Schlumberger Technology Corporation

Cadence Design Systems, Inc.

Kla Tencor Corporation

Tokyo Electron Limited

Therma-wave, Inc.

Invarium, Inc.

Kla Corporation

Other

Schlumberger-doll Research

Abdurrahman Sezginer

Adam Eales Norton

Kenneth C Johnson

Robert L Kleinberg

Fred E Stanke

Rui-Fang Shi

Hsu-Ting Huang

Douglas D Griffin, Ii

Franz X Zach

Bayram Yenikaya

Milind Ganesh Weling

Roy Varada Prasad

Boqin Q Sun

Masafumi Fukuhara

Judy Huckabay

Gokhan Percin

Rodney C Smedt

Gang Pan

Yalin Xiong

Holger Tuitje

Qiang Q Zhang

Bing Li

Bruno Jorion

Hawren Fang

Ali K Toufaily

Michiel Victor Paul Kruger

Jesus Orsely Carrero

John Fielden

Dipankar Pramanik

Reza Taherian

Bo Wu

Nicholas J Heaton

Sangbong Park

Shu-Kong Chang

Luis Eduardo DePavia

Chi-Song Horng

James Stephen Hall

Xiaochun Li

Weston L Sousa

Yanwei Liu

Eric Vella

Kuljit Virk

Devendra Joshi

Mohammad Mehdi Daneshpanah

Patrick LoPresti

Joe Blecher

Alex Pokrovskiy

Luis E Depavia

Justin Ghan

Lisheng Gao

Bjorn Brauer

Jacques R Tabanou

Robert Freedman

Hong Xiao

John Josef Hench

Reinhart Ciglenec

Alan D Brodie

Krishnamurthy Ganesan

Hong Chen

Andrew J Hayman

Chanh Cao Minh

Charles Flaum

Robert M Danen

Lawrence Muray

Grace Hsiu-Ling Chen

Philip S Cheung

Grace H Chen

Pabitra N Sen

Michael S Bakeman

Junqing Huang

David Cooke Noice

Vassilios Constantinos Gerousis

John Gerling

Weng C Chew

Robert Shinagawa

Ge Cong

Steven F Crary

Gordon Rouse

Jason Sweis

Vladimir L'vovich Druskin

Christian Straley

M Reza Taherian

Dennis J Pittman

Ram S Ramanujam

Pavan Kumar

Xuelong Cao

Anwei Liu

Gökhan Perçin

Tatung Chow

Wolf Staud

Paul A Fornari

Nick Petrone

Michelle Zimmerman

Balaji Ganapathy

Dylan H Davies

Mohammad Reza Taherian

Douglas Griffin

Lawrence L Latour

Martin Erich Poitzsch

Dmitri Starodub

Context attributes for optical imaging of a patterned layer of a semiconductor die are calculated. Calculating the context attributes includes calculating convolutions of a pattern of the patterned layer with respective kernels of a plurality of kernels, wherein the plurality of kernels is orthogonal. Defects on the semiconductor die are found in accordance with the context attributes.

Finding semiconductor defects using convolutional context attributes

Disclosed are methods and apparatus for inspecting a photolithographic reticle. A plurality of reference far field images are simulated by inputting a plurality of reference near field images into a physics-based model, and the plurality of reference near field images are generated by a trained deep learning model from a test portion of the design database that was used to fabricate a test area of a test reticle. The test area of a test reticle, which was fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing the plurality of reference far field reticle images simulated by the physic-based model to a plurality of test images acquired by the inspection system from the test area of the test reticle.

Inspection of reticles using machine learning

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

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 system is disclosed. In one embodiment, the system includes a scanning electron microscopy sub-system including an electron source configured to generate an electron beam and an electron-optical assembly including one or more electron-optical elements configured to direct the electron beam to the specimen. In another embodiment, the system includes one or more grounding paths coupled to the specimen, the one or more grounding paths configured to generate one or more transmission signals based on one or more received electron beam-induced transmission currents. In another embodiment, the system includes a controller configured to: generate control signals configured to cause the scanning electron microscopy sub-system to scan the portion of the electron beam across a portion of the specimen; receive the transmission signals via the one or more grounding paths; and generate transmission current images based on the transmission signals.

