Implementation of a reservoir computing system using the short-term effects of Pt/HfO2/TaOx/TiN memristors with self-rectification

Given the limitations of von Neumann computing systems, we propose a high-performance reservoir computing system as an alternative. These systems operate as neural networks that store the states of the input signal and require a readout layer for data processing and learning. The advantage of this system is that training only takes place at the readout layer leading to good energy efficiency and low power con-sumption. In this paper, we implement a memristor-based hardware reservoir computing system using HfO2/TaOx bilayer based memristor that can imitate the short-term memory effects. We first characterize the volatility and record the self-rectification I-V curves of the HfO2/TaOx bilayer device. We also investi-gate the transient characteristics in terms of the interval required between pulse stimulation to return its initial state. In terms of transmitting information, 4 bits is a significant unit size because at least 4 bits are required to represent a single-digit number. Motivated by this, we successfully implemented a binary 4-bit code ranging from [0 0 0 0] to [1 1 1 1] in the fabricated memristor that can be used as the input signal to a reservoir layer. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a high-performance reservoir computing system is proposed as an alternative to von Neumann computing systems, utilizing a memristor-based hardware design. The system's advantage lies in training only at the readout layer, leading to good energy efficiency and low power consumption.