Multifilamentary switching of Cu/SiOx memristive devices with a Ge-implanted a-Si underlayer for analog synaptic devices

• Published in: NPG Asia materials 2023-09, Vol.15 (1), p.48-12, Article 48
• Main Authors: Kim, Keonhee, Lim, Jae Gwang, Hu, Su Man, Jeong, Yeonjoo, Kim, Jaewook, Lee, Suyoun, Kwak, Joon Young, Park, Jongkil, Hwang, Gyu Weon, Lee, Kyeong-Seok, Park, Seongsik, Lee, Wook-Seong, Ju, Byeong-Kwon, Park, Jong Keuk, Kim, Inho
• Format: Article
• Language: English
• Subjects: 142/126; 639/166/987; 639/301/1005/1007; Behavior; Biomaterials; Chemistry and Materials Science; Constraining; Copper; Current voltage characteristics; Energy Systems; Filaments; Germanium; Hardware; Insertion; Intermetallic compounds; Ion implantation; Materials Science; Memory devices; Memristors; Neural networks; Neuromorphic computing; Optical and Electronic Materials; Silicides; Silicon; Structural Materials; Surface and Interface Science; Switching; Synapses; Thin Films
• ISSN: 1884-4057; 1884-4049; 1884-4057
• DOI: 10.1038/s41427-023-00495-8

Various memristive devices have been proposed for use in neuromorphic computing systems as artificial synapses. Analog synaptic devices with linear conductance updates during training are efficiently essential to train neural networks. Although many different analog memristors have been proposed, a more reliable approach to implement analog synaptic devices is needed. In this study, we propose the memristor of a Cu/SiO x /implanted a-SiGe x /p ++ c-Si structure containing an a-Si layer with properly controlled conductance through Ge implantation. The a-SiGe x layer plays a multifunctional role in device operation by limiting the current overshoot, confining the heat generated during operation and preventing the silicide formation reaction between the active metal (Cu) and the Si bottom electrode. Thus, the a-SiGe x interface layer enables the formation of multi-weak filaments and induces analog switching behaviors. The TEM observation shows that the insertion of the a-SiGe x layer between SiO x and c-Si remarkably suppresses the formation of copper silicide, and reliable set/reset operations are secured. The origin of the analog switching behaviors is discussed by analyzing current-voltage characteristics and electron microscopy images. Finally, the memristive-neural network simulations show that our developed memristive devices provide high learning accuracy and are promising in future neuromorphic computing hardware. Breakthrough in neuromorphic hardware: the advanced memristor Researchers develop a multilayered memristor with gradual switching behavior for neuromorphic computing applications. The device consists of Cu/SiOx/a-SiGex/c-Si layers, and its resistance is controlled by varying the implantation dose of Ge ions. This approach suppresses abrupt switching and induces gradual switching in CBRAM devices, enabling precise modulation of conductance levels and improved performance in neuromorphic computing. The Cu-based bilayer device exhibits promising analog behavior, maintaining high on-off ratios through the insertion and conductivity tuning of the current limiting layer. Simulations using memristive neural networks show a high recognition efficiency approaching 90%, demonstrating the potential for Cu-based bilayer memristor devices as artificial synapses in neuromorphic computing systems. This work presents a design guide for anlog memristive devices for artificial synapses in neuromorphic computing. Ge implanted a-Si serves multiple fuctions to i