用于神经形态计算的CsPbBr_(3)钙钛矿忆阻器性能研究

Properties of memristor based on CsPbBr_(3)perovskite for neuromorphic computing

忆阻器可以模拟生物突触行为而实现低功耗、并行计算、集成度高的神经形态计算,因此已经引起广泛关注.本文报道了一种基于溶液法制备的铯铅溴(CsPbBr_(3))钙钛矿忆阻器,以包裹在聚乙烯吡咯烷酮(PVP)中的CsPbBr_(3)为功能层的忆阻器表现出优异的空气稳定性,同时成功模拟了生物突触行为,包括突触可塑性(synaptic plasticity)、长时程增强(long-term potentiation, LTP)和短时程增强(short-term potentiation, STP)、幅度依赖可塑性、双脉冲易化(paired-pulse facilitation, PPF)、短期记忆和长期记忆以及两者的转变.本研究展示的制备简单、成本低、稳定性好的钙钛矿人工突触,为未来神经形态计算的应用提供了新的可能性.

In the past few decades, the application of electronics and computers has dramatically changed the way that we learn, work,and live. Advances in semiconductor technology have enabled computers to perform increasingly well. The reduction in chip size has led to lower preparation costs, faster computer computing rates, and less power consumption. More than 50 years have passed since the development of Moore’s law, and the characteristic length of silicon-based transistors is getting closer to the physical limit. On the other hand, conventional computer systems based on the von Neumann Architecture have additional latency and power consumption due to the separation of the computation and storage centers,leading to the emergence of the von Neumann Bottleneck, which limits the further improvement of the computational speed. In this background, neuromorphic computing inspired by the human brain has attracted a lot of attention in recent years. This integrated computation and storage center architecture can provide highly parallel, fast, efficient, and lowenergy computing and storage to overcome the von Neumann bottleneck.Unlike the current mainstream neuromorphic computing that relies on software algorithms, memristors can mimic the synaptic function of biological nerves and thus build electronic devices that implement neuromorphic computing at the physical level. Nowadays, computer systems on silicon-based chip have great limitations and face new challenges for further development. The memristor-based neuromorphic computing system provides an alternative way to realize the integration of memory and computation, bio-inspired parallel computing and efficient reconfigurable memory computer system under the development needs of big data and internet of everything. To date, several materials have been applied to the development of memristors, such as oxides, two-dimensional materials, organic materials. However, these materials have certain limitations, such as complicated preparation processes, poor stability and reliability, and insufficient bionic properties. While perovskites have excellent electronic properties, which is widely used in light-emitting diodes, solar cells, and other fields. Perovskites are suitable for the preparation of memristive devices because of the advantages of high defect tolerance, high carrier mobility, and electron capture behavior. In recent years, perovskite-based artificial synapses have also been gradually developed, but they still have the problems of lack of stability and performance.Herein, we reported a highly stable memristor prepared with cesium lead bromide(CsPbBr_(3)) perovskite and polyvinylpyrrolidone(PVP) by a one-step method with the structure of ITO/CsPbBr_(3):PVP/Au. We used CsPbBr_(3)perovskite as the resistive switching layer material and added PVP to the precursor solution to prepare memristors that can be used for neuromorphic computing. After different annealing temperatures, CsPbBr_(3)crystals with different sizes were formed, and the device annealed at 250℃ had the most excellent performance. The ITO/CsPbBr_(3):PVP/Au memristor device still maintains excellent performance when exposed to air for 9 d, exhibiting excellent stability. Meanwhile, our device successfully simulates biological synaptic behavior, including synaptic plasticity, long-term potentiation(LTP) and short-term potentiation(STP), amplitude-dependent plasticity, paired-pulse facilitation(PPF), short-term and long-term memory. The excellent performance means that our devices can be widely used in neuromorphic computing systems, which is a step forward for the development of next-generation computer systems.