HfO₂-based resistive switching memory devices for neuromorphic computing

Abstract

HfO2-based resistive switching memory (RRAM) combines several outstanding properties, such as high scalability, fast switching speed, low power, compatibility with complementary metal-oxide-semiconductor technology, with possible high-density or three-dimensional integration. Therefore, today, HfO2 RRAMs have attracted a strong interest for applications in neuromorphic engineering, in particular for the development of artificial synapses in neural networks. This review provides an overview of the structure, the properties and the applications of HfO2-based RRAM in neuromorphic computing. Both widely investigated applications of nonvolatile devices and pioneering works about volatile devices are reviewed. The RRAM device is first introduced, describing the switching mechanisms associated to filamentary path of HfO2 defects such as oxygen vacancies. The RRAM programming algorithms are described for high-precision multilevel operation, analog weight update in synaptic applications and for exploiting the resistance dynamics of volatile devices. Finally, the neuromorphic applications are presented, illustrating both artificial neural networks with supervised training and with multilevel, binary or stochastic weights. Spiking neural networks are then presented for applications ranging from unsupervised training to spatio-temporal recognition. From this overview, HfO2-based RRAM appears as a mature technology for a broad range of neuromorphic computing systems.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks

“…It should be noted that HfO 2 possesses a set of key characteristics essential for practical hardware systems, including a high dielectric constant for scalable CMOS circuits and excellent compatibility with CMOS fabrication processes. 37 As shown in Figure S2, the resistive switching phenomena were confirmed in both devices during each forming process. After such processes, the devices were operated as reversible memory devices.…”

Focus issue on hafnium oxide based neuromorphic devices

“…Finally, Brivio et al review structure, properties and applications of non-volatile as well as volatile HfO 2 -based RRAM devices in neuromorphic computing [5]. In this work, first the defect mediated filamentary switching mechanisms are discussed.…”

Demonstration of Synaptic Characteristics in VRRAM with TiN Nanocrystals for Neuromorphic System

“…[ 13 ] Particularly, representative filament‐type switching is performed using HfO x ‐based RRAM, which has been identified as a promising material for the RRAM switching layer and offers distinct advantages such as wide bandgap and high dielectric constants. [ 14,15 ] However, HfO x ‐based RRAM has inherent issues entailing individual defect diffusion and instability, which causes the fluctuation of switching values during consecutive switching cycles. [ 16–18 ] More precisely, cycle‐to‐cycle and device‐to‐device variations are determined by the filament shapes, location in the insulator, and the internal structure of the RRAM devices.…”