative Commons BY license

Nanoscale, 2023,15, 2171-2180

Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller

S. Pazos, W. Zheng, T. Zanotti, F. Aguirre, T. Becker, Y. Shen, K. Zhu, Y. Yuan, G. Wirth, F. M. Puglisi, J. B. Roldán, F. Palumbo and M. Lanza, Nanoscale, 2023, 15, 2171 DOI: 10.1039/D2NR06222D

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