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A 2.22 Mb/s True Random Number Generator Based on a GeTe<sub>x</sub> Ovonic Threshold Switching Memristor

Yuyang Fu, Jinyu Wen, Lun Wang, Ling Yang, Qihang Zhu, Wenbin Zuo, Puyi Zhang, Yi Li, Hao Tong, Guokun Ma, Hao Wang, Xiangshui Miao

2023IEEE Electron Device Letters17 citationsDOI

Abstract

True random number generators (TRNGs) based on threshold switching memristors emerge as a building block for secure electronics. However, the throughputs reported in previous studies have a stark gap with the requirements of practical applications ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$ </tex-math></inline-formula> 1 Mb/s). Here, we implement a high-speed TRNG with a GeTex ovonic threshold switching (OTS) memristor. The TRNG throughput reaches 2.22 Mb/s for a single cell, which is 2.2 times faster than the prior state-of-the-art threshold-switching-based TRNG. In addition, the TRNG endurance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${2}\times {10} ^{{9}}$ </tex-math></inline-formula> bits was achieved, and the random bits passed 12 tests in the National Institute of Standards and Technology statistical test suite. Our results demonstrated that the OTS-based TRNG could provide a high–throughput and highly secure solution for edge applications.

Topics & Concepts

Random number generationMemristorGenerator (circuit theory)ThroughputComputer scienceMathematicsAlgorithmElectrical engineeringPhysicsEngineeringTelecommunicationsQuantum mechanicsPower (physics)WirelessAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesPhysical Unclonable Functions (PUFs) and Hardware Security
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