A rate-adjustable true random number generator based on the stochastic delay of a TiN/NbOx/Pt memristor

Abstract

True random number generator (TRNG) is a critical component in hardware security that is increasingly important in the era of mobile computing and internet of things. A memristor is a promising candidate to construct a TRNG due to its intrinsic variability of switching behavior and high-density integration. Here, we present a 1T1R oscillation structure with a TiN/NbOx/Pt memristor for constructing a rate-adjustable TRNG. The randomness of the oscillation comes from the stochastic switch latency of the memristor. The experimental results demonstrate that the oscillation rate can be modulated by changing the gate bias of the transistor in series. Furthermore, a TRNG circuit with adjustable rates is constructed based on the 1T1R oscillation, and the simulated results revealed that the proposed TRNG output can pass eight NIST tests, indicating the true randomness of the circuit. Finally, the randomness in the memristor, the adjustable rate of the 1T1R oscillation, and the NIST test result of RNG are proved in turn. These results demonstrate the feasibility of the circuit, which can minimize the power consumption by adjusting the random number generation rate to correct the frequency drift caused by extrinsic factors, such as environment temperature and humidity.

Memristive Explainable Artificial Intelligence Hardware

“…For random bit generation, we constructed a Mott oscillator to use its stochastic oscillation characteristic as a robust random bit source. [30][31][32][33][34][35] Figure 2b shows a circuit configuration of the PL unit, consisting of the Mott oscillator and peripheral circuit. The Mott oscillator circuit comprised the Mott memristor (M M ), an intrinsic parallel capacitor (C P ), and a 2 kΩ load resistor (R L ).…”

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

“…Memristors, usually implemented as metal/insulator/metal (MIM) nanocells with a conductance that can be adjusted to two or more levels by applying sequences of electrical stresses 10 , could be an efficient entropy source for TRNG circuits because they can produce random variations of different magnitudes (e.g., state resistance, switching voltage/time) during operation while consuming little energy, in some cases down to ~0.1 pJ per state transition [11][12][13][14] . References [15][16][17][18][19][20] subjected metal-oxide memristors to sequences of ramped voltage stresses (RVS) of different polarities or pulsed write/verify schemes 21,22 to induce cyclical switching between a high resistive state (HRS) and low resistive state (LRS), and used the switching voltages, cycle-to-cycle resistance variability or stochastic time-dependent relaxation as entropy source in a TRNG circuit -as the switching is related to ionic movement in the MIM nanocell, these values show some degree of variability in every cycle, and they cannot be accurately predicted. However, those studies only constructed and characterized single devices, and the circuital part was simulated or modelled.…”

“…11–14 Ref. 15–20 subjected metal oxide memristors to sequences of ramped voltage stresses (RVS) of different polarities or pulsed write/verify schemes 21,22 to induce cyclical switching between a high resistive state (HRS) and a low resistive state (LRS), and used the switching voltages, cycle-to-cycle resistance variability or stochastic time-dependent relaxation as an entropy source in a TRNG circuit—as the switching is related to ionic movement in the MIM nanocell, these values show some degree of variability in every cycle, and they cannot be accurately predicted. However, these studies only constructed and characterized single devices, and the circuital part was simulated or modelled.…”