Tyndall National Institute, and the School of Chemistry, University College Cork, Cork, Ireland.
Department of Physics and Astronomy, University of Manchester, Manchester, M13 9PL, UK.
Sci Rep. 2020 Jul 22;10(1):12178. doi: 10.1038/s41598-020-68793-7.
Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ = 27.4 ms, τ = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing.
受大脑启发的神经形态计算旨在解决现代冯·诺依曼架构中不断增长的计算复杂性和功耗。由于缺乏可以模拟神经元/突触行为的硬件元件,这是尖峰神经网络(SNN)的基本构建块,因此该领域的进展受到了阻碍。在这项工作中,我们利用原子层薄通道晶体管中电子俘获事件的短/长期记忆效应,模拟神经递质的交换并模拟突触响应。使用脉冲门测量来检查再掺杂(n 型)和 Nb 掺杂(p 型)二硫化钼(MoS)场效应晶体管,这确定了电子俘获/解俘获的时间尺度。这些器件分别在 ms/min 量级上表现出有前途的短期/长期可塑性趋势。有趣的是,量化短期可塑性的脉冲对易化(PPF)显示出的时间常数(τ = 27.4 ms,τ = 725 ms)与生物突触非常匹配。增强和抑制测量描述了突触器件遍历几个模拟状态的能力,其中使用相同高度和宽度的连续脉冲可以访问至少 50 个电导值。最后,我们展示了可以模拟著名学习规则的器件,即尖峰时间依赖性可塑性(STDP),它将前突触和后突触神经元发射的时间序列编码为相应的突触权重。这些突触器件与离子器件相比具有显著优势,有望为神经形态计算硬件的发展开辟新的方向。