Memristive CNN for Wafer defect detection

Chithra, R; A.R, Aswani; James, Alex Pappachen

doi:10.1109/iscas48785.2022.9937406

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…There are several analog memristive crossbar implementations of CNN architecture (R et al, 2022 ). Figure 5 shows the hardware implementation of CNN consisting of a convolution layer, mean pooling layer, and dense layers.…”

Section: Neuro-memristive Computingmentioning

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: Neuro-memristive Computingmentioning

confidence: 99%

“…The features are then fed to the pooling layer circuit. The pooling layer reduces the dimensionality by performing mean-pooling operation (R et al, 2022 ). The output of the mean-pool operation is flattened and is connected to dense layers for classification.…”

Section: Neuro-memristive Computingmentioning

confidence: 99%

Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

Memristive CNN for Wafer defect detection

Cited by 1 publication

References 12 publications

Graphene-based RRAM devices for neural computing

Graphene-based RRAM devices for neural computing

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