Two- and three-terminal HfO2-based multilevel resistive memories for neuromorphic analog synaptic elements

Abstract

Synaptic elements based on memory devices play an important role in boosting neuromorphic system performance. Here, we show two types of fab-friendly HfO2 material-based resistive memories categorized by configuration and an operating principle for a suitable analog synaptic device aimed at inference and training of neural networks. Since the inference task is mainly related to the number of states from a recognition accuracy perspective, we first demonstrate multilevel cell (MLC) properties of compact two-terminal resistive random-access memory (RRAM). The resistance state can be finely subdivided into an MLC by precisely controlling the evolution of conductive filament constructed by the local movement of oxygen vacancies. Specifically, we investigate how the thickness of the HfO2-switching layer is related to an MLC, which is understood by performing physics-based modeling in MATLAB from a microscopic view. Meanwhile, synaptic devices driven by an interfacial switching mechanism instead of local filamentary dynamics are preferred for training accelerated neuromorphic systems, where the analogous transition of each state ensures high accuracy. Thus, we introduce three-terminal electrochemical random-access memory that facilitates mobile ions across the entire HfO2 switching area uniformly, resulting in highly controllable and gradually tuned current proportional to the amount of migrated ions.