Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller. Issue 5 (11th January 2023)

• Record Type: Journal Article
• Authors: Pazos, Sebastian; Zheng, Wenwen; Zanotti, Tommaso; Aguirre, Fernando; Becker, Thales; Shen, Yaqing; Zhu, Kaichen; Yuan, Yue; Wirth, Gilson; Puglisi, Francesco Maria; Roldán, Juan Bautista; Palumbo, Felix; Lanza, Mario
• Is Part Of: Nanoscale. Volume 15:Issue 5(2023)
• Page Start: 2171
• Page End: 2180
• Publication Date: 2023-01-11
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d2nr06222d
• Languages: English
• ISSNs: 2040-3364

Abstract

Abstract : A 2D hexagonal boron nitride (h-BN) memristor with inkjet-printed silver electrodes is fabricated for ultra-stable random telegraph noise and connected to a custom, low-cost true random number generator fabricated using commercial components. 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.