With
the advent of artificial intelligence (AI), memristors have
received significant interest as a synaptic building block for neuromorphic
systems, where each synaptic memristor should operate in an analog
fashion, exhibiting multilevel accessible conductance states. Here,
we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl
cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional
binary to synaptic analog switching can be achieved simply by reducing
the size of the formed filament. With the quantized conductance states
observed in the flexible pV3D3 memristor, analog potentiation and
depression characteristics of the memristive synapse are obtained
through the growth of atomically thin Cu filament and lateral dissolution
of the filament via dominant electric field effect, respectively.
The face classification capability of our memristor is evaluated via
simulation using an artificial neural network consisting of pV3D3
memristor synapses. These results will encourage the development of
soft neuromorphic intelligent systems.