Effects of switching layer morphology on resistive switching behavior: A case study of electrochemically synthesized mixed-phase copper oxide memristive devices. (June 2022)

• Record Type: Journal Article
• Authors: Kundale, Somnath S.; Patil, Akhilesh P.; Patil, Snehal L.; Patil, Prashant B.; Kamat, Rajanish K.; Kim, Deok-kee; Kim, Tae Geun; Dongale, Tukaram D.
• Is Part Of: Applied materials today. Volume 27(2022)
• Publication Date: 2022-06
• Subjects: Resistive switching -- Memristive device -- Artificial synapse -- Electrochemical synthesis -- Copper oxide; Materials science -- Periodicals; Materials -- Research -- Periodicals; 620.1105
• DOI: 10.1016/j.apmt.2022.101460
• Languages: English
• ISSNs: 2352-9407

Abstract

Highlights: Studied influence of morphology, thickness, and the bottom electrode on RS effect. Electrodeposited and anodized devices show digital and analog RS effects, respectively. Mimicked complex hebbian learning rules using artificial Cux O based synapse. Demonstrated image edge detection capability using a deep learning algorithm. Identified the conduction and RS mechanisms of electrochemically synthesized devices. Abstract: Resistive switching (RS) behavior can serve as a building block in the development of non-volatile memory and neuromorphic computing applications. Thus far, various device parameters have been modulated appropriately to achieve the desired characteristics from RS devices. However, no clear guideline for device parameter modulation has been reported. Herein, we systematically investigate the effect of the switching layer morphology and thickness, as well as the choice of the bottom electrode, on RS properties by using electrochemically synthesized mixed-phase copper oxide (Cux O) as a model material for memristive devices. By controlling various electrochemical parameters, we have fabricated Cux O switching layers with various morphologies (microcrystal, microcubic, compact thin film, short dendritic nanowire, granular, and nanoparticle). Interestingly, the Cux O-based RS devices fabricated by means of electrodeposition (Cux O/FTO) exhibit the forming-free digital RS property, which is suitable for non-volatile memory, whereas those fabricated by utilizing anodization (Cux O/Cu) exhibit the analog RS property, which makes them suitable for use in synaptic learning applications. A convolutional neural network (CNN) was implemented using experimental synaptic weights of the anodized Cux O RS device for image edge detection application. In addition, conduction mechanisms and possible RS mechanisms are suggested for both types of devices. These results indicate that the electrochemically synthesized switching layers are promising for use in both non-volatile memory and neuromorphic computing applications. Graphical abstract (TOC): Image, graphical abstract