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Markov Chain Signal Generation based on Single Magnetic Tunnel Junction

Yuan, X, Jian, J, Chai, Z, An, S, Gao, Y, Zhou, X, Zhang, JF, Zhang, W and Min, T (2023) Markov Chain Signal Generation based on Single Magnetic Tunnel Junction. IEEE Electron Device Letters. p. 1. ISSN 0741-3106

Markov Chain Signal Generation based on Single Magnetic Tunnel Junction.pdf - Accepted Version

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Markov chain (MC) is a stochastic model that describes a sequence of events where the probability of each event depends only on the previous state. Such memoryless property makes MC widely used in machine learning and encryption, but the hardware implementation of MC generation remains challenging. This paper presents a hardware solution for generating MC signals using only one industry-ready magnetic tunnel junction (MTJ). High quality standard MC signal has been generated with low error and good randomness. The proposed solution also demonstrates the potential in increasing the generation speed. The presented solution offers a hardware-friendly implementation of MC signal in semiconductor chips.

Item Type: Article
Additional Information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords: 0906 Electrical and Electronic Engineering; Applied Physics
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor: A Symplectic
Date Deposited: 16 Oct 2023 16:07
Last Modified: 17 Oct 2023 13:54
DOI or ID number: 10.1109/led.2023.3322194
URI: https://researchonline.ljmu.ac.uk/id/eprint/21717
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