Kla Tencor Corporation

Kla Corporation

Kla-tencor Technologies Corporation

Richard Wallingford

Jason Z Lin

Eliezer Rosengaus

Lawrence Robert Miller

Mark J Roulo

Krishnamurthy Bhaskar

John S Taylor

Paul T Russell

Kishore Bubna

Lisheng Gao

Tao Luo

Bjorn Brauer

Jason Kirkwood

James A Smith

Lu Chen

Mohan Mahadevan

Junqing (Jenny) Huang

Kris Bhaskar

Yong Zhang

Kai Cao

Xiumei Liu

Stephanie Chen

Matthew Giusti

Mark Armstrong McCord

Brian Duffy

Allen Park

Ellis Chang

Robert M Danen

Sangbong Park

Jing Zhang

Santosh Bhattacharyya

Grace H Chen

Chien-Huei (Adam) Chen

Pavel Kolchin

Songnian Rong

Ardis Liang

Markus B Huber

Chetana Bhaskar

Hubert Altendorfer

Brooke Bruguier

Kais Maayah

Ashok V Kulkarni

Scott Allen Young

Hucheng Lee

Subramanian Balakrishnan

Grace Hsiu-Ling Chen

Ajay Gupta

Xiaochun Li

Junqing Huang

Ge Cong

Jeremy Nesbitt

Cecelia Anne Campochiaro

Bryant Mantiply

Yujie Dong

Nurmohammed Patwary

Mike Van Riet

Vijay Ramachandran

Kedar Grama

Vladimir Tumakov

Michael Daino

Kaustubh (Kaust) Namjoshi

Michael Cook

An auto-focusing system is disclosed. The system includes an illumination source. The system includes an aperture. The system includes a projection mask. The system includes a detector assembly. The system includes a relay system, the relay system being configured to optically couple illumination transmitted through the projection mask to an imaging system. The relay system also being configured to project one or more patterns from the projection mask onto a specimen and transmit an image of the projection mask from the specimen to the detector assembly. The system includes a controller including one or more processors configured to execute a set of program instructions. The program instructions being configured to cause the one or more processors to: receive one or more images of the projection mask from the detector assembly and determine quality of the one or more images of the projection mask.

Automated focusing system for tracking specimen surface with a configurable focus offset

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a semantic segmentation model configured for assigning labels to each of multiple pixels in an image responsive to what is represented in each of the multiple pixels. The image is an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the assigned labels and without a reference image for the specimen.

Semantic image segmentation for semiconductor-based applications

Methods and systems for deep learning alignment for semiconductor applications are provided. One method includes transforming first actual information for an alignment target on a specimen from either design data to a specimen image or a specimen image to design data by inputting the first actual information into a deep generative model such as a GAN. The method also includes aligning the transformed first actual information to second actual information for the alignment target, which has the same information type as the transformed first actual information. The method further includes determining an offset between the transformed first actual information and the second actual information based on results of the aligning and storing the determined offset as an align-to-design offset for use in a process performed on the specimen.

Deep generative model-based alignment for semiconductor applications

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

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 focus settings for use in a specimen scan are provided. One method includes generating a focus map defined as values of best focus as a function of position on a specimen using output generated in one or more pre-focus swaths scanned on the specimen by an output acquisition subsystem configured to direct energy to a specimen, to detect energy from the specimen, and to generate output responsive to the detected energy. The method also includes interpolating the focus map to generate focus settings for a scan performed on the specimen during a process and storing information for the generated focus settings for use in the scan performed on the specimen during the process.

Determining focus settings for specimen scans

Mixed-mode includes receiving inspection results including one or more images of a selected region of the wafer, the one or more images include one or more wafer die including a set of repeating blocks, the set of repeating blocks a set of repeating cells. In addition, mixed-mode inspection includes adjusting a pixel size of the one or more images to map each cell, block and die to an integer number of pixels. Further, mixed-mode inspection includes comparing a first wafer die to a second wafer die to identify an occurrence of one or more defects in the first or second wafer die, comparing a first block to a second block to identify an occurrence of one or more defects in the first or second blocks and comparing a first cell to a second cell to identify an occurrence of one or more defects in the first or second cells.

Method and system for mixed mode wafer inspection

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

Methods and systems for performing active learning for defect classifiers are provided. One system includes one or more computer subsystems configured for performing active learning for training a defect classifier. The active learning includes applying an acquisition function to data points for the specimen. The acquisition function selects one or more of the data points based on uncertainty estimations associated with the data points. The active learning also includes acquiring labels for the selected one or more data points and generating a set of labeled data that includes the selected one or more data points and the acquired labels. The computer subsystem(s) are also configured for training the defect classifier using the set of labeled data. The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem.

Active learning for defect classifier training

Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.

Systems and methods for detecting defects on a wafer

An inspection system includes an illumination sub-system, a collection sub-system, and a controller. The illumination sub-system includes an illumination source configured to generate a beam of illumination and a set of illumination optics to direct the beam of illumination to a sample. The collection sub-system includes a set of collection optics to collect illumination emanating from the sample and a detector configured to receive the collected illumination from the sample. The controller is configured to acquire a test image of the sample, reconstruct the test image to enhance the resolution of the test image, and detect one or more defects on the sample based on the reconstructed test image.

Systems and methods for defect detection using image reconstruction

Systems and methods for providing an augmented input data to a convolutional neural network (CNN) are disclosed. Wafer images are received at a processor. The wafer image is divided into a plurality of references images each associated with a die in the wafer image. Test images are received. A plurality of difference images are created by differences the test images with the reference images. The reference images and difference images are assembled into the augmented input data for the CNN and provided to the CNN.

Data augmentation for convolutional neural network-based defect inspection

Disclosed are methods and apparatus for optimizing a mode of an inspection tool. A first image or signal for each of a plurality of first apertures of the inspection tool is obtained, and each first image or signal pertains to a defect area. For each of a plurality of combinations of the first apertures and their first images or signals, a composite image or signal is obtained. Each composite image or signal is analyzed to determine an optimum one of the combinations of the first apertures based on a defect detection characteristic of each composite image. In one aspect, determining an optimum one of the combinations of the first apertures includes selecting a set of one or more individual apertures that result in the highest signal to noise ratio for the defect area, and the method includes setting the optimum combination of the first apertures on the inspection tool and inspecting a sample using such optimum combination of the first apertures.

Apparatus and methods for finding a best aperture and mode to enhance defect detection

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San Jose, CA, United States of America

Richard Wallingford