Wang Zijian, Guan Zeyu, Wang He, Zhou Xiang, Li Jiachen, 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, Anhui 230026, People's Republic of China.
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China.
ACS Appl Mater Interfaces. 2024 May 1;16(17):22122-22130. doi: 10.1021/acsami.4c01234. Epub 2024 Apr 16.
The recent discovery of ferroelectricity in pure ZrO has drawn much attention, but the information storage and processing performances of ferroelectric ZrO-based nonvolatile devices remain open for further exploration. Here, a ZrO (∼8 nm)-based ferroelectric capacitor using RuO oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2) of >30 μC/cm, leakage current density of ∼2.79 × 10 A/cm at 1 MV/cm, and estimated polarization retention of >10 years are achieved. When the ferroelectric capacitor is connected with a transistor, a memory window of ∼0.8 V and eight distinct states can be obtained in such a ferroelectric field-effect transistor (FeFET). Through the conductance manipulation of the FeFET, a high object image recognition accuracy of ∼93.32% is achieved on the basis of the CIFAR-10 dataset in the convolutional neural network (CNN) simulation, which is close to the result of ∼94.20% obtained by floating-point-based CNN software. These results demonstrate the potential of ferroelectric ZrO devices for nonvolatile memory and artificial neural network computing.
最近在纯ZrO中发现铁电性引起了广泛关注，但基于铁电ZrO的非易失性器件的信息存储和处理性能仍有待进一步探索。在此，制备了一种使用RuO氧化物电极的基于ZrO（约8纳米）的铁电电容器，并研究了电场循环下铁电正交相的演变。实现了大于30 μC/cm²的铁电剩余极化、在1 MV/cm下约2.79×10⁻⁶ A/cm²的漏电流密度以及估计大于10年的极化保持时间。当铁电电容器与晶体管连接时，在这种铁电场效应晶体管（FeFET）中可获得约0.8 V的存储窗口和八个不同状态。通过对FeFET的电导操纵，在卷积神经网络（CNN）模拟中基于CIFAR-10数据集实现了约93.32%的高目标图像识别准确率，这与基于浮点的CNN软件获得的约94.20%的结果相近。这些结果证明了铁电ZrO器件在非易失性存储器和人工神经网络计算方面的潜力。
