Neuromorphic computing based on memristors capable of
in-memory
computing is promising to break the energy and efficiency bottleneck
of well-known von Neumann architectures. However, unstable and nonlinear
conductance updates compromise the recognition accuracy and block
the integration of neural network hardware. To this end, we present
a highly stable memristor with self-assembled vertically aligned nanocomposite
(VAN) SrTiO3:MgO films that achieve excellent resistive
switching with low set/reset voltage variability (4.7%/–5.6%)
and highly linear conductivity variation (nonlinearity = 0.34) by
spatially limiting the conductive channels at the vertical interfaces.
Various synaptic behaviors are simulated by continuously modulating
the conductance. Especially, convolutional image processing using
diverse crossbar kernels is demonstrated, and the artificial neural
network achieves an overwhelming recognition accuracy of up to 97.50%
for handwritten digits. Even under the perturbation of Poisson noise
(λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the
high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%,
respectively. Importantly, the logic memory function is proven experimentally
based on the nonvolatile properties. This work provides a material
system and design idea to achieve high-performance neuromorphic computing
and logic operation.