Ferroelectric
materials with a modulable polarization extent hold
promise for exploring voltage-driven neuromorphic hardware, in which
direct current flow can be minimized. Utilizing a single active layer
of an insulating ferroelectric polymer, we developed a voltage-mode
ferroelectric synapse that can continuously and reversibly update
its states. The device states are straightforwardly manifested in
the form of variable output voltage, enabling large-scale direct cascading
of multiple ferroelectric synapses to build a deep physical neural
network. Such a neural network based on potential superposition rather
than current flow is analogous to the biological counterpart driven
by action potentials in the brain. A high accuracy of over 97% for
the simulation of handwritten digit recognition is achieved using
the voltage-mode neural network. The controlled ferroelectric polarization,
revealed by piezoresponse force microscopy, turns out to be responsible
for the synaptic weight updates in the ferroelectric synapses. The
present work demonstrates an alternative strategy for the design and
construction of emerging artificial neural networks.