神经形态工程学
冯·诺依曼建筑
瓶颈
计算机科学
材料科学
人工神经网络
晶体管
逻辑门
功率消耗
功率(物理)
光电子学
计算机硬件
嵌入式系统
电子工程
电气工程
电压
人工智能
工程类
物理
算法
量子力学
操作系统
作者
Yongkai Liu,Tianyu Wang,Kangli Xu,Zhenhai Li,Jiajie Yu,Jialin Meng,Hao Zhu,Qi Sun,David Wei Zhang,Lin Chen
出处
期刊:Materials horizons
[The Royal Society of Chemistry]
日期:2024-01-01
摘要
Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.
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