Forming-less compliance-free multistate memristors as synaptic connections for brain-inspired computing - Ng, Sien (2020)
Hardware realization of artificial neural networks (ANNs) requires analogue weights to be encoded into the device conductances via blind update and access operations, leveraging Kirchhoff’s circuit laws. However, most memristive solutions lag behind in this aspect due to numerous device nonidealities, like limited number of addressable states, need for a stringent compliance current control, and an electroforming process. By modulating the oxygen vacancy profile of tin oxide switching elements, here we design and evaluate multistate memristors as synaptic connections for brain-inspired computing. Harnessing the advantages of a forming-less compliance-free operation, our devices display gradual switching transitions across multiple conductance states, sufficing the switching requirements of synaptic connections in an ANN. The soft boundary conditions are analyzed systematically, and spike-based plasticity rules, state-dependent spike-timing-dependent-plasticity (STDP) modulations, ternary digital logic, and analogue updatability schemes are proposed and demonstrated comprehensively to establish the analogue programming window of our memristors.