ReRAM-based synaptic device for neuromorphic computing

To compete with nonvolatile FLASH memory technology, we need to develop stackable, cross-point ReRAM device. Although various materials have been reported, it is difficult to meet device criteria such as high speed operation, low power switching, switching uniformity, endurance, long-term retention and selection device for cross-point array. We have investigated various ReRAM devices to meet the nonvolatile memory requirements. We developed wafer scale PCMO-ReRAM devices which exhibit excellent switching uniformity and analog memory behavior. Using PCMO-ReRAM, we can implement analog artificial synapse characteristics for neuromorphic systems. We demonstrated the optimum ReRAM synapse characteristics which are suitable for the neuromorphic application. We compared the time series prediction and face recognition function with varying potentiation and depression characteristics of ReRAM synapse.