热膨胀工程铁电晶体管阵列用于可扩展边缘AI计算
Geonwook Kim1, Hyunho Seok2,3,4, Sihoon Son3,4
1School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
ACS nano
|January 27, 2026
概括
本研究介绍了可重新配置的铁电场效应晶体管 (FeFET),可以克服传统计算瓶. 这些设备能够为人工智能应用程序提供高效的内存计算,提高速度并降低能源消耗.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 人工智能的人工智能
背景情况:
- ·诺伊曼架构在人工智能中面临能量和延迟问题,原因是单独的内存和逻辑.
- 铁电晶体管为内存计算提供了潜力,但需要重新配置.
研究的目的:
- 为高效的人工智能工作负载开发可重新配置的铁电晶体管平台.
- 为了证明使用这些新型设备进行内存计算的可行性.
主要方法:
- 工程金属-铁电金属绝缘体-半导体 (MFMIS) 结构使用 (W) 或化 (TiN) 门电极.
- 使用了氧化 (HZO) 铁电层,并评估了设备性能指标.
- 模拟了VGG-8卷积神经网络,并进行了实验阵列操作.
主要成果:
- 在W门的MFMIS-FeFET中实现了高性能:大内存窗口 (~11V),>10^6开/关比,10^12耐久周期和22个可编程状态.
- 在模拟的VGG-8网络中,在非理想条件下,CIFAR-10分类的准确性达到了97.2%.
- 在FeFET数组中实验实现了模拟域卷积用于边缘检测和特征提取.
结论:
- 可重新配置的MFMIS-FeFET阵列是用于神经形态和内存计算系统的可扩展,低功耗平台.
- 启用了内存和逻辑的单体集成,解决了·诺伊曼瓶.
- 该技术支持智能边缘系统和未来超越CMOS计算范式.
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