横向门 CuInP2S6铁电场效应晶体管用于神经形态计算
Youna Huang1,2,3, Linkun Wang1,2,3, Fengyuan Zhang1,3
1Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
ACS applied materials & interfaces
|July 4, 2025
概括
这项研究引入了用于先进AI的新型2D侧门铁电场效应晶体管 (LG-FeFETs). 这些设备提供多层次的数据处理和可调节的突触功能,优于用于神经形态计算的传统晶体管.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 计算机工程 计算机工程
背景情况:
- 人工智能 (AI) 的日益增长的需求推动了对高效数据存储和神经形态计算的需求.
- 铁电场效应晶体管 (FeFET) 通过整合内存和处理,为诺曼架构瓶提供了一个潜在的解决方案.
- 侧门式FeFET (LG-FeFET) 提供了诸如低泄漏电流和降低设备高度等优势.
研究的目的:
- 使用范德瓦尔斯的铁电CuInP2S6 (CIPS) 和MoS2.2的异构结构,开发和描述二维 (2D) 侧面门 FeFET (LG-FeFET).
- 研究这些二维LG-FeFET的多层次数据处理能力和可调节的突触功能.
- 评估这些设备在神经形态计算应用中的性能,包括神经网络训练和储库计算.
主要方法:
- 在范德瓦尔斯异构结构中使用CIPS和MoS2制造二维LG-FeFET设备.
- 电气特性测量内存窗口,泄漏电流和开/关比.
- 在现场压响应力显微镜 (PFM) 分析极化动态.
- 实现神经网络和储库计算的在线培训设备.
主要成果:
- 二维LG-FeFET显示了大内存窗口 (10V),低泄漏电流 (<0.01nA) 和高开/关比 (10^5).
- 设备在电刺激下成功模拟了突触可塑性 (长期和短期).
- 通过神经网络训练的突触装置,手写数字的高识别精度 (97.4%) 实现了.
- 基于设备的短期可塑性,用于图像分类的储计算得到了证明.
结论:
- 开发的二维LG-FeFET显示了高密度数据处理系统的巨大潜力.
- 这些设备是下一代神经形态计算应用的有希望的候选者.
- 该研究强调了通过二维LG-FeFET中的极化演变动态来控制多重导电状态.
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