摘要
Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity,even in ultra-thin film form.However,studying the weak response from ultra-thin film requires improved measurement sensitivity.In general,resonance-enhanced piezoresponse force microscopy(PFM)has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum.However,an iterative approach,such as traditional least squares(LS)fitting,is sensitive to noise and can result in the misunderstanding of weak responses.In this study,we developed the deep neural network(DNN)hybrid with deep denoising autoencoder(DDA)and principal component analysis(PCA)to extract resonance information.The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm,allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf_(0.5)Zr_(0.5)O_(2) thin films.Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.
基金
This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C2009642)
Basic Science Research Program through the NRF funded by the Ministry of Education(No.2019R1A6A1A03033215).Some coding and analysis research was supported by the Center for Nanophase Materials Sciences(NMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory。
