Kla Tencor Corporation

Kla-tencor Technologies Corporation

Kla Corporation

Hedong Yang

Ajay Gupta

Amir Azordegan

Sankar Venkataraman

Chien-Huei Adam Chen

Mohan Mahadevan

Michael John Van Riet

Thanh Huy Ha

Jianwei Wang

Christopher Sears

Laurent Karsenti

Matthew Lent

Gian Francesco Lorusso

Li He

Peter Francis White

Cho Huak Teh

Huajun Ying

Christopher Maher

Huina Xu

Yair Carmon

Gongyuan Qu

Ichiro Honjo

Noga Bullkich

Udy Danino

Hai Jiang

Adam Chien-Huei Chen

Bo Xiong

Kay Wang

Michael Van Riet

Parameters from an inspection image representing mechanical vibrations and electromagnetic interference can be determined. An X-direction vibration spectrum can be determined based on the X-direction offsets. A Y-direction vibration spectrum can be determined based on the Y-direction offsets. The determinations can be based on a swath image of a workpiece, such as a semiconductor wafer or reticle.

Noise diagnostics for an electron beam inspection system with swathing

Objective lens alignment of a scanning electron microscope review tool with fewer image acquisitions can be obtained using the disclosed techniques and systems. Two different X-Y voltage pairs for the scanning electron microscope can be determined based on images. A second image based on the first X-Y voltage pair can be used to determine a second X-Y voltage pair. The X-Y voltage pairs can be applied at the Q4 lens or other optical components of the scanning electron microscope.

Scanning electron microscope objective lens calibration using X-Y voltages iteratively determined from images obtained using said voltages

Methods and systems for detecting and classifying defects on a specimen are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for detecting defects on a specimen and classifying the defects detected on the specimen. The neural network includes a first portion configured for determining features of images of the specimen generated by an imaging subsystem. The neural network also includes a second portion configured for detecting defects on the specimen based on the determined features of the images and classifying the defects detected on the specimen based on the determined features of the images.

Unified neural network for defect detection and classification

Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

Contour based defect detection

One embodiment relates to a method of inspecting an array of cells on a substrate. A reference image is generated using a cell image that was previously determined to be defect free. A reference contour image which includes contours of the reference image is also generated. The reference contour image is used to detect defects in the array of cells on the substrate. Another embodiment relates to a system for detecting defects in an array on a substrate. Other embodiments, aspects and features are also disclosed.

Contour-based array inspection of patterned defects

One embodiment relates to a method of inspecting a site location on a target substrate. Contours are obtained, the contours having been generated from a reference image using a design clip. A target image of the site location is acquired. The contours are aligned to the target image, and contrast values are computed for pixels on the contours. A threshold is applied to the contrast values to determine contour-based defect blobs. Another embodiment relates to a method of generating contours for use in inspecting a site location for defects. Other embodiments, aspects and features are also disclosed.

Contour-based defect detection using an inspection apparatus

One embodiment relates to a method of classifying a defect on a substrate surface. The method includes scanning a primary electron beam over a target region of the substrate surface causing secondary electrons to be emitted therefrom, wherein the target region includes the defect. The secondary electrons are detected from the target region using a plurality of at least two off-axis sensors so as to generate a plurality of image frames of the target region, each image frame of the target region including data from a different off-axis sensor. The plurality of image data frames are processed to generate a surface height map of the target region, and surface height attributes are determined for the defect. The surface height attributes for the defect are input into a defect classifier. Other embodiments, aspects and features are also disclosed.

Methods and apparatus for classification of defects using surface height attributes

One embodiment relates to a method of automated microalignment using off-axis beam tilting. Image data is collected from a region of interest on a substrate at multiple beam tilts. Potential edges of a feature to be identified in the region are determined, and computational analysis of edge-related data is performed to positively identify the feature(s). Another embodiment relates to a method of automated detection of undercut on a feature using off-axis beam tilting. For each beam tilt, a determination is made of difference data between the edge measurement of one side and the edge measurement of the other side. An undercut on the feature is detected from the difference data. Other embodiments are also disclosed.

Automated feature analysis with off-axis tilting

One embodiment disclosed relates to an automated process for focusing a charged-particle beam in an apparatus onto an area of a substrate. A focusing parameter of the apparatus is set to a value, and intensity data is acquired from the area. The foregoing setting and acquiring steps are repeated for a range of values for the focusing parameter. A focusing sharpness measure is computed for each value of the focusing parameter based on noise in the acquired intensity data, and an in-focus value is determined for the focusing parameter based on the computed focusing sharpness measures. The focusing parameter of the apparatus may be, for example, an objective lens current, or a substrate bias voltage. The computation of the noise-based focusing sharpness measure may involve generating shifted or interleaved signals and calculating correlations between these signals. The focusing may be advantageously performed on an area lacking substantial edge information.

Non-feature-dependent focusing

One embodiment disclosed relates to a scanning electron beam apparatus. The apparatus includes an electron beam column, a scanning system, and a detection system. Circuitry in the apparatus is configured to store detected pixel data from each scan into one of the multiple frame buffers. A multi-frame data processor is configured to analyze the pixel data available in the multiple frame buffers. Another embodiment disclosed relates to a scanning electron beam apparatus having a data processor is configured to process the image data with a filter function having a filter strength, store results of the processing, and repeat the processing and the storing using various filter strengths. The results of the processing may comprise a critical dimension measurement at each filter strength.

Scanning electron beam apparatus and methods of processing data from same

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Santa Clara, CA, United States of America

Hedong Yang