Experimental study of LiNbO3 memristors for use in neuromorphic computing

This paper describes the fabrication and characterization of Lithium Niobate (LiNbO3) memristor devices that have the ability to be tuned to a specific resistance state within a continuous resistance range. This is essential for programming neuromorphic systems based on memristor crossbars in order to achieve best deep learning capability. The memristor devices were formed using a 42 nm layer of LiNbO3 sandwiched between two metal electrodes. I-V curves demonstrate a typical and repeatable memristor characteristic from - 3 V to 3 V. Such devices have a continuous resistance range that has a maximum to minimum resistance ratio of about 100, and the ability to program intermediate resistance states. The results also show the ability to read the device symmetrically with a positive or negative voltage, and strong data retention after the programming phase. (C) 2016 Elsevier B.V. All rights reserved.