R&D: Enhanced Analog Synaptic Behavior of SiNx/a-Si Bilayer Memristors Through Ge Implantation
Artificial neural network simulation shows that neuromorphic system with implanted SiNx/a-Si memristor provides 91.3% learning accuracy mainly due to improved linearity.
This is a Press Release edited by StorageNewsletter.com on January 20, 2021 at 2:06 pmNPG Asia Materials has published an article written by Keonhee Kim, Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, South Korea, and School of Electrical Engineering, Korea University, Seoul, 02841, South Korea, Soojin Park, Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, South Korea, and Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, South Korea, Su Man Hu, Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, South Korea, and School of Electrical Engineering, Korea University, Seoul, 02841, South Korea, Jonghan Song, Weoncheol Lim, Center for Advanced Analysis, Korea Institute of Science and Technology, Seoul, 02792, South Korea, Yeonjoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jongkil Park, Jong Keuk Park, Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, South Korea, Byeong-Kwon Ju, School of Electrical Engineering, Korea University, Seoul, 02841, South Korea, Doo Seok Jeong, Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, South Korea, and Inho Kim, Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
Abstract: “Conductive bridging random access memory (CBRAM) has been considered to be a promising emerging device for artificial synapses in neuromorphic computing systems. Good analog synaptic behaviors, such as linear and symmetric synapse updates, are desirable to provide high learning accuracy. Although numerous efforts have been made to develop analog CBRAM for years, the stochastic and abrupt formation of conductive filaments hinders its adoption. In this study, we propose a novel approach to enhance the synaptic behavior of a SiNx/a-Si bilayer memristor through Ge implantation. The SiNx and a-Si layers serve as switching and internal current limiting layers, respectively. Ge implantation induces structural defects in the bulk and surface regions of the a-Si layer, enabling spatially uniform Ag migration and nanocluster formation in the upper SiNx layer and increasing the conductance of the a-Si layer. As a result, the analog synaptic behavior of the SiNx/a-Si bilayer memristor, such as the nonlinearity, on/off ratio, and retention time, is remarkably improved. An artificial neural network simulation shows that the neuromorphic system with the implanted SiNx/a-Si memristor provides a 91.3% learning accuracy mainly due to the improved linearity.“