Wang He, Guan Zeyu, Li Jiachen, Luo Zhen, Du Xinzhe, Wang Zijian, Zhao Haoyu, Shen Shengchun, Yin Yuewei, Li Xiaoguang
Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China.
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China.
Adv Mater. 2024 Apr;36(15):e2211305. doi: 10.1002/adma.202211305. Epub 2024 Feb 7.
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 SiO/HfZrO 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 × 10 A cm, 8 discrete states, good retention > 10 s at 85 °C, and endurance > 10. In addition, it provides a large read current (88 A cm) 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 HfO-based FTJs are promising for high-performance nonvolatile memories and electrical synapses.
大数据时代需要超快速、低功耗且与硅兼容的材料和器件用于信息存储和处理。在此，基于SiO/HfZrO复合势垒以及两个导电电极的铁电隧道结（FTJ）在硅衬底上被设计并制造出来。该FTJ在5V电压下实现了500皮秒的最快写入速度（比已报道的与硅兼容的FTJ快两个数量级），或者在1.5V低电压下达到10纳秒的速度（在类似速度的FTJ中电压最低），写入电流密度低至1.3×10 A/cm²，具有8个离散状态，在85°C下保持时间大于10秒，耐久性大于10次。此外，它在0.1V时提供大的读取电流（88 A/cm²），比已报道的FTJ大两个数量级。有趣的是，在基于FTJ的突触中，通过小于1.2V的10纳秒脉冲可获得具有高线性度（<1）的逐渐可调电导状态（128个状态），并且通过在线神经网络模拟在识别时尚产品图像方面实现了91.8%的高精度。这些结果突出表明，与硅兼容的基于HfO的FTJ在高性能非易失性存储器和电突触方面具有潜力。
