R-M and M-R Model for Interpreting Logic State of Memristor Aided NOR with Enhanced Noise Margin

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Year : 2024 | Volume : 01 | Issue : 01 | Page : 35 41
    By

    shalini singh,

  • Shyam Akashe,

Abstract

Memristive crossbar arrays are one of the fascinating subjects in the field of nanoelectronics and memory devices. The arrangement of arrays consists of grid-like structure with rows and columns of memristors which are resistive devices that can preserve their resistance state even after power is turned OFF. They have now emerged as a promising alternative to traditional memory technologies due to their high density, non-volatility and low power consumption. This paper presents the technique of reading the state of memristor via two different models M-R model and R-M model in Memristor Aided Logic (MAGIC) NOR gate for prime and optimum noise margin. A comparative analysis of both the models are presented in which the M-R model is found to be more beneficial with 5% maximum voltage drop which is a bearable range for any voltage divider. Additionally, the study presents a thorough methodology for determining noise margins in order to assess the dependability and stability of logic circuits. The resilience of memristor-based logic devices in practical applications is determined by this analysis, which guarantees performance under a range of operating situations.

Keywords: Memristor, Magic NOR, Model, Noise Margin, Voltage Divider, Memory

[This article belongs to Recent Trends in Mathematics ]

How to cite this article:
shalini singh, Shyam Akashe. R-M and M-R Model for Interpreting Logic State of Memristor Aided NOR with Enhanced Noise Margin. Recent Trends in Mathematics. 2024; 01(01):35-41.
How to cite this URL:
shalini singh, Shyam Akashe. R-M and M-R Model for Interpreting Logic State of Memristor Aided NOR with Enhanced Noise Margin. Recent Trends in Mathematics. 2024; 01(01):35-41. Available from: https://journals.stmjournals.com/rtm/article=2024/view=189915


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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received 02/09/2024
Accepted 30/09/2024
Published 17/12/2024
217

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