Kla Corporation

Kla Tencor Corporation

Avner Safrani

Amnon Manassen

Andrew V Hill

Shai Mark

Dimitry Sanko

Etay Lavert

Ron Rudoi

Yoav Grauer

Roel Gronheid

Gilad Laredo

Yossi Simon

Yonatan Vaknin

Adi Pahima

Stephen Hiebert

Shlomo Eisenbach

Maor Arbit

Amir Aizen

Systems and methods for generating volumetric data are disclosed. Such systems and methods may include scanning a sample at a plurality of focal planes located along a depth direction of the sample. Such systems and methods may include generating, via a detector of a metrology sub-system, a plurality of images of a volumetric field of view of the sample at the plurality of focal planes. Such systems and methods may include aggregating the plurality of images to generate volumetric data of the volumetric field of view of the sample. The metrology sub-system may include a Linnik interferometer.

3D profilometry with a Linnik interferometer

An overlay metrology system may include an objective lens, illumination optics to illuminate an overlay target including a first grating with a first pitch on a first sample layer and a second grating with a second pitch on a second sample layer, where the first and second sample layers are separated by a layer separation distance greater than a depth of field of the objective lens. The system may further include collection optics with a radially-varying defocus distribution to compensate for the layer separation distance such that the first and second gratings are simultaneously in focus on the detector.

Single grab overlay measurement of tall targets

A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

System and method for determining target feature focus in image-based overlay metrology

A teaching substrate is loaded into a load port of an equipment front-end module (EFEM) of a fabrication or inspection tool. The EFEM includes a substrate-handling robot. The teaching substrate includes a plurality of sensors and one or more wireless transceivers. The tool includes a plurality of stations. With the teaching substrate in the EFEM, the substrate-handling robot moves along an initial route and sensor data are wirelessly received from the teaching substrate. Based at least in part on the sensor data, a modified route distinct from the initial route is determined. The substrate-handling robot moves along the modified route, handling the teaching substrate. Based at least in part on the sensor data, positions of the plurality of stations are determined.

Automatic teaching of substrate handling for production and process-control tools

A reticle inspection system and a method of handling a reticle in a reticle inspection system are provided. The reticle inspection system includes an active reticle carrier and an inspection tool. The reticle is disposed on the active reticle carrier, and the inspection tool is configured to determine an orientation of the reticle when the active reticle carrier is disposed on a reticle stage. The active reticle carrier is movable between a loading station and the reticle stage and is configured to rotate the reticle to reorient the reticle based on the orientation of the reticle while the active carrier is disposed on the reticle stage.

Active reticle carrier for in situ stage correction

A metrology system may include an imaging sub-system to image a metrology target buried in a sample, where the sample is formed from bonded first and second substrates with a metrology target at the interface. The metrology system may further include an illumination sub-system with an illumination field stop and an illumination pupil, where the illumination field stop includes an aperture to provide that a projected size of the field-stop aperture on a measurement plane corresponding to the metrology target matches a field of view of the detector at the measurement plane, and where the illumination pupil includes a central obscuration to provide oblique illumination of the metrology target with angles greater than a cutoff angle selected to prevent illumination from the illumination source from reflecting off of the bottom surface of the sample and through the field of view of the detector at the measurement plane.

Imaging system for buried metrology targets

A thickness measurement system may include an illumination source, a beam splitter to split illumination from the illumination source into two beams, a translation stage configured to translate a reference sample along a measurement direction, a first interferometer to generate a first interferogram between a first surface of a test sample and a first surface of the reference sample, and a second interferometer to generate a second interferogram between a second surface of the test sample and a second surface of the reference sample. A thickness measurement system may further include a controller to receive interference signals from the first and second interferometers as the translation stage scans the reference sample, and determine a thickness of the test sample based on the thickness of the reference sample and a distance travelled by the translation stage between peaks of envelopes of the interference signals.

Dual-interferometry wafer thickness gauge

A notch detection system receives images of a sample from the imaging detector, in which the sample includes a notched surface and an un-notched surface bounded by a sidewall and further includes at least one notch known notch specifications. The images are generated such that illumination unobstructed by the sample is received by the detector and the sample prevents incident illumination from reaching the detector. The system further determines whether each image includes a notch, identifies the notched surface, and directs a sample positioner to position the sample with the notched surface in a selected direction when a notch is identified in at least one image of the one or more images.

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Misgav, Israel

Avner Safrani