# Fabrication of Stochastic Ni@PVP Nanowire Networks for Memristive Platforms

## TL;DR

This paper introduces a new type of memristive nanowire network using nickel and PVP, suitable for energy-efficient neuromorphic computing.

## Contribution

A low-cost, scalable Ni@PVP nanowire network is proposed with optimal resistive switching properties for memristive applications.

## Key findings

- Ni@PVP nanowire networks show low-voltage resistive switching (1–2 V) with a resistance ratio of ~200.
- Optimal performance is achieved with a PVP concentration ratio of 1:25, showing stable retention and reproducible endurance.
- The networks exhibit fading memory and nonlinear dynamics suitable for physical reservoir computing.

## Abstract

Single memristive nanowire networks have emerged as a promising pathway for energy-efficient neuromorphic computing, owing to their intrinsic nonlinearity, high dimensionality, fading memory and volatile switching dynamics relevant to physical reservoir computing. While prior works focused on oxide- or silver-based network systems, these approaches face trade-offs between operating voltage, cost, stability, and scalability. This work presents a proof-of-concept demonstration of stochastic polyvinylpyrrolidone (PVP)-coated nickel nanowire networks as low-cost and scalable memristive platforms, exhibiting low-voltage resistive switching (1–2 V). The electrical characterization reveals predominantly volatile resistive switching combined with nonvolatile behavior, consistent with a filamentary conduction mechanism at nanowire junctions. The switching dynamics are governed by the polymer coating thickness, with an intermediate PVP concentration (Ni@PVP = 1:25) showing optimal performance, with a resistance ratio of ~200, stable retention over 1 h, and a reproducible endurance of over 45 cycles. These results establish Ni@PVP nanowire networks as promising memristive platforms for neuromorphic hardware applications and physical reservoir computing, with relevant properties such as fading memory and nonlinear dynamics.