光学精密工程2025，Vol.33Issue(9)：1341-1352,12.DOI:10.37188/OPE.20253309.1341
基于自注意力神经网络的低信噪比光谱干涉膜厚测量
Low signal-to-noise ratio spectral interferometry film thickness measurement based on self-attention neural network
摘要
Abstract
To enhance the robustness of film thickness measurements from low signal-to-noise ratio(SNR)spectral data,a measurement approach based on a self-attention neural network(SANN)is devel-oped.While the conventional Fourier transform method effectively measures thickness on high SNR data,its accuracy deteriorates as noise obscures the principal interference frequency under low SNR conditions,hindering precise thickness extraction.This study introduces a self-attention neural network model that takes spectral data as input and outputs film thickness,employing an adaptive attention mechanism to dy-namically weight spectral points across different wavelengths,thereby improving analysis of low SNR spectral data.Experimental data were obtained using a spectral interference film thickness measurement system and subsequently augmented through wavelength drift and adaptive intensity normalization strate-gies to expand the dataset and enhance the model's generalization.Model optimization identified an archi-tecture comprising eight encoder layers and 128 hidden nodes per layer.Using wafer measurements as a case study,evaluation on spectral data containing outliers demonstrated a maximum relative thickness mea-surement error of 3.62%on the low SNR validation set.These results indicate that the proposed method effectively suppresses noise influence,mitigates outlier deviations common in Fourier transform approach-es,and substantially improves measurement stability.the applicability of the proposed method is validated to a broader range of thin film measurement scenarios.关键词
干涉测量/晶圆厚度/光谱干涉式/自注意力神经网络/抗噪声能力/测量稳定性Key words
interferometric measurement/wafer thickness/spectral interference/self-attention neural network/anti-noise ability/measurement robustness分类
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王晨,王子政,刘曌燃,姚程源,胡春光..基于自注意力神经网络的低信噪比光谱干涉膜厚测量[J].光学精密工程,2025,33(9):1341-1352,12.基金项目
国家重点研发计划资助项目(No.2022YFF0708300) （No.2022YFF0708300）
国家自然科学基金面上项目(No.52475566) （No.52475566）
