Design and analysis of 2T2M hybrid CMOS-Memristor based RRAM

Shaarawy, Noha; Emara, Ahmed; El-Naggar, Ahmed M.; Elbtity, Mohammed; Ghoneima, Maged; Radwan, Ahmed G.

doi:10.1016/j.mejo.2018.01.001

Cited by 26 publications

(8 citation statements)

References 19 publications

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“…In the write mode, the access transistors are enabled, and the word on the data line is stored in the memristors devices. 28,53 The data are stored in a complementary fashion in the 2T2M bit-cell, which is the opposite of the regular memories. In the sense that if one memristor sets to be in a high resistance state, the opposite one should be set in the low resistance state.…”

Section: T2m Bit-cell Based Re-tcammentioning

confidence: 99%

“…Each branch comprises one memristor to store the data and one MOS transistor to access the data. In the write mode, the access transistors are enabled, and the word on the data line is stored in the memristors devices 28,53 . The data are stored in a complementary fashion in the 2T2M bit‐cell, which is the opposite of the regular memories.…”

Section: Introductionmentioning

confidence: 99%

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Variability analysis of resistive ternary content addressable memories

Bahloul

Fouda

Barraj

et al. 2020

Circuit Theory & Apps

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Resistive ternary content addressable memories (Re-TCAMs) are considered a potential platform for in-memory associative processing. Re-TCAM permits the store and process of the data in the same physical area, allowing inmemory computing. Given the unique properties of the non-volatile memristive device, Re-TCAM is capable of low search energy, large-scale storage, and massively parallel processing. However, like any other semiconductor, memristor as a nanodevice suffers from fabrication process imperfections that provoke a significant variance in their high (R H) and low (R L) resistance states. These variations might lead to the malfunction of the Re-TCAM array and affect its performance and reliability. This paper proposes a simplified mathematical formulation of the memristor state variability for 2T2M bit-cell-based Re-TCAM array. We present a comprehensive statistical design, considering different circuit parameters and variance effects, such as tolerance of memristance variation, the memristor ratio (α = R H =R L), transistor technology, and memory width. The impact of these parameters on the performance is formulated as the probability of error is extensively studied, and a closed-form expression for the optimal threshold voltage is derived. The utility of the presented investigation is illustrated using a design example with real device parameters.

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Section: T2m Bit-cell Based Re-tcammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variability analysis of resistive ternary content addressable memories

Bahloul

Fouda

Barraj

et al. 2020

Circuit Theory & Apps

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“…Oxide-based memristors are new generation memory devices due to their low power consumption, high switching speed, high density, and compatibility with complementary metal–oxide–semiconductor (COMS) processes. − Oxide-based memristors can be classified into two types: anionic oxide memristors which mainly employ migration of oxygen vacancies and cation oxide memristors based on the cation drift and related phenomena. − Although these devices have potential in multistate storage and neuromorphic computing, their application has been limited by the fluctuation of resistance states. LiCoO 2 -based memristors deliver improved performance due to highly reversible intercalation and extraction of lithium ions under an external electric field.…”

Section: Introductionmentioning

confidence: 99%

Stability and Repeatability of a Karst-like Hierarchical Porous Silicon Oxide-Based Memristor

Gao

Huang

et al. 2019

ACS Appl. Mater. Interfaces

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A memristor architecture based on porous oxide materials has the potential to be used in artificial synaptic devices. Herein, we present a memristor system employing a karst-like hierarchically porous (KLHP) silicon oxide structure with good stability and repeatability. The KLHP structure prepared by an electrochemical process and thermal oxidation exhibits high ON-OFF ratios up to 105 during the endurance test, and the data can be maintained for 105 s at a small read voltage 0.1 V. The mechanism of lithium ion migration in the porous silicon oxide structure has been discussed by a simulated model. The porous silicon oxide-based memristor is very promising because of the enhanced performance as well as easily accessed neuromorphic computing.

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“…There are two main RRAM memory architectures: crossbar [90,[107][108][109] and grid [110][111][112][113]. The crossbar approach does not have access transistors in its memory cell topology.…”

Section: Memory Applicationsmentioning

confidence: 99%

Memristors: A Journey from Material Engineering to Beyond Von-Neumann Computing

Dias

Butzen

2021

JICS

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Memristors are a promising building block to the next generation of computing systems. Since 2008, when the physical implementation of a memristor was first postulated, the scientific community has shown a growing interest in this emerging technology. Thus, many other memristive devices have been studied, exploring a large variety of materials and properties. Furthermore, in order to support the design of practical applications, models in different abstract levels have been developed. In fact, a substantial effort has been devoted to the development of memristive based applications, which includes high-density nonvolatile memories, digital and analog circuits, as well as bio-inspired computing. In this context, this paper presents a survey, in hopes of summarizing the highlights of the literature in the last decade.

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Design and analysis of 2T2M hybrid CMOS-Memristor based RRAM

Cited by 26 publications

References 19 publications

Variability analysis of resistive ternary content addressable memories

Variability analysis of resistive ternary content addressable memories

Stability and Repeatability of a Karst-like Hierarchical Porous Silicon Oxide-Based Memristor

Memristors: A Journey from Material Engineering to Beyond Von-Neumann Computing

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