Emulation of Synaptic Plasticity on Cobalt based Synaptic Transistor for Neuromorphic Computing

TL;DR

This paper presents a cobalt-based synaptic transistor capable of emulating key neural functions like plasticity, learning, and memory, advancing neuromorphic computing hardware with multilevel conductance states and dynamic filtering.

Contribution

It introduces a novel metallic channel-based synaptic transistor demonstrating emulation of biological synaptic behaviors and transition from short-term to long-term memory.

Findings

Successfully emulated short-term and long-term plasticity.

Achieved multilevel, nonvolatile conductance states.

Demonstrated learning, forgetting, and relearning behaviors.

Abstract

Neuromorphic Computing (NC), which emulates neural activities of the human brain, is considered for low-power implementation of artificial intelligence. Towards realizing NC, fabrication, and investigations of hardware elements such as synaptic devices and neurons are essential. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using a wide variety of materials. However, studies on metallic channel based synaptic transistors remain vastly unexplored. Here, we have demonstrated a three-terminal cobalt-based synaptic transistor to emulate biological synapse. We realized gating controlled multilevel, nonvolatile conducting states in the proposed device. The device could successfully emulate essential synaptic functions demonstrating short-term and long-term plasticity. A transition from short-term memory to long-term memory has been realized by tuning gate pulse amplitude and duration. The crucial cognitive behavior viz., learning, forgetting, and relearning, has been emulated, showing resemblance to the human brain. Along with learning and memory, the device showed dynamic filtering behavior. These results provide an insight into the design of metallic channel based synaptic transistors for neuromorphic computing.