Kla Corporation

Kla Tencor Corporation

Xiaochun Li

Sangbong Park

Bjorn Brauer

Hucheng Lee

Nurmohammed Patwary

Lisheng Gao

Junqing Huang

James A Smith

Abdurrahman Sezginer

Kenong Wu

Hong Chen

Keith B Wells

Tao Luo

Huan Jin

Siqing Nie

Wei Si

Richard Wallingford

Eugene Shifrin

Yalin Xiong

Santosh Bhattacharyya

Grace H Chen

Jin Qian

Ge Cong

Markus B Huber

Hawren Fang

Bryant Mantiply

Li Yu

Ruifang Shi

Pavan Kumar

Ramon Ynzunza

Michael Cook

Qing Luo

Vladimir Tumakov

Chunwei Song

Zhuang Liu

Weifeng Zhou

Heonju Shin

Manikandan Mariyappan

Prashant Verma

Jusang Maeng

Zhifeng Huang

Neil Troy

Tarunark Singh

David Dowling

Leon Yu

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.

Systems and methods for detecting defects on a reticle are provided. One system is configured for generating different stacked difference images for multiple instances of first patterned areas in different rows on a wafer based on images generated for the first patterned areas in the different rows. The system is also configured for performing double detection based on the different stacked difference images. The system then identifies defects on the reticle based on the defects detected by the double detection. As described further herein, the systems and methods detect defects from multiple reticle rows printed on a wafer, which can reduce noise and enable detection of substantially small repeater defects. The embodiments are particularly useful for high sensitivity repeater defect detection for extreme ultraviolet (EUV) reticles and multi-die reticles (MDR).

Print check repeater defect detection

A rendered image is generated from a semiconductor device design file. The rendered image is segmented based on a grey level of the rendered image. Care areas are determined based on the segmenting. Defect inspection is performed in the care areas. This process can be performed on a wafer inspection tool that uses photon optics or electron beam optics.

Segmentation of design care areas with a rendered design image

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 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

A sample characterization system is disclosed. In embodiments, the sample characterization system includes a controller communicatively coupled to an inspection sub-system, the controller including one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to: acquire one or more target image frames of a sample; generate a target tensor with the one or more acquired target image frames; perform a first set of one or more decomposition processes on the target tensor to generate one or more reference tensors including one or more reference image frames; identify one or more differences between the one or more target image frames and the one or more reference image frames; and determine one or more characteristics of the sample based on the one or more identified differences.

System and method for inspection using tensor decomposition and singular value decomposition

Methods and systems for determining one or more alignment parameters for use in a process performed on a specimen are provided. One method includes determining measures of similarity between images generated by an imaging system for corresponding locations in each of two or more pairs of dies on a specimen and performing cluster analysis based on the determined measures of similarity to identify the images that are most similar to each other and to assign different subsets of the images that are most similar to each other to different die clusters, respectively. The method also includes separately determining one or more alignment parameters for two or more of the different die clusters. The one or more alignment parameters are used for aligning images generated by the imaging system for the specimen or another specimen to a common reference.

Image alignment setup for specimens with intra- and inter-specimen variations using unsupervised learning and adaptive database generation methods

Systems and methods for detecting defects on a reticle are provided. One system includes computer subsystem(s) configured for performing at least one repeater defect detection step in front-end processing during an inspection process performed on a wafer having features printed in a lithography process using a reticle. The at least one repeater defect detection step performed in the front-end processing includes identifying any defects detected at corresponding locations in two or more test images by double detection and any defects detected by stacked defect detection as first repeater defect candidates. One or more additional repeater defect detections may be performed on the first repeater defect candidates to generate final repeater defect candidates and identify defects on the reticle from the final repeater defect candidates.

Systems and methods increase the signal to noise ratio of optical inspection of wafers to obtain higher inspection sensitivity. The computed reference image can minimize a norm of the difference of the test image and the computed reference image. A difference image between the test image and a computed reference image is determined. The computed reference image includes a linear combination of a second set of images.

Wafer inspection using difference images

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

Methods and systems for detecting defects on a wafer are provided. One system includes one or more computer subsystems configured for generating a rendered image based on information for a design printed on the wafer. The rendered image is a simulation of an image generated by the optical inspection subsystem for the design printed on the wafer. The computer subsystem(s) are also configured for comparing the rendered image to an optical image of the wafer generated by the optical inspection subsystem. The design is printed on the wafer using a reticle. In addition, the computer subsystem(s) are configured for detecting defects on the wafer based on results of the comparing.

Optical die to database inspection

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San Jose, CA, United States of America

Xiaochun Li