Laterally Gated CuInPS Ferroelectric Field Effect Transistors for Neuromorphic Computing.

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 CuInPS (CIPS) and MoS 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), 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.