Analog Synapses Based on Nonvolatile FETs With Amorphous ZrO<sub>2</sub> Dielectric for Spiking Neural Network Applications
Huan Liu, Jing Li, Guosheng Wang, Jiajia Chen, Xiao Yu, Yan Liu, Chengji Jin, Shulong Wang, Yue Hao, Genquan Han
Abstract
In this article, an analog synapse based on a nonvolatile field-effect transistor with amorphous ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> dielectrics has been fabricated and demonstrated. The conductance modulation properties of the devices have been systematically evaluated. Due to the polarization switching dynamics of the ferroelectric-like amorphous ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> dielectric, which is attributable to the voltage-driven oxygen vacancies and negative charges dipoles, the proposed ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based devices exhibit superior synaptic characteristics, including good symmetry and linearity for both potentiation and depression, with small cycle-to-cycle variations. The ratio of maximum conductance and minimum conductance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${G}_{max}/{G}_{min})$ </tex-math></inline-formula> of devices reaches 130, with conductance states over 30. Also, spike-timing-dependent plasticity (STDP) has been mimicked in the devices successfully. Furthermore, based on the experimental STDP characteristics and conductance modulation properties of potentiation and depression, a spiking neural network architecture constructed by the proposed ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based synapses has been simulated. High offline and online learning accuracy of 94% and 87%, respectively, on the handwritten digits dataset, has been achieved.