Non-von-Neumann
computer architecture is gaining a great deal of
interest for eliminating the speed bottleneck in transferring data
between the processing and memory units by improving the processing
parallelism. Hardware-driven neuromorphic systems are pursued actively
for this goal, and they should accompany the innovations in the hardware
components for higher energy efficiency. In this work, an indium gallium
zinc oxide (IGZO)-based synaptic device was developed, and its synaptic
behaviors were closely characterized. Processing simplicity has been
improved in the structure
of the device using a p+-Si bottom electrode (BE) in the
Si substrate, and gradual switching characteristics have been obtained
using a Pd top electrode (TE) with self-graded oxygen concentrations.
By controlling the amount of oxygen atoms in depositing the switching
layer, both highly linear weight adjustability and low-energy operation
capability have been accomplished. In the end, the visual recognition
of the IGZO synaptic device is evaluated with the Modified National
Institute of Standards and Technology (MNIST) patterns in the neural
network.