  As global data generation continues to rise, there is an increasing demand
for revolutionary in-memory computing methodologies and efficient machine
learning solutions. Despite recent progress in electrical and electro-optical
simulations of machine learning devices, the all-optical nonthermal function
remains challenging, with single wavelength operation still elusive. Here we
report on an optical and monochromatic way of neuromorphic signal processing
for brain-inspired functions, eliminating the need for electrical pulses.
Multilevel synaptic potentiation-depression cycles are successfully achieved
optically by leveraging photovoltaic charge generation and polarization within
the photoferroelectric substrate interfaced with the graphene sensor.
Furthermore, the demonstrated low-power prototype device is able to reproduce
exact signal profile of brain tissues yet with more than two orders of
magnitude faster response. The reported properties should trigger all-optical
and low power artificial neuromorphic development based on photoferroelectric
structures.
