Multilevel state ferroelectric La:HfO2-based memristors and their implementations in associative learning circuit and face recognition

Abstract

As Moore’s Law approaches physical limits, traditional von Neumann buildings are facing challenges. The application of memristors in multilayer storage, neuromorphic systems and analog circuits has the potential to overcome the von Neumann architecture bottleneck. Here, we fabricated high-performance memristors based on the Pd/La: HfO2/La2/3Sr1/3MnO3 device on silicon substrate, which facilitate the compatibility with complementary metal oxide semiconductor processes. The memristor devices exhibited good cycling stability and multilevel resistive state storage capabilities. And the synaptic properties of the device, such as long-term potentiation/depression, short-term memory to long-term memory, spike time-dependent plasticity, and double-pulse facilitation, were also shown. Based on the brain-like synaptic behavior of the device, a high recognition rate of 91.11% was achieved in recognizing face images in neural-inspired computing. Through theoretical calculation and hardware associative learning circuit test, the hafnium-based ferroelectric memristor was successfully applied to biological associative learning behavior for the first time.