Reconfigurable Ferroelectric Field‐Effect Transistor Integrating Freestanding BaTiO ₃ and MoS ₂ for Neuromorphic Computing

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

ABSTRACT 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 BaTiO 3 and a 2D MoS 2 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.

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