journal article·2022-12

A robust high-performance electronic synapse based on epitaxial ferroelectric Hf0.5Zr0.5O2 films with uniform polarization and high Curie temperature

Yankun Wang, Qiang Wang, Jinyan Zhao, Tore Niermann, Yangyang Liu, Liyan Dai, Kun Zheng, Yanxiao Sun, Yijun Zhang, Jutta Schwarzkopf, Thomas Schroeder, Zhuangde Jiang, Wei Ren, Gang Niu

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Abstract

Ferroelectric tunnel junction (FTJ) is a promising emerging memristor for the artificial synapse in neuro-inspired computing, which has parallel data processing and low power consumption. The achievement of high-performance electronic synapses requires in-depth exploration of the correlation between the material properties and the device performances as well as the related physical mechanism, which are, however, still quite lacking. We demonstrate here a robust electronic synapse realized by epitaxial ferroelectric Hf0.5Zr0.5O2 (HZO) films with a high Curie temperature of 930 °C and a pristine highly uniform polarization. Based on the optimized ferroelectric HZO film and in-depth understanding of the FTJ mechanism, a robust and high-performance electronic synapse has been successfully realized with high ON/OFF ratio of >500, large continuous conductance regulation range of 1–250 nS and high reliability with the retention of >104 s. Such electronic synapses show good multilevel conductance modulations and synaptic behaviors, such as long-term potentiation (LTP), long-term depression (LTD) and spike-timing dependent plasticity (STDP). A simulated neural network with the synaptic characteristics indicates high recognition accuracy (93.7%) for MNIST database. These results pave a pathway to apply HZO based electronic synapses as the active block in future neuromorphic computing.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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