Analog Synapses Based on Nonvolatile FETs With Amorphous ZrO2 Dielectric for Spiking Neural Network Applications

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.