Publication

Reservoir computing (RC) represents a powerful neuromorphic framework for spatiotemporal signal processing. Owing to their intrinsic nonlinear dynamics, memristors are well-suited for RC systems, where volatile devices typically function as the reservoir and nonvolatile devices serve as the readout layer. However, prior implementations have relied on dissimilar, non-silicon materials to realize these two functionalities, leading to significant integration challenges.

Here, we report memristor arrays based on few-layer silicon nanosheets (Si NSs) that enable both volatile and nonvolatile switching within a single material platform, governed by the lateral dimensions of the Si NSs. Devices incorporating small-sized Si NSs exhibit volatile switching with a low set voltage of 0.23 V and stable reservoir dynamics, whereas those based on large-sized Si NSs demonstrate nonvolatile switching with low switching voltages (0.24/–0.18 V) and near-linear conductance modulation. Mechanistic investigations indicate that oxygen vacancies located at nanosheet edges regulate conductive filament dynamics associated with silver ion diffusion, thereby enabling controllable switching volatility. An Si NS-based RC processor is further demonstrated, achieving high accuracy in temporal information processing.

Researcher/Author: 

Xing Chuanwang

Published in: Device, Volume 3, Issue 9, 2025

Date Added : 19 September 2025