Synthesis and Characterization of Solution-Processible Graphitic Nanomaterials for Emerging RRAM Device Applications
Resistive Random Access Memory (RRAM) is an emerging non-volatile memory device that has received much attention in the field. Due to a rare combination of its simple structure, low cost, high speed, and low power consumption, it is considered as one of the most promising candidates for the future computing paradigm. Recently, RRAM is investigated as a critical element for neuromorphic computing where gray-scale or intermediate resistance states of RRAM are used to emulate a synaptic function. However, one critical challenge in using RRAM for neuromorphic computing is that RRAM exhibits a relatively high variability of its switching characteristics and parameters. For example, a great cycle-to-cycle variability of resistance values may significantly degrade the model accuracy of machine learning. In this doctoral dissertation, we propose a novel idea to mitigate the large variability issue of RRAM by inserting a graphitic nanosheet as the predictable oxygen exchange layer (OEL). This new OEL features the same amount of defect sites for oxygen migration in each programming cycle. We used the techniques of the improved Hummer's method and the chemical reduction to synthesize reduced graphene oxide (rGO) while fabricating the rGO-inserted RRAM device by using the sputtering and the shadow mask techniques. It is found that the rGO-RRAM exhibits a reduced variability in SET switching voltages as well as resistances at low-resistance state (LRS) and high-resistance state (HRS).