1T Spiking Neuron Using Ferroelectric Junctionless FET with Ultra-Low Energy Consumption of 24 aJ/Spike

Khanday, Mudasir A.; Rashid, Shazia; Khanday, Farooq Ahmad

doi:10.1007/s11063-023-11387-x

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…The sharp transition region, shown in Figure 2C, has been exploited to get spiking behavior with least energy consumption. This eliminates the need of special post‐CMOS devices and complex additional circuitry reported in previous studies 10–29 . The neuronal operation of the proposed design can be understood from Figure 2A.…”

Section: Concept and Operation Of The Proposed Designmentioning

confidence: 87%

“…Various CMOS and post‐CMOS devices have been used for electrical implementation of an LIF neuron, as proposed in the literature 10–29 . To obtain the energy‐efficient behavior, devices with sharp breakdown characteristics have been preferred.…”

Section: Concept and Operation Of The Proposed Designmentioning

confidence: 99%

“…Two‐terminal devices, such as neuristor, 17 biristors, 18,19 RRAM, 20,21 and PCM, 22 offer energy efficiency but demand complex additional circuitry to accurately replicate neural behavior and lack control over spiking patterns. The current emphasis lies on Field Effect Transistor (FET) based devices discussed in references 23–29. These can further be classified as impact ionization (II) based devices and tunneling based devices.…”

Section: Introductionmentioning

confidence: 99%

“…However, a notable drawback of these II‐based devices is their significant energy consumption, attributed to their reliance on high voltage processes. Among the emerging tunneling‐based devices, the tunnel FET, 26 junctionless FET (JLFET), 27,28 and ferroelectric field‐effect transistors (FeFETs) 29 stand out for their remarkable energy efficiency. However, there are trade‐offs associated with these devices.…”

Section: Introductionmentioning

confidence: 99%

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An energy‐efficient tunable threshold spiking neuron with excitatory and inhibitory function

Khanday,

Khanday

2024

Int J Numerical Modelling

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In this work, a complementary metal‐oxide‐semiconductor (CMOS) based leaky‐integrate and fire neuron has been proposed and investigated for neuromorphic applications. The neuron has been designed in Cadence Virtuoso and validated experimentally. It has been observed that the neuron consumes a maximum energy of 68.87 fJ/spike. The response of the neuron to excitatory as well as inhibitory inputs has been studied. To verify the applicability, the proposed neuron has been explored for reconfigurable threshold logic to implement various linearly separable Boolean functions including OR, AND, NOT, NOR, and NAND. Moreover, the threshold tunability of the neuron has also been verified and this property has been exploited to design threshold‐controlled logic gates. Instead of adjusting the weights of the applied inputs, the functionality of such gates can be controlled by changing the threshold of the neuron, simplifying the synaptic architecture of a neural network. Finally, a multilayer network has been designed and the recognition ability of the proposed network for MNIST handwritten digits has been verified with an accuracy of 96.93%.

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Section: Concept and Operation Of The Proposed Designmentioning

confidence: 87%

Section: Concept and Operation Of The Proposed Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An energy‐efficient tunable threshold spiking neuron with excitatory and inhibitory function

Khanday,

Khanday

2024

Int J Numerical Modelling

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SG‐FET Based Spiking Neuron With Ultra‐Low Energy Consumption for ECG Signal Classification

Zargar,

Khanday,

Khanday

2024

Int J Numerical Modelling

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This paper presents an energy‐efficient single‐transistor leaky integrate‐and‐fire neuron, based on Suspended Gate‐FET (SG‐FET), for signal classification and neuromorphic computing applications. By leveraging the SG‐FET model, extensive simulations were conducted to demonstrate the device's remarkable neuronal ability. The device faithfully emulated the intricate behaviour of biological neurons, without the need for external circuitry. One of the standout achievements lies in the device's astonishingly low energy consumption of 94.5 aJ per spike. Therefore, it outperforms the previously proposed one‐transistor (1‐T) neurons, which makes it a potential candidate for energy‐efficient neuromorphic computing. To verify the practical viability of the device, an emulation was seamlessly integrated into a spiking neural network framework, allowing for real‐time signal classification. In this specific case, the device excelled in the classification of electrocardiogram (ECG) signals, achieving an impressive accuracy rate of 85.6%. This outcome highlights the device's efficacy in handling real‐world signal processing tasks with remarkable precision and efficiency.

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1T Spiking Neuron Using Ferroelectric Junctionless FET with Ultra-Low Energy Consumption of 24 aJ/Spike

Cited by 2 publications

References 27 publications

An energy‐efficient tunable threshold spiking neuron with excitatory and inhibitory function

An energy‐efficient tunable threshold spiking neuron with excitatory and inhibitory function

SG‐FET Based Spiking Neuron With Ultra‐Low Energy Consumption for ECG Signal Classification

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