Deterministic Conductive Filament Formation and Evolution for Improved Switching Uniformity in Embedded...

The extreme device-to-device variation of switching performance is one of the major obstacles preventing the applications of metal-oxide-based memristors in large-scale memory storage and resistive neural networks. Recent experimental works have reported that embedding metal nano-islands (NIs) in metal oxides can effectively improve the uniformity of the memristors, but the underlying role of the NIs is not fully understood. Here, to address this specific problem, we develop a physical model to understand the origin of the variability and how the embedded NIs can improve the performance and uniformity of memristors. We find that due to the dimension confinement effect, embedding metal NIs can modulate the electric field distribution and lead to a more deterministic formation of the conductive filament (CF) from their vicinity, in contrast to the random growth of CFs without embedded NIs. This deterministic CF formation, via vacancy nucleation, further reduces the forming, reset, and set voltages and enhances the uniformity of the operation voltages and current ON/OFF ratios. We further demonstrate that modifying the shapes of the metal NIs can modulate the field strengths/distributions around the NIs and that choosing the NI metal composition and shape that chemically facilitate vacancy formations can further optimize the CF morphology, reduce the operation voltages, and improve the switching performance. Our work thus provides a fundamental understanding of how embedded metal NIs improve the resistive switching performance in oxide-based memristors and could potentially guide the selection of embedded NIs to realize a more uniform memristor that also operates at a higher efficiency than present materials.