Perovskite-Oxide-Based Ferroelectric Synapses Integrated on Silicon

Perovskite-oxide-based ferroelectric tunnel junctions (FTJs) hold great potential for applications in non-volatile memory and neuromorphic computing due to their unique properties. However, the challenges in synthesizing high crystalline quality perovskite oxides directly on silicon wafer limit the applications of these FTJs in conventional Si-based integrated circuits, let alone the neural networks. Herein, perovskite oxide FTJs with an ON/OFF ratio up to 1.2x10(6), writing/erasing speed down to 1 nanosecond, and cycling endurance (>10(6)) are achieved by integrating ultrathin freestanding ferroelectric perovskite oxide membranes directly on silicon wafers using a wet-transfer method. Moreover, synapses based on these FTJs exhibit long-term plasticity. For handwritten digits recognition task, the convolutional neural network (CNN) simulation is implemented achieving a recognition accuracy up to 98.9% based on the experimental performance, close to the result of 99.2% by software-floating-point-based CNN. This work sheds light on the integration of ferroelectric perovskite oxides directly on silicon for high-performance FTJ-based non-volatile memory and synaptic devices.