Resistive random-access memory (RRAM) with the ability to store and process information has been considered to be one of the most promising emerging devices to emulate synaptic behavior and accelerate the computation of intelligent algorithms. However, variation and limited resistance levels impede RRAM as a synapse for weight storage in neural network mapping. In this work, we investigate a TaOx-based RRAM with Al ion local doping. Compared with a device without doping, the device with locally doped Al ion exhibits excellent uniformity and analog characteristics. The operating voltage and resistance states show tighter distributions. Over 150 adjustable resistance states can be achieved through tuning compliance current (CC) and reset stop voltage. Moreover, incremental resistance switching is available under optimized identical pulses. The improved uniformity and analog characteristics can be attributed to the collective effects of reduced oxygen vacancy (Vo) formation energy and weak conductive filaments induced by the local Al ion dopants.
Low operating voltage resistive random access memory based on graphene oxide–polyvinyl alcohol nanocomposite thin films
