Abstract
AbstractResistive switching random‐access memory (RRAM) has attracted tremendous interest for applications in both embedded memory and neuromorphic computing. In this paper, two distinct types of resistive switching in Ti/TiOx/Pd‐based RRAM devices depending on the bottom electrode morphology is reported. One is filamentary resistive switching where the bottom Pd electrode has spikes caused by liftoff. The enhancement of local electric field caused by the spikes induces the formation of conductive filaments and hence leads the device to be nonvolatile. The other one is dynamic resistive switching, where the bottom electrode is flat without spikes and the device resistance changes dynamically with time. For such devices, the electric field and the oxygen ion concentration gradient in the TiOx layer have competing effects on the oxygen ion movement, which makes the Schottky barrier height at metal/oxide interface vary dynamically with time. Based on detailed physical analysis, a compact behavioral model of the dynamic RRAM device is established. The model fits well with experimentally measured device characteristics, and can be easily incorporated in deep learning algorithms. The work paves the road for further optimization of dynamic RRAM device and also its application in dynamic signal processing.
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