基于自注意力神经网络的低信噪比光谱干涉膜厚测量

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中图分类号：TH741 文献标识码：Adoi：10.37188/OPE.20253309.1341

CSTR：32169.14.OPE.20253309.1341

Low signal-to-noise ratio spectral interferometry film thickness measurement based on self-attention neural network

WANG Chen，WANG Zizheng，LIU Zhaoran，YAO Chengyuan，HU Chunguang （StateKeyLaboratoryofPrecisionMeasurement Technology and Instruments， Tianjin University，Tianjin 3OOO72，China） * Corresponding author， E-mail： cghu@tju. edu.cn

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 developed.While the conventional Fourier transform method effectively measures thicknesson 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 attntion mechanism to dynamically 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 strategies to expand the dataset and enhance the model's generalization.Model optimization identified an architecture comprising eight encoder layers and l28 hidden nodes per layer. Using wafer measurements as a case study，evaluation on spectral data containing outliers demonstrated a maximum relative thickness measurement 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 approaches，and substantially improves measurement stability. the applicability of the proposed method is validated to a broader range of thin film measurement scenarios.

ey words： interferometric measurement； wafer thickness； spectral interference； self-attention neuralnetwork；anti-noise ability；measurement robustness

1引言

晶圆厚度测量是半导体制造、光学镀膜和微电子器件加工中的核心环节，在3D集成电路、极紫外光刻和高精度纳米制造中尤为关键[1-3]。（剩余14532字）