Approaching the Ideal Linearity in Epitaxial Crystalline-type Memristor by Controlling Filament Growth.

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behaviour with high asymmetry, which aggravates the complexity of peripheral circuit system.[6-10] Controllable growth of conductiv filaments is highly demanded for achieving the highly-linear conductance modulat ion. However, the stochastic behaviour of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, we report the epitaxially grown Hf0.5 Zr0.5 O2 -based memristor with the linearity and symmetry approaching ideal case. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5 Zr0.5 O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly-linear and multi-level conductance modulation capacity. Our method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing. This article is protected by copyright. All rights reserved.