Memristive devices are among the most emerging electronic
elements
to realize artificial synapses for neuromorphic computing (NC) applications
and have potential to replace the traditional von-Neumann computing
architecture in recent times. In this work, pulsed laser deposition-manufactured
Ag/TiO2/Pt memristor devices exhibiting digital and analog
switching behavior are considered for NC. The TiO2 memristor
shows excellent performance of digital resistive switching with a
memory window of order ∼103. Furthermore, the analog
resistive switching offers multiple conductance levels supporting
the development of the bioinspired synapse. A possible mechanism for
digital and analog switching behavior in our device is proposed. Remarkably,
essential synaptic functions such as pair-pulse facilitation, long-term
potentiation (LTP), and long-term depression (LTD) are successfully
realized based on the change in conductance through analog memory
characteristics. Based on the LTP-LTD, a neural network simulation
for the pattern recognition task using the MNIST data set is investigated,
which shows a high recognition accuracy of 95.98%. Furthermore, more
complex synaptic behavior such as spike-time-dependent plasticity
and Pavlovian classical conditioning is successfully emulated for
associative learning of the biological brain. This work enriches the
TiO2-based resistive random-access memory, which provides
information about the simultaneous existence of digital and analog
behavior, thereby facilitating the further implementation of memristors
in low-power NC.