Intensive harmonized synapses with amorphous Cu2O-based memristors using ultrafine Cu nanoparticle sublayers formed via atomically controlled electrochemical pulse deposition

• Published in: Materials horizons Vol. 10; no. 9; pp. 3382 - 3392
• Main Authors: Dong Su Kim, Suh, Hee Won, Cho, Sung Woon, Shin Young Oh, Lee, Hak Hyeon, Lee, Kun Woong, Choi, Ji Hoon, Cho, Hyung Koun
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 29.08.2023
• Subjects: Copper oxides; Electric fields; Filaments; Memory devices; Nanoparticles; Neuromorphic computing; Random access memory; Switching; Synapses; Ultrafines
• ISSN: 2051-6347; 2051-6355
• DOI: 10.1039/d3mh00508a

Resistive random-access memory (RRAM) devices have significant advantages for neuromorphic computing but have fatal problems of uncontrollability and abrupt resistive switching behaviors degrading their synaptic performance. In this paper, we propose the electrochemical design of an active Cu2O layer containing a strategic sublayer of ultrafine Cu nanoparticles (U-Cu NPs) to form uniformly dispersed conducting filaments, which can effectively improve the reliability for analog switching of RRAM-based neuromorphic computing. The electrochemical pulse deposited (EPD) U-Cu NPs are linked to the bottom electrode through a semi-conductive path within the bottom Cu2O layer, since the EPD is preferentially carried out on the conductive sites. All Cu2O films with U-Cu NPs are developed in situ in the single electrolyte bath without any pause. The proposed U-Cu NPs can concentrate the external electric field and can generate conductive filament paths for analog resistive switching. The applied electric field was uniformly spread to U-Cu NPs at the center of the active layer and displays resistive switching behavior via multiple conductive filaments. This shows a strong harmony between the resistance-switching characteristics and the analog operation of the active layer. This RRAM device shows outstanding gradual analog switching, great linearity, dynamic range, endurance, precision, speed, and retention characteristics simultaneously and adequately for neuromorphic computing by realizing multiple weak filament-type operation.