A spin-based true random number generator exploiting the stochastic precessional switching of nanomagnets

作者：Rangarajan Nikhil^*; Parthasarathy Arun; Rakheja Shaloo

来源：Journal of Applied Physics, 2017, 121(22): 223905.

DOI：10.1063/1.4985702

In this paper, we propose a spin-based true random number generator (TRNG) that uses the inherent stochasticity in nanomagnets as the source of entropy. In contrast to previous works on spin-based TRNGs, we focus on the precessional switching strategy in nanomagnets to generate a truly random sequence. Using the NIST SP 800-22 test suite for randomness, we demonstrate that the output of the proposed TRNG circuit is statistically random with 99% confidence levels. The effects of process and temperature variability on the device are studied and shown to have no effect on the quality of randomness of the device. To benchmark the performance of the TRNG in terms of area, throughput, and power, we use SPICE (Simulation Program with Integrated Circuit Emphasis)-based models of the nanomagnet and combine them with CMOS device models at the 45 nm technology node. The throughput, power, and area footprints of the proposed TRNG are shown to be better than those of existing state-of-the-art TRNGs. We identify the optimal material and geometrical parameters of the nanomagnet to minimize the energy per bit at a given throughput of the TRNG circuit. Our results provide insights into the device-level modifications that can yield significant system-level improvements. Overall, the proposed spin-based TRNG circuit shows significant robustness, reliability, and fidelity and, therefore, has a potential for on-chip implementation. Published by AIP Publishing.

• 出版日期2017-6-14