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.
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14 June 2017
Research Article|
June 14 2017
A spin-based true random number generator exploiting the stochastic precessional switching of nanomagnets
Nikhil Rangarajan;
Nikhil Rangarajan
a)
Department of Electrical Engineering, New York University
, New York, New York 11201, USA
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Arun Parthasarathy;
Arun Parthasarathy
Department of Electrical Engineering, New York University
, New York, New York 11201, USA
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Shaloo Rakheja
Shaloo Rakheja
Department of Electrical Engineering, New York University
, New York, New York 11201, USA
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a)
Electronic mail: [email protected]
J. Appl. Phys. 121, 223905 (2017)
Article history
Received:
February 18 2017
Accepted:
June 01 2017
Citation
Nikhil Rangarajan, Arun Parthasarathy, Shaloo Rakheja; A spin-based true random number generator exploiting the stochastic precessional switching of nanomagnets. J. Appl. Phys. 14 June 2017; 121 (22): 223905. https://doi.org/10.1063/1.4985702
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