Analog memristive time dependent learning using discrete nanoscale RRAM devices

We propose a scheme that mimics the analog time dependent learning characteristics of biological synapses using a small set of discrete nanoscale RRAM devices whose switching voltages vary stochastically. Using numerical models and simulations, we demonstrate that a voltage limited analog memristor operating in the tunneling regime and a parallel combination of <; 10 RRAM devices having discrete resistance states (two resistance states - high and low), can both be employed as artificial synapses with similar statistical performance. We also show that by appropriately choosing the programming voltages and hence the switching probability of the RRAM devices, it is possible to tune the relative conductance of the synaptic element anywhere in the range of 2-100. This paper thus shows the possibility of using discrete RRAM devices to realize an analog functionality in artificial learning systems.