Flexible memristive spiking neuron for neuromorphic sensing and computing

Abstract

Inspired by the working modes of the human brain, the spiking neuron, as the basic computing unit, plays an important role in artificial perception and neuromorphic computing systems. However, the neuron circuit based on complementary metal-oxide-semiconductor technology has a complex structure, high power consumption, and limited flexibility. These features are not conducive to the large-scale integration and the application of flexible sensing systems compatible with the human body. The flexible memristor prepared in this paper shows stable threshold switching characteristics and excellent mechanical bending characteristics with bending radius up to 1.5 mm and bending times up to 10<sup>4</sup> times. The compact neuron circuit constructed based on this device shows the key features of the neuron, such as threshold-driven spiking, all-or-nothing, refractory period, and strength-modulated frequency response. The frequency-input voltage relationship of the neuron shows the similarity of the rectified linear unit, which can be used to simulate the function of rectified linear unit in spiking neural networks. In addition, based on the electron transport mechanism, a core-shell model is introduced to analyze the working mechanism of the flexible memristor and explain the output characteristics of the neuron. In this model, the shell region consisting of NbO<sub>2</sub> is subject to ohmic conduction, while the core region consisting of Nb<sub>2</sub>O<sub>5-<em>x</em></sub> is dominated by Poole-Frenkel conduction. These two mechanisms, combined with Newton's law of cooling, dominated the threshold switching behavior of flexible memristor device. Furthermore, the threshold switching characteristic of the memristor is simulated, verifying the rationality of the working mechanism of the flexible memristor. Concerning that the threshold voltage decreases with the increasing temperature, a correction term is added to the temperature of the shell region. Subsequently, the output characteristics of the neuron regulated by the input voltage are simulated. The simulation results show that the frequency increases with the input voltage and the threshold voltage decrease with the input voltage, which consistent with the experimental results. The introduction of the correction term confirms the influence of the thermal accumulation effect of the flexible substrate on neuron output characteristics. Finally, we built a spiking neural network based on memristive spiking neurons to implement handwriting recognition, achieving a 95.6% recognition rate, which is comparable to the ideal result of the artificial neural network (96%). This result shows the potential of the memristive spiking neurons for neuromorphic computing. In this paper, the study of flexible neurons can guide the design of neuromorphic sensing and computing systems.

High endurance organic resistive switching memory based on 1, 2-dicyanobenzene and polymer composites

“…第一作者.E-mail: li_wei1998@163.com 1 引 言 随着大数据时代的到来和人工智能、云计算、生物电子等技术的发展，研发 高密度、快速的数据存储设备迫在眉睫 [1] 。然而，基于互补金属氧化物半导体 (CMOS) 工艺微电子制造技术的传统存储技术逐渐接近摩尔定律的极限，缩小尺 寸的技术复杂性和成本越来越高 [2,3] 。电阻式随机存取存储器 (Resistive Random Access Memory，RRAM)，特别是三明治结构的 RRAM，由于具有结构简单、易 于 3D 集成、材料选择范围广、高耐久性和低功耗等优点，成为下一代信息存储 技术的有力候选者，受到学术界和产业界的广泛关注 [4][5][6][7][8][9][10] 。三明治结构的 RRAM 由上、下两个电极和夹在两电极之间的活性层构成，两个电极和活性层在电刺激 下发生相互作用从而在高阻态 (High Resistance State，HRS) 和低阻态 (Low Resistance State，LRS) 之间切换，实现数据"0"和"1"的存储。 近年来， 有机材料作为活性层的有机阻变存储器 (Organic Resistive Switching Memory，ORSM) 由于可溶液法大面积制备、可实现柔性、开关速度快、生物兼 容，并且可通过简单的分子设计调节性能等优点被广泛研究 [11][12][13][14][15][16] 。特别是基于分 子水平的给体-受体 (Donor-Acceptor， D-A) 本体异质结结构可以在电刺激下发生 电荷转移相互作用，已被广泛应用于 ORSM 的活性层 [17][18][19][20][21]…”