Reliable analog resistive switching behaviors achieved using memristive devices in AlO (x) /HfO (x) bilayer structure for neuromorphic systems

Human brain synaptic memory simulation based on resistive random access memory (RRAM) has enormous potential to replace the traditional von Neumann digital computer thanks to several advantages, including its simple structure, its high-density integration, and its capabilities regarding information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering the AlO (x) /HfO (x) bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitating several synaptic learning functions, including learning experience behaviors, short-term plasticity, long-term plasticity transition, and spike-timing-dependent plasticity (STDP). In addition, the memristor based on STDP learning rules is implemented in image pattern recognition. These results may show the potential of HfO (x) -based memristors for future information storage and neuromorphic computing applications.