Defect inspection and review using transmissive current image of charged particle beam system

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

Disclosed are methods and apparatus for inspecting a photolithographic reticle. A near field reticle image is generated via a deep learning process based on a reticle database image produced from a design database, and a far field reticle image is simulated at an image plane of an inspection system via a physics-based process based on the near field reticle image. The deep learning process includes training a deep learning model based on minimizing differences between the far field reticle images and a plurality of corresponding training reticle images acquired by imaging a training reticle fabricated from the design database, and such training reticle images are selected for pattern variety and are defect-free. A test area of a test reticle, which is fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing a plurality of references images from a reference far field reticle image to a plurality of test images acquired by the inspection system from the test reticle. The reference far field reticle image is simulated based on a reference near field reticle image that is generated by the trained deep learning model.

In one embodiment, disclosed are apparatus, methods, and targets for determining a phase shift of a photomask having a phase-shift target. An inspection or metrology system is used to direct an incident beam towards the target and then detect a plurality of intensity measurements that are transmitted through the target in response to the incident beam. A phase shift value for the target may then be determined based on the intensity measurements.

Apparatus and methods for measuring phase and amplitude of light through a layer

Disclosed is a system that includes a light source for generating an illumination beam and an illumination lens system for directing the illumination beam towards a sample. The system further includes a collection lens system for directing towards a detector output light from the sample in response to the illumination beam and a detector for receiving the output light from the sample. The collection lens system includes a fixed-design compensator plate having individually selectable filters with different configurations for correcting system aberration of the system under different operating conditions. The system also includes a controller operable for: (i) generating and directing the illumination beam towards the sample, (ii) selecting operating conditions and a filter for correcting the system aberration under such selected operating conditions, (iii) generating an image based on the output light, and (iv) determining whether the sample passes inspection or characterizing such sample based on the image.

Arbitrary wavefront compensator for deep ultraviolet (DUV) optical imaging system

Block-to-block reticle inspection includes acquiring a swath image of a portion of a reticle with a reticle inspection sub-system, identifying a first occurrence of a block in the swatch image and at least a second occurrence of the block in the swath image substantially similar to the first occurrence of the block and determining at least one of a location, one or more geometrical characteristics of the block and a spatial offset between the first occurrence of the block and the at least a second occurrence of the block.

Block-to-block reticle inspection

Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire a plurality of images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field is recovered for each of the pattern areas of the test reticle based on the acquired images from each pattern area of the test reticle. The recovered reticle near field is then used to determine whether the test reticle or another reticle will likely result in unstable wafer pattern or a defective wafer.

Apparatus and methods for inspecting reticles

Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field for each of the pattern areas of the test reticle is recovered based on the acquired images from each pattern area of the test reticle. A lithography model is applied to the reticle near field for the test reticle to simulate a plurality of test wafer images, and the simulated test wafer images are analyzed to determine whether the test reticle will likely result in an unstable or defective wafer.

Apparatus and methods for predicting wafer-level defect printability

Disclosed are methods and apparatus for facilitating an inspection of a sample using an inspection tool. An inspection tool is used to obtain an image or signal from an EUV reticle that specifies an intensity variation across the EUV reticle, and this intensity variation is converted to a CD variation that removes a flare correction CD variation so as to generate a critical dimension uniformity (CDU) map without the flare correction CD variation. This removed flare correction CD variation originates from design data for fabricating the EUV reticle, and such flare correction CD variation is generally designed to compensate for flare differences that are present across a field of view (FOV) of a photolithography tool during a photolithography process. The CDU map is stored in one or more memory devices and/or displayed on a display device, for example, of the inspection tool or a photolithography system.

Critical dimension uniformity monitoring for extreme ultra-violet reticles

A reticle is inspected with an imaging system to obtain a measured image of a structure on the reticle, and the structure has an unknown critical dimension (CD). Using a model, a calculated image is generated using a design database that describes a pattern used to form the structure on the reticle. The model generates the calculated image based on: optical properties of reticle materials of the structure, a computational model of the imaging system, and an adjustable CD. A norm of a difference between the measured and calculated images is minimized by adjusting the adjustable CD and iteratively repeating the operation of generating a calculated image so as to obtain a final CD for the unknown CD of the structure. Minimizing the norm of the difference is performed simultaneously with respect to the adjustable CD and one or more uncertain parameters of the imaging system.

Techniques and systems for model-based critical dimension measurements

Critical dimension uniformity monitoring for extreme ultraviolet reticles

Growing community of inventors

Monte Sereno, CA, United States of America

Abdurrahman Sezginer