发表机构
University of Münster; University of Twente（明斯特大学; 特温特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于金属纳米粒子的神经形态计算架构，确立三项核心设计规则优化其性能，突破表达能力的屏蔽长度限制，实现高能效动态神经形态应用。
AI 中文摘要
物理计算利用复杂动力系统实现高能效的数据处理。本研究提出一种基于金属纳米粒子的神经形态架构，该架构在SiO₂/Si衬底上通过分子结实现纳米粒子间的互连。研究表明，周围的静态控制电极可将该纳米粒子网络从被动存储库转变为可调谐的非线性动力系统。通过分析这些电极如何将简单的一维电压输入路由为多维信号响应，研究确立了三项核心设计规则以最大化计算性能：第一，在系统截止频率附近运行可实现非线性电荷隧穿与线性电容存储之间的最优平衡；第二，调控底层SiO₂厚度可设定静电屏蔽长度并决定存储类型，厚氧化层会缩短屏蔽长度，导致大于该长度的网络转变为类持久非易失性状态，而小于屏蔽长度的网络仅表现出 fading memory；第三，通过异质分子结引入结构无序可克服表达能力的固有极限，虽然网络的计算表达能力随其物理尺寸增大而提升，但最终会受限于屏蔽长度，利用局域无序打破内部空间对称性可绕过这种饱和，使控制电压能独立操控特定信号幅度与相位，为动态神经形态应用普遍最大化性能。
英文摘要
Physical computing leverages complex dynamical systems for energy-efficient data processing. In this work, we present a neuromorphic architecture based on metallic nanoparticles interconnected by molecular junctions on a $\text{SiO}_2$/Si substrate. We demonstrate that surrounding static control electrodes transform this nanoparticle network from a passive reservoir into a tunable nonlinear dynamical system. By analyzing how these electrodes route simple one-dimensional voltage inputs into multidimensional signal responses, we establish three core design rules to maximize computational performance. First, operating near the system's cutoff frequency achieves an optimal balance between nonlinear charge tunneling and linear capacitive memory. Second, tuning the underlying $\text{SiO}_2$ thickness sets the electrostatic screening length and dictates the memory type. Thick oxide layers reduce the screening length, causing networks larger than this length to transition into a persistent, non-volatile-like regime. Conversely, networks smaller than the screening length exhibit only fading memory. Third, introducing structural disorder via heterogeneous molecular junctions overcomes inherent limits on expressivity. While a network's computational expressivity scales with its physical size, it is ultimately capped by the screening length. Breaking internal spatial symmetries with localized disorder bypasses this saturation, allowing control voltages to independently manipulate specific signal amplitudes and phases, universally maximizing performance for dynamic neuromorphic applications.
