Kla Corporation

Other

Kla Tencor Corporation

Hunan Ehome Health Technology Company Limited

Ge Cong

Abdurrahman Sezginer

Richard Wallingford

Sangbong Park

Lijun Wu

Boshi Huang

Bjorn Brauer

Hong Chen

Grace Hsiu-Ling Chen

Yalin Xiong

Santosh Bhattacharyya

Xiaochun Li

Lih-Huah Yiin

Kuljit Virk

Hawren Fang

Wei Zhao

Patrick McBride

Bosheng Zhang

Richard Cong

Vinayak Dattatreya Phalke

Sanbong Park

Ramaprasad Kulkarni

Vahid Noormofidi

Xuzhao Liu

Leon Yu

Amrish Patel

A system includes a processing unit communicatively coupled to a detector array of an optical wafer characterization system. The processing unit is configured to perform one or more steps of a method or process including the steps of acquiring one or more target images of a target location on a wafer from the detector array, applying a de-noising filter to at least the one or more target images, determining one or more difference images from one or more reference images and the one or more target images, and up-sampling the one or more difference images to generate one or more up-sampled images. One or more wafer defects are detectable in the one or more difference images or the up-sampled images.

System and method for optical wafer characterization with image up-sampling

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 aligning images of a specimen are provided. One method includes reducing noise in a test image generated for a specimen by an imaging subsystem thereby generating a denoised test image. The method also includes detecting one or more patterned features in the denoised test image extending in at least a horizontal or vertical direction. In addition, the method includes designating an area of the denoised test image in which the detected one or more patterned features are located as a region of interest in the denoised test image. The method further includes aligning the denoised test image to a reference image for the specimen using only the region of interest in the denoised test image and a corresponding area in the reference image.

Image alignment for noisy images

A system for characterizing a specimen is disclosed. In one embodiment, the system includes a controller configured to: receive training images of one or more defects of the specimen; generate a machine learning classifier based on the training images; receive product images of one or more defects of a specimen; determine one or more defect type classifications of one or more defects with the machine learning classifier; filter the product images with one or more smoothing filters; perform binarization processes to generate binarized product images; perform morphological image processing operations on the binarized product images; determine one or more algorithm-estimated defect sizes of the one or more defects based on the binarized product images; and determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications.

System and method for determining type and size of defects on blank reticles

A rendered image is aligned with a scanning electron microscope (SEM) image to produce an aligned rendered image. A reference image is aligned with the SEM image to produce an aligned reference image. A threshold probability map also is generated. Dynamic compensation of the SEM image and aligned reference image can produce a corrected SEM image and corrected reference image. A thresholded defect map can be generated and the defects of the thresholded probability map and the signal-to-noise-ratio defects of the thresholded defect map are filtered using a broadband-plasma-based property to produce defect-of-interest clusters.

BBP assisted defect detection flow for SEM images

Techniques for fast and accurate measuring test strip intensities are disclosed herein. A cassette attachment device for detecting intensity of a test strip cassette includes a strip chamber configured to accommodate at least a portion of a test strip, a light source configured to provide illumination to the test strip cassette via indirect lighting, an attaching mechanism configured to attach the device to a mobile device, and a camera window configured to transmit light signal reflected from the test strip cassette such that the mobile device can capture an image of the test strip cassette illuminated by the light source.

Cassette attachment device for detecting intensity of test strip cassette

Techniques for fast and accurate measuring test strip intensities are disclosed herein. A method for measuring a test strip intensity comprising steps of obtaining an image of a sample line in a test strip and a plurality of reference lines, wherein the reference lines have known intensities; determining grayscale values of the sample line and the reference lines from the image; constructing a standard curve based on the grayscale values versus the known intensities of the reference lines; and determining the intensity of the sample line by fitting the grayscale value of the sample line on the standard curve.

Method for fast test strip intensity recognition

Methods and apparatus relating to the inspection of photomasks are described. In an embodiment, detection of thin line or sub-resolution assist features may be used for selective sensitivity during photomask inspection. Other embodiments are also described.

Detection of thin line for selective sensitivity during reticle inspection

Growing community of inventors

Pleasanton, CA, United States of America

Ge Cong