材料科学
硅
复合数
电压
光电子学
铁电性
纳米技术
电气工程
复合材料
工程类
电介质
作者
He Wang,Zeyu Guan,Jiachen Li,Zhen Luo,Xinzhe Du,Zijian Wang,Haoyu Zhao,Shengchun Shen,Yuewei Yin,Xiaoguang Li
标识
DOI:10.1002/adma.202211305
摘要
The big data era requires ultrafast, low-power, and silicon-compatible materials and devices for information storage and processing. Here, ferroelectric tunnel junctions (FTJs) based on SiO2/Hf0.5Zr0.5O2 composite barrier and both conducting electrodes are designed and fabricated on Si substrates. The FTJ achieves the fastest write speed of 500 ps under 5 V (2 orders of magnitude faster than reported silicon-compatible FTJs) or 10 ns speed at a low voltage of 1.5 V (the lowest voltage among FTJs at similar speeds), low write current density of 1.3 × 104 A cm-2, 8 discrete states, good retention > 105 s at 85 °C, and endurance > 107. In addition, it provides a large read current (88 A cm-2) at 0.1 V, 2 orders of magnitude larger than reported FTJs. Interestingly, in FTJ-based synapses, gradually tunable conductance states (128 states) with high linearity (<1) are obtained by 10 ns pulses of <1.2 V, and a high accuracy of 91.8% in recognizing fashion product images is achieved by online neural network simulations. These results highlight that silicon-compatible HfO2-based FTJs are promising for high-performance nonvolatile memories and electrical synapses.
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