The Origin of CBRAM With High Linearity, On/Off Ratio, and State Number for Neuromorphic Computing

Abstract

Conductive bridge random access memory (CBRAM) has been concentrated recently for its ultrasmall size, low power consumption, synaptic characteristics, and application in neuromorphic computing. However, the accuracy of the CBRAM array-based neural network is not high enough due to the low linearity, limited ON/OFF ratio, and the number of states. The original illustration and the optimization methods are still paucity. In this work, the origin of the characteristics of CBRAM has been revealed from the filament distribution of the devices, which inspires us to design an inserted graphene structure of CBRAM and preset seeds leading to high linearity (0.995), ON/OFF ratio (26.4), and the number of states (63). The Monte Carlo simulation results reveal that the CBRAM with more seeds can promote a larger number of potential advantage path (PAP) conducing better characteristics. Moreover, the PAP can be modulated by the number of preset seeds. Finally, a handwritten recognition neural network has been realized by using a 1T-1R array, and high recognition accuracy (92%) has been obtained, which shows that devices with higher PAP can eventually promote higher recognition accuracy.