Camtek Ltd.

Daniel Buzaglo

Menachem Regensburger

Roni Flieswasser

Moti Yanuka

Evgenia Chernomaz

Yossi Pinhassi

A method for automatic defect classification, the method may include (i) acquiring, by a first camera, at least one first image of at least one area of an object; (ii) processing the at least one first image to detect a group of suspected defects within the at least one area; (iii) performing a first classification process for initially classifying the group of suspected defects; (iii) determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification; (iv) when determining that the first subgroup of the suspected defects requires additional information from the second camera then: (a) acquiring second images, by the second camera, of the first subgroup of the suspected defects; and (b) performing a second classification process for classifying the first subgroup of suspected defects.

Automatic defect classification

There may be provided a method for determining three dimensional (3D) defect information, the method may include performing a two-dimensional (2D) inspection of an area of a wafer to generate 2D defect information related to defects of the area of the wafer; estimating 3D defect information regarding the defects of the area of the wafer, wherein the estimating is based on the 2D defect information related to defects of the area of the wafer, and a mapping between 2D defect information and 3D defect information, wherein the mapping is generated using a supervised deep learning machine process.

Defect detection

A method for storing and displaying a layers' information of a layer-made object, comprising: (a) forming a layer-image of each layer of said object layers, wherein said layer image is a real perpendicular view image of said layer; (b) aligning each of said layer-images in a common two-dimensions axis, wherein said layer-image location in said common axis is in correspondence with the location of said layer in said object when is assembled; (c) forming two dimension matrixes, a two dimension matrix for each layer-image, wherein each matrix element has three variables (x,y,j)&#x2014;when 'x' and 'y' are representing a point location on said layer-image according to said common axis and &#x201c;j&#x201d; represents an appearance of material view in said point of said layer-image; and (d) adding said two dimension matrixes into a three-dimensions matrix, wherein each matrix element has four variables (x,y,z,j)&#x2014;when &#x201c;x&#x201d; and &#x201c;y&#x201d; are representing said point location, &#x201c;z&#x201d; represents layer identification and &#x201c;j&#x201d; represents said appearance of material.

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Migdal Haemeq, Israel

Daniel Buzaglo