Weighted synaptic behaviors of HfON based RRAM device by a novel waveform modulation method

In this work, a novel waveform approach based on two synaptic plasticity learning rules was proposed to investigate the characteristic of RRAM for electronic synapse application. The connectivity among excitatory neurons is adapted by Spike-Timing-Dependent-Plasticity (STDP) learning, while the change of the weight is regulated by another learning rule: Spike-Rate-Dependent-Plasticity (SRDP). The effect of the pulse frequency applied on resistance was investigated. It indicates that higher frequency is beneficial to the change of the resistance of device. The novel waveform method also integrates the frequency and timing variables that affect the modulation of the weight.