Biological
nervous systems evolved in nature have marvelous information
processing capacities, which have great reference value for modern
information technologies. To expand the function of electronic devices
with applications in smart health monitoring and treatment, wearable
energy-efficient computing, neuroprosthetics, etc., flexible artificial
synapses for neuromorphic computing will play a crucial role. Here,
carbon nanotube-based ferroelectric synaptic transistors are realized
on ultrathin flexible substrates via a low-temperature approach not
exceeding 90 °C to grow ferroelectric dielectrics in which the
single-pulse, paired-pulse, and repetitive-pulse responses testify
to well-mimicked plasticity in artificial synapses. The long-term
potentiation and long-term depression processes in the device demonstrate
a dynamic range as large as 2000×, and 360 distinguishable conductance
states are achieved with a weight increase/decrease nonlinearity of
no more than 1 by applying stepped identical pulses. The stability
of the device is verified by the almost unchanged performance after
the device is kept in ambient conditions without additional passivation
for 240 days. An artificial neural network-based simulation is conducted
to benchmark the hardware performance of the neuromorphic devices
in which a pattern recognition accuracy of 95.24% is achieved.