Stratasys, Inc.

Applied Materials Israel Limited

Applied Materials Isreal Ltd

Ore Shtalrid

Boaz Cohen

Blake Zachary Courter

Ran Schleyen

Ohad Shaubi

Irad Peleg

Stephen Demai

Kirill Savchenko

Nur Arad

Ran Badanes

Yossi Koren

Denis Suhanov

Uri Mike

There is provided a method of automated defects' classification, and a system thereof. The method comprises obtaining data informative of a set of defects' physical attributes usable to distinguish between defects of different classes among the plurality of classes; training a first machine learning model to generate, for the given defect, a multi-label output vector informative of values of the physical attributes, thereby generating for the given defect a multi-label descriptor; and using the trained first machine learning model to generate multi-label descriptors of the defects in the specimen. The method can further comprise obtaining data informative of multi-label data sets, each data set being uniquely indicative of a respective class of the plurality of classes and comprising a unique set of values of the physical attributes; and classifying defects in the specimen by matching respectively generated multi-label descriptors of the defects to the multi-label data sets.

Machine learning-based classification of defects in a semiconductor specimen

A method of rendering data for addressable dispensing of material over a working surface, comprises: receiving input image data arranged grid-wise over a plurality of picture-elements; generating an initial map describing a distance field and having a plurality of map-elements each storing distance information corresponding to one picture-element; for each picture-element of at least a portion of the picture-elements: linearly scanning the map independently along a first axis and along a second axis, and updating a respective map-element based on values of map-elements visited during the scan. The distance field can include distances defined perpendicularly to the working surface.

Method and system for rendering data for addressable dispensing

There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

Machine learning-based defect detection of a specimen

A method of additive manufacturing for producing a 2-part mold suitable for use in injection molding, comprises: obtaining a computerized 3D representation of a 2-part mold (3D mold); delimiting one or more regions within the 3D mold; assigning a material or a combination of materials to each delimited region which is different from different from materials used in other delimited regions or outside the delimited regions; and producing the 2-part mold by additive manufacturing; wherein the delimiting comprises carrying out an offset from a surface of the 3D mold which is intended to be in contact with injected material, and wherein the offset is carried out within the solid part of the 3D mold.

3D printing of a structure for injection molding

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Yavne, Israel

Ore Shtalrid