In this study, TiN/NbOx/Pt memristor devices with short-term memory (STM) and self-rectifying characteristics are used for reservoir computing. The STM characteristics of the device are detected using direct current sweep and pulse transients. The self-rectifying characteristics of the device can be explained by the work function differences between the TiN and Pt electrodes. Furthermore, neural network simulations were conducted for pattern recognition accuracy when the conductance was used as the synaptic weight. The emulation of synaptic memory and forgetfulness by short-term memory effects are demonstrated using paired-pulse facilitation and excitatory postsynaptic potential. The efficient training reservoir computing consisted of all 16 states (4-bit) in the memristor device as a physical reservoir and the artificial neural network simulation as a read-out layer and yielded a pattern recognition accuracy of 92.34% for the modified National Institute of Standards and Technology dataset. Finally, it is found that STM and long-term memory in the device coexist by adjusting the intensity of pulse stimulation.
Potentiation and depression, PPF, EPSC, SADP, SNDP
MNIST pattern-recognition simulation/4-bit reservoir computing with CNN
This work
Tab.1
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