铁电性
材料科学
神经形态工程学
电介质
光电子学
量子隧道
半导体
非易失性存储器
人工神经网络
计算机科学
机器学习
作者
J. Kim,Yongjin Park,Jung Woo Lee,Eunjin Lim,Jung‐Kyu Lee,Sungjun Kim
标识
DOI:10.1002/admt.202400050
摘要
Abstract The use of Hf 0.5 Zr 0.5 O 2 (HZO) films within hafnia‐based ferroelectric tunnel junctions (FTJ) presents a promising avenue for next‐generation non‐volatile memory devices. HZO exhibits excellent ferroelectric properties, ultra‐thinness, low power consumption, nondestructive readout, and compatibility with silicon devices. In this study, Mo/HZO/n + Si devices are investigated, incorporating a 1 nm HfO 2 dielectric layer at the top and bottom of the HZO ferroelectric layer. Comparing the FTJ device configurations, it is observed that the metal‐ferroelectric‐dielectric‐semiconductor (MFIS) outperforms the metal‐dielectric‐ferroelectric‐semiconductor (MIFS) in terms of ferroelectricity, displaying a high 2P r value of ≈69 µC cm −2 . Additionally, MFIS exhibits lower leakage current, higher tunneling electro‐resistance ratio, and a thin dead layer during short pulse switching, as confirmed through DC double sweeping of I−V characteristics. The modified half‐bias scheme demonstrates a maximum array size of 191 for MFIS, showcasing its superior performance over MIFS. Synaptic characteristics, including potentiation, depression, paired‐pulse facilitation, spike‐rate‐dependent plasticity, and excitatory postsynaptic current, are measured using MFIS, highlighting its outstanding ferroelectric properties. As a physical reservoir, the FTJ device implements 16 states of 4 bits in reservoir computing. Finally, pattern recognition using a deep learning neural network achieves high accuracy with using the Modified National Institute of Standards and Technology dataset.
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