Realizing spike-timing dependent plasticity learning rule in Pt/Cu:ZnO/Nb:STO memristors for implementing single spike based denoising autoencoder

Suresh, Bharathwaj; Boppidi, Pavan Kumar Reddy; Rao, Bo; Banerjee, Saswata; Kundu, Souvik

doi:10.1088/1361-6439/ab235f

Cited by 15 publications

(16 citation statements)

References 40 publications

(75 reference statements)

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“…The magnitude of the corresponding voltage is 0.50 V which is less than the Set voltage of Cu:ZnO memristor. R i j is the resistance of the memristor at the ith row and jth column and is expressed in (11) [30][31][32] of VTEAM memristor model:…”

Section: Methodologiesmentioning

confidence: 99%

“…where u is a unit step function of applied voltage v. K off and K on are the constants which control the transition from one state to another. Whereas, α off and α on are also the constants which were used to fit non-linearity [30,31]. Equation (9) gives us the way in which weights were stored in terms of X and (11) tells us how X affects the resistance of a memristor.…”

Section: Methodologiesmentioning

confidence: 99%

“…The magnitude of the corresponding voltage is 0.50 V which is less than the Set voltage of Cu:ZnO memristor.

R_{i j}

is the resistance of the memristor at the ith row and jth column and is expressed in (11) [30–32] of VTEAM memristor model:

R_{i j} = ()(, R_{off} - R_{on}) \times \frac{X}{D} + R_{on}

R_{off}

is the resistance in the high resistance state. As per VTEAM model, the change in X with respect to time is given by

\begin{matrix} right leftthickmathspace.5em \\ \frac{d X}{d t} & = K_{off} {()(, \frac{v}{v_{off}} - 1)}^{α_{off}} u (v - v_{off}) \\ + K_{on} {()(, \frac{v}{v_{on}} - 1)}^{α_{on}} u (v_{on} - v) \end{matrix}

where u is a unit step function of applied voltage v.

K_{off}

and

K_{on}

are the constants which control the transition from one state to another.…”

Section: Methodologiesmentioning

confidence: 99%

“…Since the resistance of a memristor varies with the amount of current passing through the memristor, it can store the weights of the neural network and update them using simple voltage pulses [31].…”

Section: Theory Of Icamentioning

confidence: 99%

“…A Pt/Cu:ZnO/Nb:STO memristor was fabricated and the detailed fabrication procedures are provided in our earlier publication [28]. Since the resistance of a memristor varies with the amount of current passing through the memristor, it can store the weights of the neural network and update them using simple voltage pulses [31].…”

Section: Memristor As a Synapsementioning

confidence: 99%

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Implementation of fast ICA using memristor crossbar arrays for blind image source separations

Boppidi

Louis

Subramaniam

et al. 2020

IET Circuits, Devices & Systems

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Independent component analysis (ICA) is an unsupervised learning approach for computing the independent components (ICs) from the multivariate signals or data matrix. The ICs are evaluated based on the multiplication of the weight matrix with the multivariate data matrix. This study proposes a novel Pt/Cu:ZnO/Nb:STO memristor crossbar array for the implementation of both ACY ICA and Fast ICA for blind source separation. The data input was applied in the form of pulse width modulated voltages to the crossbar array and the weight of the implemented neural network is stored in the memristor. The output charges from the memristor columns are used to calculate the weight update, which is executed through the voltages kept higher than the memristor Set/Reset voltages (±1.30 V). In order to demonstrate its potential application, the proposed memristor crossbar arrays based fast ICA architecture is employed for image source separation problem. The experimental results demonstrate that the proposed approach is very effective to separate image sources, and also the contrast of the images are improved with an improvement factor in terms of percentage of structural similarity as 67.27% when compared with the software-based implementation of conventional ACY ICA and Fast ICA algorithms.

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Section: Methodologiesmentioning

confidence: 99%

Section: Methodologiesmentioning

confidence: 99%

“…The magnitude of the corresponding voltage is 0.50 V which is less than the Set voltage of Cu:ZnO memristor.

R_{i j}

is the resistance of the memristor at the ith row and jth column and is expressed in (11) [30–32] of VTEAM memristor model:

R_{i j} = ()(, R_{off} - R_{on}) \times \frac{X}{D} + R_{on}

R_{off}

is the resistance in the high resistance state. As per VTEAM model, the change in X with respect to time is given by

\begin{matrix} right leftthickmathspace.5em \\ \frac{d X}{d t} & = K_{off} {()(, \frac{v}{v_{off}} - 1)}^{α_{off}} u (v - v_{off}) \\ + K_{on} {()(, \frac{v}{v_{on}} - 1)}^{α_{on}} u (v_{on} - v) \end{matrix}

where u is a unit step function of applied voltage v.

K_{off}

and

K_{on}

are the constants which control the transition from one state to another.…”

Section: Methodologiesmentioning

confidence: 99%

Section: Theory Of Icamentioning

confidence: 99%

Section: Memristor As a Synapsementioning

confidence: 99%

See 3 more Smart Citations

Implementation of fast ICA using memristor crossbar arrays for blind image source separations

Boppidi

Louis

Subramaniam

et al. 2020

IET Circuits, Devices & Systems

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Speckle Pattern Analysis of PVK:rGO Composite Based Memristor Device

Jamali,

Sajjadi,

Taherkhani

et al. 2024

Macro Materials & Eng

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The memristors are expected to be fundamental devices for neuromorphic systems and switching applications. The device made of a sandwiched layer of poly(N‐ vinylcarbazole) and reduced graphene composite between asymmetric electrodes (ITO/PVK:rGO/Al) exhibits bistable resistive switching behavior. The performance of the memristor can be optimized by controlling the doped graphene oxide. To assess the device performance when it switches between ON and OFF states, optical characterization approaches are highly promising due to their non‐destructive and remote nature. Here, speckle pattern (SP) analysis to this end is introduced. SPs include a huge amount of information about their generating mechanism, which is extracted through statistical elaboration. SPs of the PVK:rGO in different states in situ and examine the conduction mechanism is acquired. The variations in the statistical parameters are attributed to the resistance state of the PVK:rGO with regard to the physical switching mechanism. The resistance/conduction state, in turn, depends on the activity and properties of PVK:rGO memristors, as well as the additional non‐uniformities induced through the variations of density of carriers. The present optical methodology can be potentially served as a bench‐top device for characterization purposes of similar devices during their operating.

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Emulation of bio-synaptic behaviours in copper-doped zinc oxide memristors: A nanoscale scanning probe microscopic study

Mandal

Mitra

et al. 2022

Applied Surface Science

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Realizing spike-timing dependent plasticity learning rule in Pt/Cu:ZnO/Nb:STO memristors for implementing single spike based denoising autoencoder

Cited by 15 publications

References 40 publications

Implementation of fast ICA using memristor crossbar arrays for blind image source separations

Implementation of fast ICA using memristor crossbar arrays for blind image source separations

Speckle Pattern Analysis of PVK:rGO Composite Based Memristor Device

Emulation of bio-synaptic behaviours in copper-doped zinc oxide memristors: A nanoscale scanning probe microscopic study

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