R&D: Pure ZrO2 Ferroelectric Thin Film for NVM and Neural Network Computing

ACS Applied Materials & Interfaces has published an article written by Zijian Wang, Zeyu Guan, He Wang, Xiang Zhou, Jiachen Li, Shengchun Shen, Yuewei Yin, 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, and Xiaoguang Li, 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, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People’s Republic of China.

Abstract: “The recent discovery of ferroelectricity in pure ZrO₂ has drawn much attention, but the information storage and processing performances of ferroelectric ZrO₂-based nonvolatile devices remain open for further exploration. Here, a ZrO₂ (∼8 nm)-based ferroelectric capacitor using RuO₂ oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2P_r) of >30 μC/cm², leakage current density of ∼2.79 × 10^–8 A/cm² 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 ZrO₂ devices for nonvolatile memory and artificial neural network computing.“