Reconfigurable Ferroelectric Field-Effect Transistor Integrating Freestanding BaTiO₃ and MoS₂ for Neuromorphic...

In the era of big data and artificial intelligence, the demand for computing capacity is growing exponentially, driving the need for transformative computing technologies. Neuromorphic computing, which adopts a non-von Neumann architecture, has emerged as a promising solution to overcome the limitations of conventional systems. As fundamental building blocks for neuromorphic hardware, artificial synapses are of great importance. Here, we report a reconfigurable ferroelectric field-effect transistor (FeFET) with a metal-ferroelectric-metal-insulator-semiconductor (MFMIS) structure that serves as a three-terminal artificial synapse, integrating memory and computing functionalities. By modulating the input pulse width, this FeFET-fabricated with a ferroelectric perovskite BaTiO₃ and a 2D MoS₂ channel-can be configured as either a non-volatile memory, exhibiting a counterclockwise hysteresis window larger than 3.8 V, or a volatile synaptic device with low power consumption of 1.22 fJ per synaptic event. Furthermore, by leveraging both non-volatile memory and volatile synaptic modes, we demonstrate the device's applications in convolutional neural network (CNN)-based traffic sign recognition and classification for autonomous driving, as well as motion direction decision-making via multi-level programming. These results provide a viable strategy for the development of neuromorphic devices in next-generation computing.