Reservoir Computing System with Diverse Input Patterns in HfAlO-Based Ferroelectric Memristor

Abstract

Ferroelectric memristors, particularly those based on hafnia, are gaining attention as potential candidates for neuromorphic computing. These devices offer advantages over perovskite-based ferroelectric memristors owing to their simpler structures, compatibility with complementary metal-oxide semiconductor technology, and low-power consumption characteristics. Additionally, improvements in ferroelectric memristor's performance, such as enhancing tunneling electro resistance (TER) and polarization retention, can be achieved using methods like aluminum doping and insulating film deposition. In this study, we implement a physical reservoir computing (RC) system utilizing the metal-ferroelectric-insulator-semiconductor-structured ferroelectric memristor based on Al-doped HfO2 as an artificial synapse. Specifically, we ensure the universality and diversity of the system by experimentally demonstrating a robust reservoir layer capable of handling various types of input pulses. To utilize the ferroelectric memristor in the reservoir layer of the RC system, we employ partial polarization switching of ferroelectric materials. We measure the retention loss characteristics of the device for pulse amplitude, interval, and width, and quantify the time constant values by fitting them to a stretched exponential function. Additionally, we validate the suitability of the fabricated device as an artificial synapse by mimicking various short-term plasticity functions of biological synapses. Furthermore, we experimentally demonstrate various applications related to learning and memory of the brain, such as image training and Pavlov's experiment, utilizing the short-term memory characteristics of the fabricated device. Lastly, we evaluate the robustness of the RC system under various input conditions by employing the fabricated device as a reservoir layer.