Laterally Gated CuInP<sub>2</sub>S<sub>6</sub> Ferroelectric Field Effect Transistors for Neuromorphic Computing
Youna Huang, Linkun Wang, Fengyuan Zhang, Wenjie Ming, Yuxin Liu, Shenglong Zhu, Yang Li, Wei Wang, Changjian Li
Abstract
With the rapid development of artificial intelligence (AI) technologies, the demand for data storage and neuromorphic in-memory computing has been increasing. Ferroelectric field effect transistors (FeFETs) that couple semiconductors with functional ferroelectrics hold great promise for overcoming the bottlenecks of the von Neumann architecture. A laterally gated FeFET (LG-FeFET) employs an in-plane electric field to switch the out-of-plane polarization, offering the benefit of low leakage current and reduced device height for device integration. Here, we demonstrate two-dimensional (2D) laterally gated FeFET (LG-FeFET) devices utilizing ferroelectric CuInP 2 S 6 (CIPS) and MoS 2 semiconductors in a van der Waals (vdW) heterostructure, exhibiting multilevel data processing capabilities and tunable synaptic functions. The 2D LG-FeFET exhibits a large memory window (10 V), low leakage current (<0.01 nA), and a large on/off ratio (10 5 ), dramatically outperforming the vertical gate FETs. The device successfully emulates the synapses’ plasticity under electric stimuli, including long-term and short-term plasticity. Our in situ piezoresponse force microscopy (PFM) measurement confirms that the multiple conductance states in 2D LG-FeFET devices are directly controlled by the polarization evolution dynamics. Furthermore, using this synaptic device for online training of a neural network for recognition of handwritten digits, a high recognition accuracy (97.4%) is attained. Finally, based on the short-term plasticity of the device, we demonstrated reservoir computing for image classification. Our results show that the LG-FeFET device holds great promise for high-density data processing systems and neuromorphic computing applications.