Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

Abstract

Some memristors with metal/insulator/metal (MIM) structure have exhibited random telegraph noise (RTN) current signals, which makes them ideal to build true random number generators (TRNG) for advanced data encryption. However, there is still no clear guide on how essential manufacturing parameters like materials selection, thicknesses, deposition methods, and device lateral size can influence the quality of the RTN signal. In this paper, an exhaustive statistical analysis on the quality of the RTN signals produced by different MIM‐like memristors is reported, and straightforward guidelines for the fabrication of memristors with enhanced RTN performance are presented, which are: i) Ni and Ti electrodes show better RTN than Au electrodes, ii) the 50 μm × 50 μm devices show better RTN than the 5 μm × 5 μm ones, iii) TiO2 shows better RTN than HfO2 and Al2O3, iv) sputtered‐oxides show better RTN than ALD‐oxides, and v) 10 nm thick oxides show better RTN than 5 nm thick oxides. The RTN signals recorded have been used as entropy sources in high‐throughput TRNG circuits, which have passed the randomness tests of the National Institute of Standards and Technology. The work can serve as a useful guide for materials scientists and electronic engineers when fabricating MIM‐like memristors for RTN applications.

Standards for the Characterization of Endurance in Resistive Switching Devices

“…In RS devices, the set and reset transitions are induced by applying electrical stresses to the metallic electrodes, which produce the modification of the atomic structure of the insulator or semiconductor between them . While the magnitude of the stress-induced structural changes can be roughly adjusted by tuning the voltage, duration, and separation of the PVS, accurate control of the number of atoms moved in each cycle and their position in the device has never been achieved and it is considered to be impossible. , Therefore, the conductance in LRS (after set) and in HRS (after reset) can be very different after each RS cycle (Figure a,b), , and the switching voltages, times, and energies may also be slightly different in each cycle, which is why it is often said that the RS is a stochastic phenomenon. , In fact, the cycle-to-cycle variations of R HRS , R LRS , V SET , V RESET , E SET , E RESET , t SET , and t RESET are so unpredictable that they have been employed as an entropy source in true random number generators (TRNG) and physical unclonable functions (PUF) for data encryption. − …”

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

“…This is further verified by the results of NIST SP 800-22 statistical test suite results given in Table S1. To benchmark the performance of the TRNG demonstrated in this work, the proposed device is compared with other TRNGs in the literature, [3][4][5][6]9,11,23,[30][31][32][33][34] which employ different physical processes in various platforms for random number generation, the results of which are given in Table S2.…”

“…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, 3 multi-modal ring oscillators, 4 random Raman fiber lasers, 5 memristors, [6][7][8] amplified spontaneous emission, 9 photon arrival time measurements, 10 superparamagnetic tunnel junctions, 11 carbon nanotube transistors, 12 etc. have been demonstrated recently.…”

“…A battery of tests as recommended in the National Institute of Standards and Technology (NIST) SP 800-22 statistical test suite 14 are typically employed in literature to assess the randomness of the generated output bits. 5,6,[9][10][11][12][13] We wish to emphasize that the NIST SP 800-22 battery of tests does not scrutinize the true randomness but instead analyzes the output of a true/pseudo-random source for features desirable for applications in cryptography. On the other hand, entropy has been in use in various fields of study as a measure to quantify the degree of uncertainty present in a system and was introduced to information theory by Shannon in his seminal paper.…”

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.…”