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.
|Original language||English (US)|
|State||Published - Dec 29 2022|
Bibliographical noteKAUST Repository Item: Exported on 2023-01-13
Acknowledgements: This work was supported by the Ministry of Science and Technology of China (grants no. 2019YFE0124200 and 2018YFE0100800), the National Natural Science Foundation of China (grants no. 61874075), the Collaborative Innovation Centre of Suzhou Nano Science and Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions, the 111 Project from the State Administration of Foreign Experts Affairs of China, and the Baseline funding program of the King Abdullah University of Science and Technology. The authors also acknowledge the funding from the following Argentinean institutions: Ministerio de Ciencia, Tecnología e Innovación (MINCyT) under contracts, PICT 2016/0579, PME 2015-0196 and PICTE 2018-0192; UTN-FRBA under projects CCUTIBA4764TC, MATUNBA4936, CCUTNBA5182, and CCUTNBA6615.
ASJC Scopus subject areas
- Materials Science(all)