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中国物理学会期刊

双层结构突触仿生忆阻器的时空信息传递及稳定性

Spatiotemporal signal processing and device stability based on bi-layer biomimetic memristor

CSTR: 32037.14.aps.70.20210274
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  • 现有计算机体系架构下的神经网络难以对多任务复杂数据进行高效处理, 成为制约人工智能技术发展的瓶颈之一, 而人脑的并行运算方式具有高效率、低功耗和存算一体的特点, 被视为打破传统冯·诺依曼计算体系最具潜力的运算体系. 突触仿生器件是指从硬件层面上实现人脑神经拟态的器件, 它可以模拟脑神经对信息的处理方式, 即“记忆”和“信息处理”过程在同一硬件上实现, 这对于构建新的运算体系具有重要的意义. 近年, 制备仿生突触器件的忆阻材料已获得进展, 但多聚焦于神经突触功能的模拟, 对于时空信息感知和传递的关键研究较为缺乏. 本文通过制备一种双层结构忆阻器, 实现了突触仿生器件的基本功能包括双脉冲易化和抑制、脉冲时间依赖突触可塑性(spiking time dependent plasticity, STDP)和经验式学习等, 还对器件的信息感知、传递特性和稳定性进行了研究, 发现该器件脉冲测试结果满足神经网络处理时空信息的基本要求, 这一结果可以为忆阻器在类脑芯片中的应用提供参考.

     

    The neural network under the current computer architecture is difficult to process complex data efficiently, thus becoming one of the bottlenecks restricting the development of artificial intelligence technology. The human brain has the characteristics of high efficiency, low power consumption and integration of memory and computing, and is regarded as a most potential computing system to break the traditional von Neumann computing system. Synaptic biomimetic device is to realize the neural mimicry of human brain from the hardware level. It can simulate the information processing mode of brain nerve, that is, the process of “memory” and “calculation” can be realized on the same device, which is of great significance in building a new computing system. In recent years, the fabrication of memristor materials for bio-mimetic synaptic devices has made progress, but most of them focus on the simulation of synaptic function. The key research of pulse signal perception and information transmission is relatively lacking. In this paper, an bi-layer memristor with structure Al/nc-Al AlN/A2O3/Ag is fabricated by rf sputtering method to realize the basic functions of bionic synaptic devices. It is found that this bio-mimetic memristor exhibits bipolar switching property which is the basic condition to produce memristor based neural synapse. Both of PPF and PPD process can be observed and there will be no firing signal observed if the pulse interval is as large as 350 ms. The change of device conductance should be related to pulse voltage, frequency and pulse number applied. The larger pulse voltage, frequency and number will cause device conductance to increase sharply in both positive and negative pulse voltage region. The STDP measurement is executed with different sequence pulses from post and previous neuron separately. If the pulse of previous synapse comes in front of pulse from post synapse, the conductance will increase, which is so-called LTP process. If the pulse of previous neuron comes behind of pulse from post neuron, the conductance will be reduced as well. Triplet STDP measurement is executed with at least three pulses from previous and post neuron at the meanwhile. It is concluded that if the interval time of the first two pulses is fixed, the device conductance more depends on the value of the second and third pulse interval. Ebbinghaus forgetting curve can be used to explain the reason why the device conductance declines with time going by. The stability study of this memristor includes endurance and retention properties at both room and high temperature. It is found this biomimetic memristor can maintain its conductance for over 115.7 days at 85 ℃, which is long enough for current neural network design.

     

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