Department of Physics, National University of Singapore, Singapore, Singapore.
NUSNNI-NanoCore, National University of Singapore, Singapore, Singapore.
Nature. 2021 Sep;597(7874):51-56. doi: 10.1038/s41586-021-03748-0. Epub 2021 Sep 1.
Profuse dendritic-synaptic interconnections among neurons in the neocortex embed intricate logic structures enabling sophisticated decision-making that vastly outperforms any artificial electronic analogues. The physical complexity is far beyond existing circuit fabrication technologies: moreover, the network in a brain is dynamically reconfigurable, which provides flexibility and adaptability to changing environments. In contrast, state-of-the-art semiconductor logic circuits are based on threshold switches that are hard-wired to perform predefined logic functions. To advance the performance of logic circuits, we are re-imagining fundamental electronic circuit elements by expressing complex logic in nanometre-scale material properties. Here we use voltage-driven conditional logic interconnectivity among five distinct molecular redox states of a metal-organic complex to embed a 'thicket' of decision trees (composed of multiple if-then-else conditional statements) having 71 nodes within a single memristor. The resultant current-voltage characteristic of this molecular memristor (a 'memory resistor', a globally passive resistive-switch circuit element that axiomatically complements the set of capacitor, inductor and resistor) exhibits eight recurrent and history-dependent non-volatile switching transitions between two conductance levels in a single sweep cycle. The identity of each molecular redox state was determined with in situ Raman spectroscopy and confirmed by quantum chemical calculations, revealing the electron transport mechanism. Using simple circuits of only these elements, we experimentally demonstrate dynamically reconfigurable, commutative and non-commutative stateful logic in multivariable decision trees that execute in a single time step and can, for example, be applied as local intelligence in edge computing.
大脑中的神经元之间存在丰富的树突状突触连接,这些连接构成了复杂的逻辑结构,使大脑能够进行复杂的决策,其性能远远超过任何人工电子模拟。这种物理复杂性远远超出了现有的电路制造技术:此外,大脑中的网络是动态可重构的,这为其提供了灵活性和对不断变化的环境的适应能力。相比之下,最先进的半导体逻辑电路基于硬连线执行预定义逻辑功能的阈值开关。为了提高逻辑电路的性能,我们正在通过在纳米级材料特性中表达复杂逻辑来重新构想基本的电子电路元件。在这里,我们使用电压驱动的条件逻辑互连接,将一个金属有机配合物的五个不同的氧化还原状态之间的条件逻辑互连接,来嵌入一个“密林”的决策树(由多个如果-那么-否则的条件语句组成),其中包含单个忆阻器中的 71 个节点。这个分子忆阻器(一种“记忆电阻器”,是一种全局被动的电阻开关电路元件,与电容器、电感器和电阻器一起构成了电路元件的基本集合)的电流-电压特性表现出在单个扫描周期内,两个电导水平之间的八个递归和历史相关的非易失性开关转换。每个分子氧化还原状态的身份都通过原位拉曼光谱确定,并通过量子化学计算得到证实,揭示了电子输运机制。仅使用这些元件的简单电路,我们在多变量决策树中实验证明了动态可重构、可交换和不可交换的有状态逻辑,这些逻辑可以在单个时间步长内执行,例如,可以应用于边缘计算中的本地智能。