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LIF neuron —a memristive realization

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This study introduces a pioneering design for leaky integrate-and-fire (LIF) neurons by integrating memristor devices with CMOS transistors, thereby forming an innovative hybrid CMOS/memristor neuron circuit. Employing Pt/TaOx/Ta as the memristor device, the proposed model was meticulously implemented and rigorously evaluated using the Cadence Virtuoso simulation environment. The simulation outcomes affirm the effective functionality of the design, marking a significant advancement in hybrid circuit engineering. Notably, the proposed neuron circuit exhibits a compact footprint, attributed to the efficient utilization of hybrid CMOS/memristor gates. This characteristic is poised to address the critical challenge of scaling in current neuromorphic systems, offering a viable pathway to substantially augment density and cater to the escalating demands of advanced computational architectures. The findings of this research hold promising implications for enhancing the efficiency and scalability of neuromorphic systems, setting a new benchmark for future developments in this domain.
Title: LIF neuron —a memristive realization
Description:
This study introduces a pioneering design for leaky integrate-and-fire (LIF) neurons by integrating memristor devices with CMOS transistors, thereby forming an innovative hybrid CMOS/memristor neuron circuit.
Employing Pt/TaOx/Ta as the memristor device, the proposed model was meticulously implemented and rigorously evaluated using the Cadence Virtuoso simulation environment.
The simulation outcomes affirm the effective functionality of the design, marking a significant advancement in hybrid circuit engineering.
Notably, the proposed neuron circuit exhibits a compact footprint, attributed to the efficient utilization of hybrid CMOS/memristor gates.
This characteristic is poised to address the critical challenge of scaling in current neuromorphic systems, offering a viable pathway to substantially augment density and cater to the escalating demands of advanced computational architectures.
The findings of this research hold promising implications for enhancing the efficiency and scalability of neuromorphic systems, setting a new benchmark for future developments in this domain.

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