  Traditional computation based on von Neumann architecture is limited by the
time and energy consumption due to data transfer between the storage and the
processing units. The von Neumann architecture is also inefficient in solving
unstructured, probabilistic, and real-time problems. To address these
challenges, a new brain-inspired neuromorphic computational architecture is
required. Due to absence of resistance-capacitance (RC) delay, high bandwidth
and low power consumption, optoelectronic artificial synaptic devices are
highly attractive. Yet stable, scalable, and
complementary-metal-oxide-semiconductor (CMOS)-compatible synapses have not
been demonstrated. In this work, persistence in the photoconductivity of
undoped and magnesium-doped scandium nitride (ScN) is equated to the inhibitory
and excitatory synaptic plasticity of the biological synapses responsible for
memory and learning. Primary functionalities of a biological synapse like
short-term memory (STM), long-term memory (LTM), the transition from
STM-to-LTM, learning and forgetting, frequency-selective optical filtering,
frequency-dependent potentiation and depression, Hebbian learning, and logic
gate operations are demonstrated.
