A Versatile Hardware Solution for Generating Power Law Noise with Tunable Long-Term Dependency

Yuan, X, Luo, Y, Chai, Z, Jian, J, Zhou, X, Zhao, F, Yue, X, Zhang, JF ORCID: 0000-0003-4987-6428, Zhang, W ORCID: 0000-0003-4600-7382 and Min, T (2025) A Versatile Hardware Solution for Generating Power Law Noise with Tunable Long-Term Dependency. IEEE Electron Device Letters, 46 (8). ISSN 0741-3106

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Abstract

Power law noise (PLN) has promising applications in healthcare and artificial intelligence (AI). In this paper, we propose a hardware solution for generating PLN with tunable long-term dependency, by combining the magnetic tunnel junction (MTJ) based two-state Markov chain (MC) generation and modulation method. PLNs with specific parameter α can be obtained by merging multiple two-state MCs. The merged PLN, with α ranging from 0.2 to 1.8 and adjustable long-term dependency as represented by the autocorrelation function (ACF), is demonstrated by varying the voltage conditions applied to the MTJs. This method provides a versatile hardware solution for constructing stochastic signal in semiconductor IC chips.

Item Type:	Article
Additional Information:	© 2025 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:	40 Engineering; 4009 Electronics, Sensors and Digital Hardware; 0906 Electrical and Electronic Engineering; Applied Physics; 4009 Electronics, sensors and digital hardware
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date of acceptance:	3 June 2025
Date of first compliant Open Access:	6 August 2025
Date Deposited:	06 Aug 2025 13:01
Last Modified:	06 Aug 2025 13:15
DOI or ID number:	10.1109/LED.2025.3577161
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26904

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