Huang Youna, Wang Linkun, Zhang Fengyuan, Ming Wenjie, Liu Yuxin, Zhu Shenglong, Li Yang, Wang Wei, Li Changjian
Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Pengcheng Laboratory, Shenzhen, Guangdong 518000, China.
ACS Appl Mater Interfaces. 2025 Jul 16;17(28):40623-40629. doi: 10.1021/acsami.5c04625. Epub 2025 Jul 4.
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.
随着人工智能（AI）技术的快速发展，对数据存储和神经形态内存计算的需求不断增加。将半导体与功能性铁电体相结合的铁电场效应晶体管（FeFET）在克服冯·诺依曼架构的瓶颈方面具有巨大潜力。横向栅控FeFET（LG-FeFET）采用面内电场来切换面外极化，具有低漏电流和降低器件高度以利于器件集成的优点。在此，我们展示了在范德华（vdW）异质结构中利用铁电CuInPS（CIPS）和MoS半导体的二维（2D）横向栅控FeFET（LG-FeFET）器件，其具有多级数据处理能力和可调谐的突触功能。二维LG-FeFET表现出大的记忆窗口（10 V）、低漏电流（<0.01 nA）和大的开/关比（10），显著优于垂直栅控FET。该器件在电刺激下成功模拟了突触的可塑性，包括长期和短期可塑性。我们的原位压电力显微镜（PFM）测量证实，二维LG-FeFET器件中的多个电导状态直接由极化演化动力学控制。此外，使用这种突触器件对用于手写数字识别的神经网络进行在线训练，获得了高识别准确率（97.4%）。最后，基于该器件的短期可塑性，我们展示了用于图像分类的储层计算。我们的结果表明，LG-FeFET器件在高密度数据处理系统和神经形态计算应用方面具有巨大潜力。
