High-Performance Edge-Line Contact Memristors with In-Plane Solid-Liquid-Solid Grown Silicon Nanowires for Probabilistic Neuromorphic Computing.

Abstract

Memristors have garnered increasing attention in neuromorphic computing hardware due to their resistive switching characteristics. However, achieving uniformity across devices and further miniaturization for large-scale arrays remain critical challenges. In this study, we demonstrate the scalable production of highly uniform, quasi-one-dimensional diffusive memristors based on heavily doped n-type silicon nanowires (SiNWs) with diameters as small as ∼50 nm, fabricated via in-plane solid-liquid-solid (IPSLS) growth technology. The edge-line contact structural design improves the control of nucleation sites and the size of conductive filaments (CFs) in Ag/SiO₂/n-SiNW memristors. These devices exhibit excellent self-compliance threshold switching characteristics, including a low operating voltage (∼0.8 V) with a standard deviation of 0.073 V, low leakage current (1 pA), high switching ratio (>10⁷), ultrafast switching speed (∼8 ns), and extremely low switching energy (47.2 fJ per operation). Additionally, we developed neurons with tunable sigmoidal probabilistic activation functions, demonstrating high uniformity across different devices. These neurons achieved an accuracy of 96.2% in binary tumor classification tasks, underscoring the potential of IPSLS-fabricated SiNWs for advanced neuromorphic computing hardware. This work highlights the effectiveness of SiNW-based memristors in addressing challenges in neuromorphic hardware design and their potential for large-scale integration.