Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

Abstract

The intrinsic stochasticity of the memristor can be used to generate true random numbers, essential for non-decryptable hardware-based security devices. Here, we propose a novel and advanced method to generate true random numbers utilizing the stochastic oscillation behavior of a NbOx mott memristor, exhibiting self-clocking, fast and variation tolerant characteristics. The random number generation rate of the device can be at least 40 kb s−1, which is the fastest record compared with previous volatile memristor-based TRNG devices. Also, its dimensionless operating principle provides high tolerance against both ambient temperature variation and device-to-device variation, enabling robust security hardware applicable in harsh environments.

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

“…However, those studies only constructed and characterized single devices, and the circuital part was simulated or modelled. References [23][24][25][26][27][28][29] have shown functional verifications based on complex experimental setups that involve laboratory characterization equipment and commercial programming tools, i.e., they are not stand-alone solutions. References 27,28,30 went farther and implemented parts of the circuit with components-of-the-shelf mounted on a protoboard, but the throughput was only 6 Kilobit/s, 30 the power overhead of the entropy source was too high in the low resistance state 31 , and required very high operating voltages 26 .…”

“…References [23][24][25][26][27][28][29] have shown functional verifications based on complex experimental setups that involve laboratory characterization equipment and commercial programming tools, i.e., they are not stand-alone solutions. References 27,28,30 went farther and implemented parts of the circuit with components-of-the-shelf mounted on a protoboard, but the throughput was only 6 Kilobit/s, 30 the power overhead of the entropy source was too high in the low resistance state 31 , and required very high operating voltages 26 . References 19,[32][33][34][35][36][37][38][39] proposed that the random telegraph noise (RTN) current signals produced by memristors (i.e., stochastic current fluctuations between two or more levels when a low and constant voltage is applied) could be used as entropy source in TRNG circuits.…”

A flexible and stretchable bionic true random number generator

“…In contrast, physical TRNGs exploit some unpredictable or, at least, difficult to predict physical process and use the outputs to produce a bits sequence that can be truly random [12], thus enabling superior reliability for data encryption and other applications, such as cybersecurity, stochastic modeling, lottery, or games of chance [15][16][17]. Up to date, a series of TRNGs based on different physical sources with different working mechanisms has been investigated to generate considerable random numbers in lieu of conventional pseudo random numbers, such as random telegraph noise (RTN) based on memristors [18][19][20][21][22], thin-film transistor [23][24][25], and triboelectric generator [26,27], laser chaos [28][29][30], photonic integrated chip [31], quantum entropy sources [32][33][34][35], bichromatic laser dye [36], crystallization robot [37], DNA synthesis [38], and so forth. However, majority of aforementioned existing TRNG implementations rely on rigid platforms and expensive complicated manufacturing crafts, which cannot compatibly adapt the portable networked devices and systems since emerging wearable technologies typically demand low-cost and mechanically flexible security hardware components.…”

A High-Quality Entropy Source Using van der Waals Heterojunction for True Random Number Generation

“…Quantum TRNGs rely on the probabilistic nature of the quantum events for randomness, while classical TRNGs such as TRNGs based on classical chaos rely on the indeterminism caused by finite measurement accuracy and the high sensitivity to initial conditions as the source of randomness. Utilizing these processes, TRNGs based on chaotic lasers, multimodal ring oscillators, random Raman fiber lasers, memristors, − amplified spontaneous emission, photon arrival time measurements, superparamagnetic tunnel junctions, carbon nanotube transistors, etc ., have been demonstrated recently. Together with the quality of randomness produced, the compactness and scalability of these technologies also play a crucial role in determining the real-life applicability of these physical processes as TRNGs.…”