Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub>-Based Ferroelectric Tunnel Junction as an Artificial Synapse for Speech Recognition
Y. H. Lu, Zeyu Guan, Bo Xu, Shengchun Shen, Yuewei Yin, Xiaoguang Li
Abstract
HfO 2 -based ferroelectric tunnel junctions (FTJs) are currently receiving significant attention in the fields of nonvolatile memory and neuromorphic computing. Here, an FTJ memristor utilizing a Pt/Hf 0.5 Zr 0.5 O 2 /TiO 2 /TiN architecture with a TiO 2 interlayer is prepared, and it exhibits stable resistive switching with an ON/OFF ratio reaching 5.8 × 10 2 at an operational speed of 50 ns, along with good retention exceeding 10 5 s (extended to over 10 years by linear extrapolation) at high temperatures up to 160 °C. Additionally, the TiO 2 interlayer improves the interface between HZO and TiN, resulting in superior resistive switching endurance of 2 × 10 8 cycles with an ON/OFF ratio greater than 50, compared to other HfO 2 -based FTJ devices. As an artificial synapse, the FTJ attains highly symmetric 128-state conductance manipulation with a low cycle-to-cycle variation of 2.75%. When leveraged in a simulated convolutional neural network for speech recognition tasks, the system achieves high accuracy ∼97.6%. Remarkably, even with a signal-to-noise ratio of 10 dB, the recognition accuracy remains at 90.2%, highlighting the remarkable noise immunity. These results underscore the significant potential of FTJs in applications involving multistate nonvolatile memory and artificial synapses, heralding advance in the field of neuromorphic computing and beyond.