Electronic synaptic plasticity and analog switching characteristics in Pt/TiOx/AlOx/AlTaON/TaN multilayer RRAM for artificial synapses

Abstract

In this work, electronic synaptic plasticity and analog bipolar switching behavior by using a Pt/TiOx/AlOx/AlTaON/TaN multilayer resistive random-access memory (RRAM) device were examined. By using highresolution transmission electron microscopy (HRTEM), the oxidation-reduction reaction occurs between the Ti buffer layer and the high-k Al2O3 layer at the top interface, and the high-k Al(2)O(3 )layer and the TaN bottom electrode at the bottom interface, resulting in the formation of the Pt/TiOx/AlOx/AlTaON/TaN multilayer RRAM structure. According to X-ray photoelectron spectroscopy (XPS) studies, different proportions of oxygen vacancies in TiOx/AlOx/AlTaON multilayer film play a key role in increasing RS properties uniformity. This multilayer structure-based RRAM device exhibits a stable RS behavior with a very large on/off resistance window (similar to 10(4)), excellent durability (500 DC endurance cycles), and 10(4) s retention period with virtuous uniformity during the cycling operation. Furthermore, the device mean value (mu) set-and reset-voltage is -2.82 V, and +1.5 V, resulting in a low-operating voltage that is beneficial for neummorphic systems. Besides, by adjusting the reset voltage, the device achieved a progressive transition from the low resistance state (LRS) to the high resistance state (HRS). To develop the artificial neural networks, basic biological properties such as potentiation, depression, and paired-pulse facilitation were efficiently mimicked. Ohmic conduction in the LRS and Schottky emission in the HRS are the two types of current transport conduction mechanisms. The analog bipolar switching behavior is governed by the redox reaction, which promotes the formation and rupture of oxygen vacancy-based conical shape conductive filaments. These findings show that multilayer RRAM devices have the potential to be used for future information storage and neuromorphic computing of artificial synapses.