Wafer defect detection by a polarization-insensitive external differential interference contrast module

Nativ, Amit; Feldman, H.; Shaked, Natan T.

doi:10.1364/ao.57.003534

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…From bare silicon wafers, 200 nm, 1 μm, and 5 μm diameter PSL spheres can be detected effectively. It is commonly used to determine the quality of detection results by calculating the peak signal-to-noise ratio (PSNR) 16 , which is defined as follows:…”

Section: Experiments and Resultsmentioning

confidence: 99%

Spot-scanning laser scattering system for defects detection of wafer surface

Zhou

Luo

Xiong

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

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As semiconductors' critical dimension decreases, higher precision inspection instruments are needed to detect defects in the manufacturing process. Optical inspection methods based on light and dark field microscopy can detect defects on large wafer areas well without damaging the wafer, but the minimum detectable defect size is limited because the defect scattering signal is easily buried by the scattering background noise from the wafer's rough surface. To detect submicron defects on wafer surfaces, a spot-scanning laser scattering scheme is developed based on the dark-field scattering technique. Using the Finite Difference Time Domain (FDTD) method and the inspection scheme, an electromagnetic scattering model of the defect on the wafer surface is established, and the defect characteristics and electromagnetic field distribution are simulated. Moreover, the effects of the collecting aperture angle on the signal intensity of defects and the discrimination of defects of different sizes, as well as the effects of the incident angle on the scattered signal intensity of submicron defects, are examined. A spot-scanning laser scattering experimental platform was built, and 200 nm, 1 μm, and 5 μm diameter polystyrene latex (PSL) spheres were deposited on the wafer surface to verify the validity of the proposed method. Signals of the three sizes of spheres were detected in the stitched images with good discrimination of signal intensity, and the signal of the 200 nm PSL sphere displayed a peak signal-to-noise ratio of 32.07 dB. This method provides a reference for further industrialized defect detection systems on wafer surfaces.

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Section: Experiments and Resultsmentioning

confidence: 99%

Spot-scanning laser scattering system for defects detection of wafer surface

Zhou

Luo

Xiong

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

View full text Add to dashboard Cite

show abstract

“…From the simplest image difference operator to the complex image synthetic algorithm [178], the post-processing algorithm plays a critical role in optical defect inspection in terms of improving SNR and contrast of defects. This is especially the case as deep learning algorithms emerge as a ubiquitous part of our daily life.…”

Section: Post-processing Algorithmsmentioning

confidence: 99%

Optical wafer defect inspection at the 10 nm technology node and beyond

Zhu¹,

Liu²,

Xu³

et al. 2022

Int. J. Extrem. Manuf.

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The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond. Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas: (1) the defect detectability evaluation, (2) the diverse optical inspection systems, and (3) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.

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

Liu,

Zhu,

et al. 2024

Int. J. Extrem. Manuf.

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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 of λ/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 and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

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Wafer defect detection by a polarization-insensitive external differential interference contrast module

Cited by 11 publications

References 22 publications

Spot-scanning laser scattering system for defects detection of wafer surface

Spot-scanning laser scattering system for defects detection of wafer surface

Optical wafer defect inspection at the 10 nm technology node and beyond

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

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