Synaptic Barristor Based on Phase-Engineered Two-Dimensional Heterostructures
ORAL
Abstract
Heterostructures built from various two-dimensional (2D) layered materials are emerging material platforms for low-power and high-performance electronic devices because of their high-quality heterointerfaces.
Here, we report a new class of artificial synaptic architecture, a three-terminal device consisting of a monolithically integrated WOx memristor and a variable-barrier WSe2/graphene Schottky diode, termed as a ‘synaptic barristor’. The device can implement essential synaptic characteristics, such as short-term plasticity and long-term plasticity. Owing to the electrostatically controlled barrier height in the ultrathin vdW heterostructure, the device exhibits gate-controlled memristive switching characteristics with tunable programming voltages of 0.2−0.5 V. Notably, by electrostatic tuning with a gate terminal, we can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. These capabilities enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks. Our demonstration represents an important step toward highly networked and energy-efficient neuromorphic circuits.
Here, we report a new class of artificial synaptic architecture, a three-terminal device consisting of a monolithically integrated WOx memristor and a variable-barrier WSe2/graphene Schottky diode, termed as a ‘synaptic barristor’. The device can implement essential synaptic characteristics, such as short-term plasticity and long-term plasticity. Owing to the electrostatically controlled barrier height in the ultrathin vdW heterostructure, the device exhibits gate-controlled memristive switching characteristics with tunable programming voltages of 0.2−0.5 V. Notably, by electrostatic tuning with a gate terminal, we can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. These capabilities enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks. Our demonstration represents an important step toward highly networked and energy-efficient neuromorphic circuits.
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Presenters
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Woong Huh
- Korea University