Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE

Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. However, limited endurance of memristor devices and variations (both cycle-to-cycle and device-to-device) are important parameters to be considered in the evaluation especially...

Full description

Saved in:

Bibliographic Details
Main Author:	Ng, Yuh Chyn
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99548/1/NgYuhChynMSKE2022.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.99548
record_format	uketd_dc
spelling	my-utm-ep.995482023-02-28T08:26:27Z Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE 2022 Ng, Yuh Chyn TK Electrical engineering. Electronics Nuclear engineering Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. However, limited endurance of memristor devices and variations (both cycle-to-cycle and device-to-device) are important parameters to be considered in the evaluation especially in memristive logic families. There are a lot of factors on memristor variability, such as the influence of temperature, influence of switching the pulse parameters, influence of the concentration of oxygen vacancies, active dielectric layer structure and thickness as well as the influence of the parameters of conducting cell electrodes. In this work, only cycle-to-cycle variation is focus on both the deterministic and probabilistic behaviour in a memristor is being simulated and compared using LTSPICE software. Knowm or Mean Metastable Switch (MMS) SPICE model is being used to present the behaviour of a memristor. Monte Carlo simulation is applied to show the probabilistic behaviour in memristor. In summary, the best practical for probabilistic memristor model is within 50% range in terms of these model parameters (VON, VOFF, RON, ROFF). Besides, the impact of variability of memristors on the performance at in-memory logic circuit using different logic design styles such as Memristor-Aided Logic (MAGIC) and Memristor Ratioed Logic (MRL) are being implemented and analysed based on a universal NOR gate. The performance analysis of implementation of both MAGIC and MRL is carried out with respect to the functionality and sensitivity after applying the fluctuation. In this work, MRL design style is more robust and less affect by the cycle-to-cycle variability. 2022 Thesis http://eprints.utm.my/id/eprint/99548/ http://eprints.utm.my/id/eprint/99548/1/NgYuhChynMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149928 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ng, Yuh Chyn Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
description	Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. However, limited endurance of memristor devices and variations (both cycle-to-cycle and device-to-device) are important parameters to be considered in the evaluation especially in memristive logic families. There are a lot of factors on memristor variability, such as the influence of temperature, influence of switching the pulse parameters, influence of the concentration of oxygen vacancies, active dielectric layer structure and thickness as well as the influence of the parameters of conducting cell electrodes. In this work, only cycle-to-cycle variation is focus on both the deterministic and probabilistic behaviour in a memristor is being simulated and compared using LTSPICE software. Knowm or Mean Metastable Switch (MMS) SPICE model is being used to present the behaviour of a memristor. Monte Carlo simulation is applied to show the probabilistic behaviour in memristor. In summary, the best practical for probabilistic memristor model is within 50% range in terms of these model parameters (VON, VOFF, RON, ROFF). Besides, the impact of variability of memristors on the performance at in-memory logic circuit using different logic design styles such as Memristor-Aided Logic (MAGIC) and Memristor Ratioed Logic (MRL) are being implemented and analysed based on a universal NOR gate. The performance analysis of implementation of both MAGIC and MRL is carried out with respect to the functionality and sensitivity after applying the fluctuation. In this work, MRL design style is more robust and less affect by the cycle-to-cycle variability.
format	Thesis
qualification_level	Master's degree
author	Ng, Yuh Chyn
author_facet	Ng, Yuh Chyn
author_sort	Ng, Yuh Chyn
title	Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
title_short	Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
title_full	Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
title_fullStr	Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
title_full_unstemmed	Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE
title_sort	simulation of in-memory logic circuit based on probabilistic memristor using ltspice
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99548/1/NgYuhChynMSKE2022.pdf
_version_	1776100617012379648

Simulation of in-memory logic circuit based on probabilistic memristor using LTSPICE

Similar Items