School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China.
Southern Illinois, University Carbondale, USA.
Nanoscale. 2023 Apr 13;15(15):7105-7114. doi: 10.1039/d2nr06044b.
Recently, with the improvement of the requirements for fast and efficient data processing in the era of artificial intelligence, new forms of computing have come into being. Developing memristor devices that can simulate the brain's computing neutral network is particularly important for applications in the field of artificial intelligence. However, there are still some challenges in their biological function simulation and related circuit design. In this work, a memristor based on perovskite rare earth nickelates (RNiO) is presented with excellent electrical performance, including three orders of magnitude higher current switching ratio and good repeatability, and can achieve bidirectional conductance regulation like weight modulation in bio-synapse. Furthermore, the synaptic like characteristics of the device have been mimicked successfully, such as excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), classical double pulse spike time-dependent plasticity (classical pair-STDP), triplet spike time-dependent plasticity (triplet-STDP), short-term plasticity (STP), long-term plasticity (LTP), the refractory period phenomenon and learning and forgetting rules. In particular, two synaptic devices and a leaky integrate-and-fire (LIF) neuron device are used to achieve a logic gate circuit to realize "AND", "OR", and "NOT" functions. The device paves the way for the application of high-density circuits in artificial intelligence.
最近,随着人工智能时代对快速高效数据处理要求的提高,新的计算形式应运而生。开发能够模拟大脑计算神经网络的忆阻器器件对于人工智能领域的应用尤为重要。然而,在其生物功能模拟和相关电路设计方面仍然存在一些挑战。在这项工作中,提出了一种基于钙钛矿稀土镍酸盐(RNiO)的忆阻器,具有优异的电学性能,包括三个数量级更高的电流开关比和良好的重复性,并可以实现类似于生物突触中的权重调节的双向电导调节。此外,该器件成功模拟了类似突触的特性,如兴奋性突触后电流(EPSC)、成对脉冲易化(PPF)、经典双脉冲尖峰时间依赖性可塑性(经典 pair-STDP)、三脉冲尖峰时间依赖性可塑性(triplet-STDP)、短期可塑性(STP)、长期可塑性(LTP)、不应期现象和学习遗忘规则。特别是,两个突触器件和一个漏电积分和放电(LIF)神经元器件被用于实现逻辑门电路,以实现“AND”、“OR”和“NOT”功能。该器件为高密度电路在人工智能中的应用铺平了道路。