  In terms of energy efficiency and computational speed, neuromorphic
electronics based on non-volatile memory devices is expected to be one of most
promising hardware candidates for future artificial intelligence (AI). However,
catastrophic forgetting, networks rapidly overwriting previously learned
weights when learning new tasks, remains as a pivotal hurdle in either digital
or analog AI chips for unleashing the true power of brain-like computing. To
address catastrophic forgetting in the context of online memory storage, a
complex synapse model (the Benna-Fusi model) has been proposed recently[1],
whose synaptic weight and internal variables evolve following a diffusion
dynamics. In this work, by designing a proton transistor with a series of
charge-diffusion-controlled storage components, we have experimentally realized
the Benna-Fusi artificial complex synapse. The memory consolidation from
coupled storage components is revealed by both numerical simulations and
experimental observations. Different memory timescales for the complex synapse
are engineered by the diffusion length of charge carriers, the capacity and
number of coupled storage components. The advantage of the demonstrated complex
synapse in both memory capacity and memory consolidation is revealed by neural
network simulations of face familiarity detection. Our experimental realization
of the complex synapse suggests a promising approach to enhance memory capacity
and to enable continual learning.
