Kla Corporation

Kla Tencor Corporation

Kuljit Virk

Abdurrahman Sezginer

Lawrence Muray

Grace Hsiu-Ling Chen

Hong Xiao

Brian Duffy

Alan D Brodie

John Gerling

Laurent Karsenti

Qiang Q Zhang

Bradley Ries

Eric Vella

Martin Gruebele

Oriel Ben Shmuel

Yakir Gorski

Emanuel Garbin

Ron Dekel

Ruslan Berdichevsky

Asaf J Elron

Sasha Smekhov

Vera Andreeva

Nick Petrone

Oren Dovrat

Shlomi Fenster

An inspection is disclosed. The system may include an illumination source configured to illuminate a sample. The sample may include a multi-layer stack including a plurality of layers formed of a first material and at least a second material. The first material may include a light transmissive material and the second material may include light reflective material. A top layer within the stack may include absorptive markers configured to selectively bind to the top layer. The absorptive markers may be configured block light transmission through layers positioned below the top layer. The top layer may include photoluminescent markers configured to selectively bind to the light reflective material to enhance a feature of interest on the sample. The system may include detectors configured to detect photoluminescent emission emitted by the photoluminescent markers and optical elements may be configured to direct the photoluminescent emission to the detectors.

System and method for reducing sample noise using selective markers

Methods and systems for detecting defects in images of a specimen are provided. One system includes a computer subsystem configured for training an ensemble of deep learning models by altering one or more parameters of the ensemble until a pseudo-loss function determined based on output of the ensemble is approximately equal to but not greater than 0.5. The computer subsystem is also configured for detecting defects in runtime specimen images by inputting the runtime specimen images into the trained ensemble and generating runtime labels for the runtime specimen images indicating if a defect has been detected in the runtime specimen images based on outputs of the deep learning models in the trained ensemble.

Ensemble of deep learning models for defect review in high volume manufacturing

A context-based inspection system is disclosed. The system may include an optical imaging sub-system. The system may further include one or more controllers communicatively coupled to the optical imaging system. The one or more controllers may be configured to: receive one or more reference images; receive one or more test images of a sample; generate one or more probabilistic context maps during inspection runtime using an unsupervised classifier; provide the generated one or more probabilistic context maps to a supervised classifier during the inspection runtime; and apply the supervised classifier to the received one or more test images to identify one or more DOIs on the sample.

Context-based defect inspection

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

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

Growing community of inventors

Fremont, CA, United States of America

Kuljit Virk