Enhanced analog synaptic behavior of SiN _x/a-Si bilayer memristors through Ge implantation

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 SiN_x/a-Si bilayer memristor through Ge implantation. The SiN_x 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 SiN_x layer and increasing the conductance of the a-Si layer. As a result, the analog synaptic behavior of the SiN_x/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 SiN_x/a-Si memristor provides a 91.3% learning accuracy mainly due to the improved linearity.