Dynamic capacitive analysis and physical modeling on ZnO resistive random access memory (RRAM) for enabling neuromorphic computing

Abstract

With the increasing demand for large data storage and artificial intelligence, resistive random-access memory (RRAM) thrives as one of the applicable candidates for the next-generation nonvolatile memory, owing to its simple structure, high scalability, high speed, low power, and tunable conductance. Among oxide-based RRAM, ZnO shows unique optical and electrical properties toward the future heterogeneous integration and low power memory-in-computing systems. In this study, we present a ZnO RRAM manufactured under earth gravity and in-space through inkjet printing. Memory devices with various fabrication environments and conditions include methanol ground, methanol flight, ethanol ground, to ethanol flight. The device fabricated under the microgravity shows a significantly reduced forming voltage and improved reliability. To investigate the filamentary formation in the ZnO RRAM, activation energy was extracted from Arrhenius equations on temperature modulations testing schemes for a comprehensive filament modeling. The capacitive models have concluded oxygen migration conduction dominated on this ZnO RRAM. Finally, the devices' conductance was modulated by AC potentiation and depression with an optimized linearity (R2 = 98%) toward a good training accuracy of 90% on the MNIST data set training toward neuromorphic computing.