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Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application
268
Citations
42
References
2019
Year
EngineeringSynaptic TransmissionEmerging Memory TechnologySynaptic SignalingSocial SciencesElectronic DevicesNanoelectronicsMemory DeviceNeuromorphic EngineeringNeuromorphic DevicesNeurocomputersGrain BoundarySynaptic PlasticityNeuroengineeringElectronic MaterialsComputational NeuroscienceCellular NeuroscienceArtificial SynapsesApplied PhysicsSelector TransistorNeuroscienceBrain-like Computing
Abstract Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based artificial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS 2 grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS 2 grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning.
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