4.8 Article

Emulation of Synaptic Plasticity on a Cobalt-Based Synaptic Transistor for Neuromorphic Computing

Journal

ACS APPLIED MATERIALS & INTERFACES
Volume 14, Issue 9, Pages 11864-11872

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c19916

Keywords

synaptic transistor; metallic channel; multilevel states; synaptic plasticity; neuromorphic computing

Funding

  1. National Research Foundation (NRF), Singapore [NRF-CRP21-2018-003]
  2. Ministry of Education (MOE) Singapore [MOE2019-T2-1-117]
  3. Ministry of Education (MoE), India
  4. Pratiksha Trust
  5. Agency for Science, Technology and Research (A*STAR) under its AME IRG grant [A20E5c0094]

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This study demonstrated a synaptic transistor based on a metallic cobalt thin film, capable of emulating key synaptic functions and achieving gate-controlled, multilevel conductance states and cognitive behaviors, providing insights for neuromorphic computing.
Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements-such as synaptic devices and neurons-are crucial. 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 various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.

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