Continuous improvements of defectivity rates in immersion photolithography via functionalized membranes in point-of-use photochemical filtration

D’Urzo, Lucia; Bayana, Hareen; Vandereyken, Jelle; Foubert, Philippe; Wu, Aiwen; Jaber, Jad A.; Hamzik, James

doi:10.1117/12.2258582

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2023

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“…Section 3 describes the classiőer that classiőes images into four types, and two ways to remove the structure for linear and patterned images. A segmentation neural network (a) Flat [36] (b) Linear [10] (c) Patterned [27] (d)…”

Section: Introductionmentioning

confidence: 99%

Defect Inspection in Semiconductor Images Using FAST-MCD Method and Neural Network

Han²,

Lee³

2023

Preprint

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Most defect inspection methods used in semiconductor manufacturing require design layout or golden die images.Unlike methods that require such additional information, this paper presents a method for automatic inspection of defects in semiconductor images with a single image.First, we devise a method to classify images into four types: flat, linear, patterned, and complex using a cosine similarity.For linear and patterned images, we obtain defect-free images that retain the structure.Then, subtract defect-free image from input image to get a flat image.The FAST-MCD method then estimates the parameters of the inlier distribution of the flat image and uses them to detect defects.A segmentation neural network is used to detect defects in complex images.

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Section: Introductionmentioning

confidence: 99%