In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this chal...In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.展开更多
Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with...Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with high efficiency and low energy consumption in this big data explosion age.Triboelectric nanogenerator(TENG)has two functions of sensing and energy conversion,which promote the application as sensor and/or power supply in self-powered neuromorphic electronics for data storage and biological synapse/neuron behaviors mimicking.This article highlights the relevant works of TENGs for memory devices,artificial synapses and artificial neurons,performs a systematic comparison,and puts forward the future research possibilities and challenges,with the hope of attracting more researchers into this field and promoting the development of TENG based neuromorphic electronics.展开更多
基金We acknowledge grants from the National Natural Science Foundation of China(Grant No.61974093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012479)+1 种基金the Science and Technology Innovation Commission of Shenzhen(Grant Nos.RCYX20200714114524157 and JCYJ20220818100206013)the NTUT-SZU Joint Research Program(Grant No.NTUT-SZU-112-02).
文摘In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.
基金We acknowledge grants from the National Natural Science Foundation of China(Grant Nos.61974093,51902205 and 62074104)the Science and Technology Innovation Commission of Shenzhen(Grant Nos.RCYX20200714114524157 and JCYJ20220818100206013)NTUTSZU Joint Research Program.
文摘Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with high efficiency and low energy consumption in this big data explosion age.Triboelectric nanogenerator(TENG)has two functions of sensing and energy conversion,which promote the application as sensor and/or power supply in self-powered neuromorphic electronics for data storage and biological synapse/neuron behaviors mimicking.This article highlights the relevant works of TENGs for memory devices,artificial synapses and artificial neurons,performs a systematic comparison,and puts forward the future research possibilities and challenges,with the hope of attracting more researchers into this field and promoting the development of TENG based neuromorphic electronics.
