High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

Zhen Luo, Zijian Wang, Zeyu Guan, Chao Ma, Letian Zhao, Chuanchuan Liu, Haoyang Sun, He Wang, Yue Lin, Xi Jin, Yuewei Yin () and Xiaoguang Li ()
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Zhen Luo: University of Science and Technology of China
Zijian Wang: University of Science and Technology of China
Zeyu Guan: University of Science and Technology of China
Chao Ma: University of Science and Technology of China
Letian Zhao: University of Science and Technology of China
Chuanchuan Liu: University of Science and Technology of China
Haoyang Sun: University of Science and Technology of China
He Wang: University of Science and Technology of China
Yue Lin: University of Science and Technology of China
Xi Jin: University of Science and Technology of China
Yuewei Yin: University of Science and Technology of China
Xiaoguang Li: University of Science and Technology of China

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity 109 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages

Date: 2022
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DOI: 10.1038/s41467-022-28303-x

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