Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence  

作　　者：Jiamin Liu Jinlong Zhu Zhe Yu Xianrui Feng Zedi Li Lei Zhong Jinsong Zhang Honggang Gu Xiuguo Chen Hao Jiang Shiyuan Liu 

机构地区：[1]State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,People’s Republic of China [2]Optics Valley Laboratory,Wuhan 430074,People’s Republic of China

出　　处：《International Journal of Extreme Manufacturing》2025年第1期623-639,共17页极端制造(英文)

基　　金：funded by National Natural Science Foundation of China(Grant Nos.52130504,52305577,and 52175509);the Key Research and Development Plan of Hubei Province(Grant No.2022BAA013);the Major Program(JD)of Hubei Province(Grant No.2023BAA008-2);the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ047);the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003);the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(Grant No.GZB20230244);the fellowship from the China Postdoctoral Science Foundation(2024M750995)。

摘　　要：In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting

关 键 词：defect inspection form birefringence breaking high order difference anisotropic illumination modes deep-subwavelength sensitivity defect classification 

分 类 号：TN3[电子电信—物理电子学]