Kla Corporation

Asml Masktools B.v.

Kla Tencor Corporation

Korea University Research and Business Foundation

Asml Netherlands B.v.

Sangbong Park

Bjorn Brauer

Hucheng Lee

Xiaochun Li

Jang Fung Chen

Duan-Fu Stephen Hsu

Douglas Van Den Broeke

Abdurrahman Sezginer

Richard Wallingford

Tamer Coskun

Junqing Huang

Ge Cong

Andrei V Shchegrov

Fei Li

Leonid Poslavsky

Hong Chen

Robert M Danen

Santosh Bhattacharyya

Bernd Geh

Liequan Lee

Erfan Soltanmohammadi

Meng Cao

Edita Tejnil

Chung-Wei Hsu

Nurmohammed Patwary

Huan Jin

Bryant Mantiply

Do-Hoon Kim

Gabriel Berger

Boshi Huang

Siqing Nie

Li Yu

Sungchul Yoo

Vladimir Tumakov

Chunwei Song

Weifeng Zhou

Wei Si

Kedar Grama

In-Kyo Kim

Jungyeon Kim

David Dowling

Tarunark Singh

Zhifeng Huang

Dmitri Starodub

Prashant Verma

Hoh Peter In

Methods and systems for detecting defects on a specimen are provided. One system performs double detection in which at least one of the reference images compared to a test image is a computed reference image generated from multiple images corresponding to the test image. The other reference image may or may not be computed from more than one of the multiple images. Such a computed reference image may also be a median-based computed reference generated from multiple-median images generated from different subsets of images in a job of images generated by an inspection subsystem for a specimen. Such a system may also group images for a die row on a specimen into different jobs based on color so that different jobs have different color value ranges. Such grouping may also be performed so that each of the jobs includes a number of images greater than a predetermined, minimum job size.

Detecting defects on specimens

Methods and systems for detecting defects on a specimen are provided. One system computes different candidate reference images from different combinations of images of the specimen generated by an inspection subsystem and combines different portions of the candidate reference images without modification to thereby generate a final reference image. The final reference image is then used for defect detection, which may be single or double detection. The embodiments are particularly useful for defect detection in areas of specimens including only non-resolvable, repeating device patterns, like cell regions, but may be used for inspection of other types of areas as well.

Wafer-to-wafer and within-wafer image contrast variations can be identified and mitigated by extracting an image frame during recipe setup and then during runtime at the same location. Image contrast is determined for the two image frames. A ratio of the contrast for the two image frames can be used to determine contrast variations and focus variation.

Image contrast metrics for deriving and improving imaging conditions

An inspection system may generate first-step images of multiple sample regions after a first process step and generate second-step images of the sample regions after a second process step, where the second process step modifies the sample in at least one of the sample regions. The system may further identify one of the sample regions as a test region and at least some of the remaining sample regions as comparison regions, where the second-step image of the test region is a test image and the second-step images of the comparison regions are comparison images. The system may further generate a multi-step difference image by subtracting a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image. The system may further identify defects in the test region associated with the second process step based on the multi-step difference image.

Inspection with previous step subtraction

Global and local alignment energies are used in an image contrast metric. The image contrast metric can be used to find optical targets. Some pixels from a gradient magnitude image and a context range image from an optical image can be used to determine the image contrast metric. A heatmap from the image contrast metrics across part of a wafer can then be used to make a list of targets. Upper and lower confidence values can be applied to rank the available targets.

Optical image contrast metric for optical target search

With the disclosed systems and methods for DRAM and 3D NAND inspection, an image of the wafer is received based on the output for an inspection tool. Geometric measurements of a design of a plurality of memory devices on the wafer are received. A care area with higher inspection sensitivity is determined based on the geometric measurements.

