Random-telegraph-noise-enabled true random number generator for hardware security

Abstract

The future security of Internet of Things is a key concern in the cyber-security field. One of the key issues is the ability to generate random numbers with strict power and area constrains. “True Random Number Generators” have been presented as a potential solution to this problem but improvements in output bit rate, power consumption, and design complexity must be made. In this work we present a novel and experimentally verified “True Random Number Generator” that uses exclusively conventional CMOS technology as well as offering key improvements over previous designs in complexity, output bitrate, and power consumption. It uses the inherent randomness of telegraph noise in the channel current of a single CMOS transistor as an entropy source. For the first time multi-level and abnormal telegraph noise can be utilised, which greatly reduces device selectivity and offers much greater bitrates. The design is verified using a breadboard and FPGA proof of concept circuit and passes all 15 of the NIST randomness tests without any need for post-processing of the generated bitstream. The design also shows resilience against machine learning attacks performed by the LSTM neural network.

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.…”

“…Although Rojas et al [23] conceptually proposed a so-called flexible thin-film transistor-based TRNG with a static random access memory structure made by single-walled carbon nanotubes and silicon technology, actually the random performances of the TRNG under mechanically flexible state were not investigated, and the structure of this TRNG has limit flexibility that cannot be stretched or drastically bent. For this reason, more efficient, economical, flexible, and even stretchable TRNG alternative approaches are imperative for filling up the research gap of information security toward emerging flexible hardware systems.…”

An integrated method for extracting the statistical distribution of RTN time constants

“…Random Telegraph Noise (RTN) is a well known phenomenon and has been investigated for many decades [1][2][3][4][5][6][7][8][9][10]. It is generally believed that RTN originates from capture-emission of charge carriers from the conduction channel of MOSFETs by individual traps [1][2][3][4][5][6][7][8][9][10]. Early works used RTN to probe individual traps and we have learnt a lot from these works.…”

“…It has been reported that a single charge can reduce Id by 10%, a level typically used to define device lifetime [5,[11][12][13][14][15]. This has led to a lot of recent research in modelling RTN [2][3][4][5][6][7][8][9][10]. For the Monte Carlo modelling of RTN in the time domain, one needs the statistical distributions of capture and emission time (CET), RTN amplitudes, and number of traps per device.…”

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation

“…In this section we compare the performance of the PNN-TRNG reported in Figure 6 of the main text with other TRNGs reported in the literature. [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] Table S2 compares performance according to four key metrics: bit rate, whether postprocessing is required, number of bits that could be tested, and performance in standard benchmark tests (NIST Statistical Test Suite 8 ). Also indicated are the underlying device technology and the method used to generate the bit stream.…”