Enhanced analog synaptic behavior of SiNx/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 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. An approach that tweaks the structures of a new type of memory chip shows promise for neuromorphic computing applications. In conductive bridging random access memory (CBRAM) technology, nanoscale metal filaments inside special insulators are used to store data in either high or low conductance states. Inho Kim from the Korea Institute of Science and Technology in Seoul, South Korea and colleagues now report beneficial effects when germanium ions are implanted into silicon-based CBRAM using high-energy beams. The team’s microscopy experiments revealed that the implantation caused metal nanoclusters to distribute evenly within the device’s switching layer. These particles enabled smoother transitions between conductance states than conventional CBRAM, better mimicking the analog switching seen in synapses. Neural network simulations demonstrated that ion-implanted devices had significantly improved learning accuracy compared to unmodified CBRAM chips. 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.