Rectifying Resistive Memory Devices as Dynamic Complementary Artificial Synapses - Berco, Dan (Frontiers in Neuroscience, 2018)
Brain inspired computing is a pioneering computational method gaining momentum in recent years. Within This scheme, artificial neural networks are implemented using two main approaches: software algorithms and designated hardware architectures. However, while software implementations show remarkable results (at high energy costs), hardware based ones, specifically resistive random access memory (RRAM) arrays that consume little power and hold a potential for enormous densities, are somewhat lagging. One of the reasons may be related to the limited excitatory operation mode of RRAMs in these arrays as adjustable passive elements. An interesting type of RRAM was demonstrated recently for having alternating (dynamic polarity switching) current rectification properties which may be used for complementary operation (much like CMOS transistors). Such an artificial synaptic device may be switched dynamically between excitatory and inhibitory modes to allow doubling the density and significantly reducing the peripheral circuit complexity.