Various memristive devices have been proposed for use in neuromorphic computing systems as artificial synapses. The analog synaptic devices with linear conductance updates during training are essential to train neural networks efficiently. Although many different analog memristors have been proposed, a more reliable approach to implement the analog synaptic devices are required. In this study, we propose the memristor of a Cu/SiOx/implanted a-SiGex/p++ c-Si structure containing a-Si layer with properly controlled conductance through Ge implantation. The a-SiGex layer plays a multi-functional role in the device operation by limiting current overshoot, confining heat generated during operation and preventing silicide formation reaction between active metal (Cu) and the Si bottom electrode. Thus, the a-SiGex interface layer enables the formation of multi-weak filaments and in turn induce analog switching behaviors. The TEM observation reveals the insertion of the a-SiGex layer between SiOx and c-Si suppresses remarkably the formation of copper silicide, and the reliable set/reset operations were secured. The origin of the analog switching behaviors was discussed by analyzing current-voltage characteristics and electron microscopy images. Lastly, the memristive-neural network simulations showed that the memristive devices developed in this study provide a high learning accuracy and be promising in future neuromorphic computing hardware.
