Lightweight TRNG Based on Multiphase Timing of Bistables

Abstract

The paper presents a concept of a True Random Number Generator (TRNG) that utilizes phase noise of a pair of ring oscillators (ROs) to increase the variance of the initial condition of a bistable. For this purpose a special TRNG D-latch architecture (TDL) has been proposed, which can either operate in the oscillatory ring-oscillator mode or the nearly-metastable mode. The RO mode increases the probability of the nearly metastable operation of the TDL, which in turn increases the mean value and variance of the resolve time. Moreover, due to the oscillatory nature of the TDL metastability, the resolve time can be easily measured and used for further randomness harvesting in the TRNG. The proposed TRNG uses a pair of TDLs to reduce sensitivity to process or temperature variation, whereas TDLs' individual resolve time, their resolve time difference and logical state contribute to randomness. In the article, the impact of the devices' imbalance (tolerance) resulting from process variation on the resultant entropy is also explored. The proposed TRNG based on TDLs is scalable and has been implemented in modern CPLD and FPGA devices whereas the bit rate reached up to 1 Mbit. The article also discusses theoretical issues related to a transformation of phase and resolve time probability density functions and their influence on TRNG parameters.

Chaos-Based Physical Unclonable Functions

“…All the parameters R o , C o , t pd , N(t) (standard deviation) and inclination of the transfer function have been adjusted to obtain identical operation of SCROs in the Simulink model and physical implementation. The detailed method of parameter extraction is explained in [48]. This way, we have obtained identical phase-walk (and jitter), f 1 and f 2 frequencies of the model and the circuit physically implemented in a programmable device.…”

“…This way, we obtained the standard deviation of noise process for various temperatures in different devices (FPGA). The method was explained in details in [48]; moreover, it was also utilized for the measurements of thermal drift of average propagation delay, not resulting from the noise processes. Both the noise standard deviation and propagation delay thermal coefficients were used in the behavioral modeling (in Simulink), in order to evaluate the temperature impact on the critical m-value.…”

Secure and Efficient Hybrid Random Number Generator Based on Sponge Constructions for Cryptographic Applications

“…Wieczorek presents a generator that utilizes both the ring oscillations and metastability phenomena. The proposed generator is successful in the randomness tests [23]. Wieczorek proposes and analyses TRNG based on nearly-metastable operation of groups of FPGA flip-flops and an adaptive feedback loop.…”

“…This change is expressed as 12 + 2 × l. For example, when the 1600-bit input vector is considered, the value of l becomes 6 and the expression of 12 + 2 × l provides 24 rounds. For security reasons, it is desirable that the vector size should be large [23].…”

Dynamic control of entropy and power consumption in TRNGs for IoT applications

“…The increasing necessity of embedded security in a wide variety of applications has spawned a proliferation of TRNGs in the scientific literature [10,11]. TRNGs that use sampling of jittery clocks as the entropy source stand out among these many proposals.…”