We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing.
- Authors
Luo, Zhen; Wang, Zijian; Guan, Zeyu; Ma, Chao; Zhao, Letian; Liu, Chuanchuan; Sun, Haoyang; Wang, He; Lin, Yue; Jin, Xi; Yin, Yuewei; Li, Xiaoguang
- 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 <1) and symmetric weight updates, with a good endurance of >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 <13 V. Based on the experimental performance, the convolutional neural network simulation achieves a high online learning accuracy of ~94.7% for recognizing fashion product images, close to the calculated result of ~95.6% by floating-point-based convolutional neural network software. Interestingly, the FTJ-based neural network is very robust to input image noise, showing potential for practical applications. This work represents an important improvement in FTJs towards building neuro-inspired computing systems. Brain-inspired computing demands high-performance synapses. Here, the authors report a subnanosecond ferroelectric tunnel junction with 256 conductance states, 109 endurance, and 5.3 fJ/bit energy consumption, satisfactory to build synaptic devices.
- Subjects
TUNNELS; CONVOLUTIONAL neural networks; ON-demand computing; PRODUCT image; ENERGY consumption; COMPUTER systems; SYNAPSES
- Publication
Nature Communications, 2022, Vol 13, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-28303-x