Duarte F R, Mukim S, Ferreira M S, Rocha C G
School of Physics, Trinity College Dublin, Dublin 2, Ireland.
Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland.
Phys Chem Chem Phys. 2024 Nov 27;26(46):29015-29026. doi: 10.1039/d4cp03242j.
Random nanowire networks (NWNs) are interconnects that enable the integration of nanoscopic building blocks (the nanowires) in a disorganized fashion, enabling the study of complex emergent phenomena in nanomaterials and built-in fault-tolerant processing functionalities; the latter can lead to advances in large-scale electronic devices that can be fabricated with no particular array/grid high-precision pattern. However, when various nanowires are assembled to form an intricate network, their individual features are somehow lost in the complex NWN frame, in line with the complexity hallmark "the whole differs from the sum of the parts". Individual nanowire materials and geometrical features can only be inferred indirectly by attempting to extract information about their initial conditions from a response function measurement. In this work, we present a mathematical framework that enables inference of the intrinsic properties of highly complex/intricate systems such as random NWNs in which information about their individual parts cannot be easily accessed due to their network formation and dynamical conductance behaviour falling in the category of memristive systems. Our method, named misfit minimization, is rooted in nonlinear regression supervised learning approaches in which we find the optimum parameters that minimize a cost function defined as the square least error between conductance evolution curves taken for a target NWN system and multiple configurational NWN samples composing the training set. The optimized parameters are features referent to the target NWN system's initial conditions obtained in an inverse fashion: from the response output function, we extract information about the target system's initial conditions. Accessing the nanowire individual features in a NWN frame, as our methodology allows, enables us to predict the conduction mechanisms of the NWN subjected to a current input source; these can be a "winner-takes-all" energy-efficient scheme using a single conduction pathway composed of multiple nanowires connected in series or multiple parallel conduction pathways. Predicting the conduction mechanism of complex and dynamical systems such as memristive NWNs is critical for their use in next-generation memory and brain-inspired technologies since their memory capability relies on the creation of such pathways activated and consolidated by the input current signal.
随机纳米线网络（NWNs）是一种互连结构，它能以无序方式实现纳米级构建块（纳米线）的集成，从而能够研究纳米材料中的复杂涌现现象以及内置的容错处理功能；后者可能会推动大规模电子设备取得进展，这类设备可以在没有特定阵列/网格高精度图案的情况下制造出来。然而，当各种纳米线组装形成一个复杂的网络时，它们各自的特性在复杂的NWN框架中会在某种程度上丧失，这符合“整体不同于部分之和”这一复杂性特征。单个纳米线的材料和几何特征只能通过尝试从响应函数测量中提取有关其初始条件的信息来间接推断。在这项工作中，我们提出了一个数学框架，该框架能够推断高度复杂/错综复杂系统的内在属性，例如随机NWNs，由于其网络形成以及动态电导行为属于忆阻系统范畴，因此难以轻易获取有关其各个部分的信息。我们的方法名为失配最小化，它基于非线性回归监督学习方法，在该方法中，我们找到最优参数，以最小化一个成本函数，该成本函数定义为目标NWN系统的电导演化曲线与构成训练集的多个构型NWN样本的电导演化曲线之间的最小二乘误差平方。优化后的参数是通过反向方式获得的与目标NWN系统初始条件相关的特征：从响应输出函数中，我们提取有关目标系统初始条件的信息。正如我们的方法所允许的那样，在NWN框架中获取纳米线个体特征，使我们能够预测NWN在电流输入源作用下的传导机制；这些机制可以是一种“赢家通吃”的节能方案，使用由多个串联连接的纳米线组成的单个传导路径，或者是多个并行传导路径。预测诸如忆阻NWNs这样的复杂动态系统的传导机制对于它们在下一代存储器和受大脑启发的技术中的应用至关重要，因为它们的存储能力依赖于由输入电流信号激活和巩固的此类路径的创建。