Design-assisted inspection for DRAM and 3D NAND devices

Methods and systems for detecting defects on a specimen are provided. One system includes one or more computer systems and one or more components executed by the one or more computer systems. The component(s) include a deep learning model configured for, for a location on a specimen, generating a gray scale simulated design data image from a high resolution image generated at the location by a high resolution imaging system. The computer system(s) are configured for generating a simulated binary design data image for the location from the gray scale simulated design data image. The computer system(s) are also configured for detecting defects at the location on the specimen by subtracting design data for the location from the simulated binary design data image.

Deep learning based defect detection

Methods and systems for setting up inspection of a specimen are provided. One system includes one or more computer subsystems configured for acquiring a reference image for a specimen and modifying the reference image to fit the reference image to a design grid thereby generating a golden grid image. The one or more computer subsystems are also configured for storing the golden grid image for use in inspection of the specimen. The inspection includes aligning a test image of the specimen generated from output of an inspection subsystem to the golden grid image.

Setting up inspection of a specimen

Methods and systems for selecting defect detection methods for inspection of a specimen are provided. One system includes one or more computer subsystems configured for separating polygons in a care area into initial sub-groups based on a characteristic of the polygons on the specimen and determining a characteristic of noise in output generated by a detector of an inspection subsystem for the polygons in the different initial sub-groups. The computer subsystem(s) are also configured for determining final sub-groups for the polygons by combining any two or more of the different initial sub-groups having substantially the same values of the characteristic of the noise. In addition, the computer subsystem(s) are configured for selecting first and second defect detection methods for application to the output generated by the detector of the inspection subsystem during inspection of the specimen or another specimen.

Selecting defect detection methods for inspection of a specimen

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

Clustering sub-care areas based on noise characteristics

Methods and systems for controlling a process for inspection of a specimen are provided. One system includes one or more computer subsystems configured for determining a statistical characteristic of difference images generated for multiple instances of a care area on a specimen and determining variation in the statistical characteristic compared to a statistical characteristic of difference images generated for multiple instances of the care area on one or more other specimens. In addition, the one or more computer subsystems are configured for determining one or more changes to one or more parameters used for detecting defects in the care area on the specimen based on the variation.

Controlling a process for inspection of a specimen

An optical characterization system and a method of using the same are disclosed. The system comprises a controller configured to be communicatively coupled with one or more detectors configured to receive illumination from a sample and generate image data. One or more processors may be configured to receive images of dies on the sample, calculate dissimilarity values for all combinations of the images, perform a cluster analysis to partition the combinations of the images into two or more clusters, generate a reference image for a cluster of the two or more clusters using two or more of the combinations of the images in the cluster; and detect one or more defects on the sample by comparing a test image in the cluster to the reference image for the cluster.

Unsupervised learning-based reference selection for enhanced defect inspection sensitivity

A method of semiconductor-wafer image alignment is performed at a semiconductor-wafer defect-inspection system. In the method, a semiconductor wafer is loaded into the semiconductor-wafer defect-inspection system. Pre-inspection alignment is performed for the semiconductor wafer. After performing the pre-inspection alignment, a first swath is executed to generate a first image of a first region on the semiconductor wafer. An offset of a target structure in the first image with respect to a known point is determined. Defect identification is performed for the first image, using the offset. After executing the first swath and determining the offset, a second swath is executed to generate a second image of a second region on the semiconductor wafer. While executing the second swath, run-time alignment of the semiconductor wafer is performed using the offset.

Defect-location determination using correction loop for pixel alignment

One or more semiconductor wafers or portions thereof are scanned using a primary optical mode, to identify defects. A plurality of the identified defects, including defects of a first class and defects of a second class, are selected and reviewed using an electron microscope. Based on this review, respective defects of the plurality are classified as defects of either the first class or the second class. The plurality of the identified defects is imaged using a plurality of secondary optical modes. One or more of the secondary optical modes are selected for use in conjunction with the primary optical mode, based on results of the scanning using the primary optical mode and the imaging using the plurality of secondary optical modes. Production semiconductor wafers are scanned for defects using the primary optical mode and the one or more selected secondary optical modes.

Optical-mode selection for multi-mode semiconductor inspection

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Union City, CA, United States of America

Sangbong Park