Neuromorphic
computing inspired by the neural systems in human brain will overcome
the issue of independent information processing and storage. An artificial
synaptic device as a basic unit of a neuromorphic computing system
can perform signal processing with low power consumption, and exploring
different types of synaptic transistors is essential to provide suitable
artificial synaptic devices for artificial intelligence. Hence, for
the first time, an electret-based synaptic transistor (EST) is presented,
which successfully shows synaptic behaviors including excitatory/inhibitory
postsynaptic current, paired-pulse facilitation/depression, long-term
memory, and high-pass filtering. Moreover, a neuromorphic computing
simulation based on our EST is performed using the handwritten artificial
neural network, which exhibits an excellent recognition accuracy (85.88%)
after 120 learning epochs, higher than most reported organic synaptic
transistors and close to the ideal accuracy (92.11%). Such a novel
synaptic device enriches the diversity of synaptic transistors, laying
the foundation for the diversified development of the next generation
of neuromorphic computing systems.