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This study introduces a neuromorphic computing architecture utilizing metallic nanoparticles connected by molecular junctions, which can be dynamically controlled via surrounding electrodes. The researchers identify three critical design rules that optimize computational performance, including the importance of operating near the system's cutoff frequency and the effects of varying the thickness of the SiO₂ layer on memory characteristics. By incorporating structural disorder, the team demonstrates a method to enhance computational expressivity beyond traditional limitations, paving the way for more efficient neuromorphic systems.
Transforming passive nanoparticle networks into tunable nonlinear systems reveals new pathways to enhance computational efficiency in neuromorphic computing.
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.
