神经形态工程学
铁电性
材料科学
晶体管
非易失性存储器
冯·诺依曼建筑
数码产品
纳米技术
记忆电阻器
工程物理
电介质
计算机科学
铁电RAM
物联网
电气工程
微电子
钙钛矿(结构)
铁电电容器
电子工程
场效应晶体管
缩放比例
计算机体系结构
极化(电化学)
铁电聚合物
电阻随机存取存储器
高效能源利用
认知计算
作者
Enlong Li,Wunan Wang,Yu Liu,Ruixue Wang,Chunlai Luo,Hongmiao Zhou,Shuo Chen,Shuxin Chen,Zhaoren Xie,Kaichen Zhu,Wenwu Li,Junhao Chu
标识
DOI:10.1002/adma.202515480
摘要
The explosive growth of artificial intelligence, big data, and the Internet of Things is driving an unprecedented demand for computing power and energy efficiency. However, conventional von Neumann architectures are increasingly constrained by the physical and economic limits of transistor scaling in the post-Moore era. Ferroelectric transistors (FeFETs) are far more than a novel memory technology and instead represent a revolutionary platform that seamlessly integrates nonvolatile storage, in-memory computation, and multi-modal sensing into a single, energy-efficient device, overcoming the bottlenecks of traditional computing architectures. This review provides a comprehensive overview of ferroelectric materials, including perovskite oxides, hafnium-based compounds, organics, and emerging 2D systems, emphasizing their polarization original mechanisms and structureproperty relationships. This study focuses on the device physics and engineering of three terminal FeFETs, with particular attention to the current issues, optimization strategies, and contrasting operation principles of ferroelectric dielectric and semiconductor-based designs. Finally, the expanding applications of FeFETs in nonvolatile memory, neuromorphic computing, and artificial intelligence hardware from device to system integration is discussed, and an outlook toward scalable, low-power, and multifunctional electronics driven by ferroelectric innovation is presented.
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