Emulation of synaptic plasticity on cobalt-based synaptic transistor 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 elementssuch as synaptic devices and neuronsare 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 gatingmodulated 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.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThe authors acknowledge the support from the CRP grant NRF-CRP21-2018-003 of the National Research Foundation (NRF), Singapore. S.N.P. acknowledges the partial support from the Tier 2 grant MOE2019-T2-1-117 of the Ministry of Education (MOE) Singapore. P.M. thanks the Ministry of Education (MoE), India, and the Pratiksha Trust for the financial support. X.R.W. acknowledges support from the Agency for Science, Technology and Research (A*STAR) under its AME IRG grant (project no. A20E5c0094)