Abstract
Artificial synapses are the fundamental of building a neuron network for neuromorphic computing to overcome the bottleneck of the von Neumann system. Based on a low-temperature atomic layer deposition process, a flexible electrical synapse was proposed and showed bipolar resistive switching characteristics. With the formation and rupture of ions conductive filaments path, the conductance was modulated gradually. Under a series of pre-synaptic spikes, the device successfully emulated remarkable short-term plasticity, long-term plasticity, and forgetting behaviors. Therefore, memory and learning ability were integrated to the single flexible memristor, which are promising for the next-generation of artificial neuromorphic computing systems.
Highlights
The classical von Neumann computing scheme is suffering a bottleneck of information transfer between the processing center and storage units [1]
Long-term potentiation (LTP)/long-term depression (LTD) is vital for face classification, digital recognition, and other artificial intelligence applications based on synaptic weight modification [9–11]
The sweeping voltage was applied in a sequence of 0 → 2 V → 0 V for the set process, and the resistance turned from high-resistance state (HRS) to low-resistance state (LRS)
Summary
The classical von Neumann computing scheme is suffering a bottleneck of information transfer between the processing center and storage units [1]. Neuromorphic computing has become an attractive candidate with the ability of learning and memory in one single system [2, 3]. Electronic synapses, with the ability of mimicking bio-synaptic behavior, are the foundation of neuromorphic systems. Bio-synaptic behaviors have been emulated by various memristors, including two-terminal devices and novel three-terminal synaptic transistors based on ionic defects [4, 5]. With history-dependent conductance, memristors were reported to simulate the long-term depression (LTD) or potentiation (LTP), pair-pulse fluctuation (PPF), paired-pulse depression (PPD), and spike-timing-dependent plasticity (STDP) [6–8]. LTP/LTD is vital for face classification, digital recognition, and other artificial intelligence applications based on synaptic weight modification [9–11]. Originating from immediate post-synaptic current response, STP is widely used
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