Gao Shengbo, Ma Mingyuan, Liang Bin, Du Yuan, Du Li, Chen Kunji
School of Physics, Nanjing University, Nanjing 210093, China.
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
Nanomaterials (Basel). 2024 Aug 29;14(17):1412. doi: 10.3390/nano14171412.
As the key hardware of a brain-like chip based on a spiking neuron network (SNN), memristor has attracted more attention due to its similarity with biological neurons and synapses to deal with the audio signal. However, designing stable artificial neurons and synapse devices with a controllable switching pathway to form a hardware network is a challenge. For the first time, we report that artificial neurons and synapses based on multilayered HfO/TiO memristor crossbar arrays can be used for the SNN training of audio signals, which display the tunable threshold switching and memory switching characteristics. It is found that tunable volatile and nonvolatile switching from the multilayered HfO/TiO memristor is induced by the size-controlled atomic oxygen vacancy pathway, which depends on the atomic sublayer in the multilayered structure. The successful emulation of the biological neuron's integrate-and-fire function can be achieved through the utilization of the tunable threshold switching characteristic. Based on the stable performance of the multilayered HfO/TiO neuron and synapse, we constructed a hardware SNN architecture for processing audio signals, which provides a base for the recognition of audio signals through the function of integration and firing. Our design of an atomic conductive pathway by using a multilayered TiO/HfO memristor supplies a new method for the construction of an artificial neuron and synapse in the same matrix, which can reduce the cost of integration in an AI chip. The implementation of synaptic functionalities by the hardware of SNNs paves the way for novel neuromorphic computing paradigms in the AI era.
作为基于脉冲神经网络（SNN）的类脑芯片的关键硬件，忆阻器因其与生物神经元和突触的相似性而在处理音频信号方面备受关注。然而，设计具有可控开关路径以形成硬件网络的稳定人工神经元和突触器件是一项挑战。我们首次报道基于多层HfO/TiO忆阻器交叉阵列的人工神经元和突触可用于音频信号的SNN训练，其具有可调阈值开关和记忆开关特性。研究发现，多层HfO/TiO忆阻器的可调挥发性和非挥发性开关是由尺寸可控的原子氧空位路径诱导的，这取决于多层结构中的原子子层。通过利用可调阈值开关特性可以成功模拟生物神经元的积分发放功能。基于多层HfO/TiO神经元和突触的稳定性能，我们构建了一种用于处理音频信号的硬件SNN架构，这为通过积分和发放功能识别音频信号提供了基础。我们利用多层TiO/HfO忆阻器设计原子导电通路，为在同一矩阵中构建人工神经元和突触提供了一种新方法，这可以降低人工智能芯片中的集成成本。通过SNN硬件实现突触功能为人工智能时代的新型神经形态计算范式铺平了道路。
