Ye Fan, Kiani Fatemeh, Huang Yi, Xia Qiangfei
Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Adv Mater. 2023 Sep;35(37):e2204778. doi: 10.1002/adma.202204778. Epub 2022 Oct 7.
A diffusive memristor is a promising building block for brain-inspired computing hardware. However, the randomness in the device relaxation dynamics limits the wide-range adoption of diffusive memristors in large arrays. In this work, the device stack is engineered to achieve a much-improved uniformity in the relaxation time (standard deviation σ reduced from ≈12 to ≈0.32 ms). The memristor is further connected with a resistor or a capacitor and the relaxation time is tuned between 1.13 µs and 1.25 ms, ranging from three orders of magnitude. The hierarchy of time surfaces (HOTS) algorithm, to utilize the tunable and uniform relaxation behavior for spike generation, is implemented. An accuracy of 77.3% is achieved in recognizing moving objects in the neuromorphic MNIST (N-MNIST) dataset. The work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption.
扩散型忆阻器是构建受大脑启发的计算硬件的一个很有前景的组件。然而，器件弛豫动力学中的随机性限制了扩散型忆阻器在大型阵列中的广泛应用。在这项工作中，对器件堆叠进行了设计，以在弛豫时间上实现大幅改善的均匀性（标准偏差σ从约12毫秒降至约0.32毫秒）。忆阻器进一步与一个电阻器或一个电容器相连，弛豫时间在1.13微秒至1.25毫秒之间可调，跨度达三个数量级。实现了利用可调且均匀的弛豫行为来生成脉冲的时间表面层次结构（HOTS）算法。在神经形态MNIST（N-MNIST）数据集中识别运动物体时达到了77.3%的准确率。这项工作为构建超低功耗的新兴神经形态计算硬件系统铺平了道路。
