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This article reviews the recent developments in a type of random access memory (RAM) called resistive RAM (RRAM) for the analog synapse, which is an important building block for neuromorphic computing systems. To achieve high learning accuracy in an artificial neural network based on the backpropagation learning rule, a linear and symmetric weight update behavior of the analog synapse is critical. The physical mechanisms in the RRA M (interfacing switching versus filamentary switching) are discussed, and the pros and cons of each mechanism to emulate the analog synaptic weights are compared. Then, various strategies from a materials and device engineering perspective are surveyed to achieve linearly and symmetric conductance changes under identical pulses. Finally, future research directions are outlined.
Woo et al. (Mon,) studied this question.