Credit score: College of Science and Know-how of China
Typical von Neumann computing programs have grown outdated with the fast growth of neuro-inspired computing. Memristor-based synaptic units, which emulate organic synapses, are believed to be promising in realizing environment friendly neuro-inspired computing. Nonetheless, beforehand developed memristors suffered from both excessive power consumption or unstableness. Ferroelectric tunnel junction (FTJ) is a brand new candidate for memristor building due to its steady information storage function, but it surely fails to fulfill the fascinating necessities when it comes to endurance, power consumption, linearity and so forth.
In a latest work printed in Nature Communications, a crew led by Prof. Li Xiaoguang and Prof. Yin Yuewei from the College of Science and Know-how of China (USTC) of the Chinese language Academy of Sciences, developed a novel FTJ synapse primarily based on Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3.
The crew comprehensively studied the properties of the newly developed FTJ synapse. Below low voltage and operation velocity near that of Dynamic Random Entry Reminiscence (DRAM), The FTJ pattern displayed 256 conductance states with passable linearity and stability. The ON/OFF ratio was as excessive as 200, and an endurance as much as 109 was additionally achieved. Even when voltage pulse near CPU velocity was utilized, the pattern nonetheless displayed 150 conductance states and low cycle-to-cycle variation.
With a purpose to research the efficiency of the FTJ synapse underneath actual circumstances, the crew carried out convolutional neural community simulations primarily based on the check results of the FTJ pattern. The goal of the simulation was to acknowledge trend product photos in F-MNIST dataset, and a excessive recognition of 94.7% primarily based on 256 states was achieved. The efficiency was corresponding to that achieved by floating-point-based software program.
Noisy photos, that are widespread these days, have introduced nice issue to picture recognition. Thus, the team then carried out simulation on noisy photos with slat & pepper noise or Gaussian noise and the popularity accuracy remained excessive, demonstrating the reliability of the newly developed memristor primarily based on FTJ synapse.
These outcomes proved that (111)-oriented FTJs did maintain promise for neuro-inspired computing.
Zhen Luo et al, Excessive-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing, Nature Communications (2022). DOI: 10.1038/s41467-022-28303-x
College of Science and Know-how of China
Ferroelectric tunnel junction allows superior neuro-inspired computing (2022, February 25)
retrieved 26 February 2022
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