Development of resistive memories based on silver doped graphene oxide for neuron simulation

Sparvoli, Marina; Marma, Jonas S.

doi:10.1109/ijcnn.2018.8489460

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The researchers are investigating using graphene or graphene oxide (GO) as electrodes or switching material of RRAM targeting in-memory computing for neuromorphic behavior (Izam et al, 2016 ; Liu et al, 2018 ; Yan et al, 2018 ; Abunahla et al, 2020a ). The control of resistance for multiple states by memorizing the previous state enables to mimic of biological synapses in the human brain neural network (Sparvoli and Marma, 2018 ; Xu et al, 2019b ; Schranghamer et al, 2020 ; Kireev et al, 2022 ). With the large development in memristive materials, an excessive amount of work is being conducted in 2D materials-based memristors for neuromorphic computing (Abunahla et al, 2020a , b ; Alimkhanuly et al, 2021 ).…”

Section: Graphene-based Rram Applicationsmentioning

confidence: 99%

Section: Graphene-based Rram Applicationsmentioning

confidence: 99%

“…The results in Dugu et al (2018) show that graphene shows a low SET/RESET current/voltage in comparison with conventional RRAM electrodes such as Pt. A perceptron model is experimentally in Sparvoli and Marma (2018). Lu et al (2022) have developed a two-terminal memristor synapse based on a silicon-argon composite film.…”

Section: Memorymentioning

confidence: 99%

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Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

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Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

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Section: Graphene-based Rram Applicationsmentioning

confidence: 99%

Section: Graphene-based Rram Applicationsmentioning

confidence: 99%

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Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

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NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks

Abunahla

Halawani

Alazzam

et al. 2020

Sci Rep

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Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the RON and ROFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

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Integrated graphene oxide resistive element in tunable RF filters

Abunahla

Gadhafi

Mohammad

et al. 2020

Sci Rep

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Adaptable communication systems are of great interest as they provide dynamic front end to accommodate the tunable spectrum management in advanced wireless systems. Memristor (acronym of memory resistor) is an emerging technology part of resistive RAM (RRAM) that has good potential for application in reconfigurable RF devices. The potentiality of using resistive switches for frequency tuning of high frequency RF filters is successfully explored in this article for the first time. Tunable RF filter is designed with detailed simulation using Ansys HFSS, and then correlated with measured results from experiment. As a proof of concept, a prototype of the tunable RF filter is fabricated by using a graphene oxide (GO) integrated with a conventional microstrip open stub notch filter. The resistor switching ability of the device is exploited for the frequency tuning. The resonating length of the notch filter is varied by changing the resistance of the active GO material between ‘HIGH’ (OFF) and ‘LOW’ (ON) resistance states. The measured results demonstrate the great potential of using RRAM in tunable RF devices. It also proves the possibility of tuning RF devices without any localized surface mount device (SMD) element or complex realization technique.

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Development of resistive memories based on silver doped graphene oxide for neuron simulation

Cited by 4 publications

References 10 publications

Graphene-based RRAM devices for neural computing

Graphene-based RRAM devices for neural computing

NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks

Integrated graphene oxide resistive element in tunable RF filters

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