Tailoring Neuroplasticity in a Ferroelectric-Gated Multi-Terminal Synaptic Transistor by Bi-Directional Modulation for Improved Pattern Edge Recognition

The dynamic modulation of the plasticity of artificial neuromorphic devices facilitates a wide range of neuromorphic functions. However, integrating diverse plasticity modulation techniques into a single device presents a challenge due to limitations in the device structure design. Here, a multiterminal artificial synaptic device capable of bi-directional modulation on its plasticity is proposed. Significantly, the conversion of inhibitory and excitatory synaptic plasticity can be achieved not only by modifying the polarity of the presynaptic voltage spike but also by exchanging its input terminal between top and bottom gate while maintaining the same presynaptic stimuli. This unique bi-directional modulation of synaptic plasticity has been attributed to two distinct physical mechanisms: nonvolatile ferroelectric polarization and interface charge trap-induced memory characteristics. Additionally, the effective dynamic modulation of the synaptic behaviors is quantified under different back-gate bias and verified in the constructed neural network perceptron. Further, a visual simulation demonstrates the enhanced clarity and precision of edge recognition through the back-gate modulation in the artificial synapses. This study provides a strategy to fulfill diversified modulation on synaptic plasticity in ferroelectric-gated transistors, thereby prompting efficient and controllable neuromorphic visual systems.