Synapse design based on memristor

The memristor is a passive two-terminal electrical device where its conductance is accurately modulated either by the charge or the flux flowing through it. In this paper, we implement a simple and flexible Verilog-A memristor model in order to perform synaptic functions, including both long-term potentiation LTP and long-term depression LTD, to subsequently validate the Hebbian learning algorithm (STDP). Our simulation results reveal the I-V characteristic of a Ta
₂
O
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-based memristive device. We prove that the Verilog-A model is able to reproduce the conductance change in the STDP learning method. Therefore, the use of memristors as synapses in neuromorphic circuits may potentially provide high connectivity and high-density area for efficient computation.