2D-3D perovskite memristor with low energy consumption and high stability for neural morphology calculation

• Main Authors: Sun, Kaixuan, Wang, Qingrui, Zhou, Long, Wang, Jingjuan, Chang, Jingjing, Guo, Rui, Tay, Beng Kang, Yan, Xiaobing
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Perovskite; Ion Migration
• Online Access: https://hdl.handle.net/10356/168994
• Institution: Nanyang Technological University

Recently, rapid progress has been made in the application of organic-inorganic halide perovskites in electronic devices, such as memristors and artificial synaptic devices. Organic-inorganic halide perovskite is considered as a promising candidate for the next generation of computing devices due to its ion migration property and advantages in manufacturing. In this work, a two-dimensional (2D)-3D organic-inorganic hybrid perovskite memristor was studied, using the stacking structure of indium tin oxide (ITO)/FA1−yMAyPbI3−xClx/(PEA)2PbI4/Au. The results show that this new type of memristor has novel resistance switching characteristics, such as scanning-rate-dependent current switching property, good current-voltage (I–V) curve repeatability, and ultralow energy consumption. A defect-modulated electron tunneling mechanism is demonstrated using the p-i-n junction model, and it is proven that the conductance state of the memristive device is determined by the defect concentration in the perovskite film near the electrode sides. In addition to the good memristive properties, this 2D-3D perovskite memristor can also function well as an artificial synapse, and its internal defect movement can faithfully simulate the inflow and extrusion of Ca2+ in biological synapses. Moreover, this perovskite-based artificial synapse has ultra-low power consumption due to the switchable p-i-n structure in organic-inorganic halide perovskites. Our finding highlights the immense application potential of the 2D-3D perovskite memristor in the future neuromorphic computing system.