Abstract: A method that may include performing first measurements of the height differences between the bumps and the corresponding areas, by illuminating the bumps and the corresponding areas with first radiation; wherein the first measurements are subjected to first measurement errors resulting from a virtual penetration of the first illumination into the layer; wherein each bump has a corresponding area that is proximate to the bump; preforming second measurements of thickness of the layer at the corresponding areas; wherein at least some of the first measurements are executed in parallel to an executing of at least some of the second measurements; determining first measurement errors, based on the second measurements; and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

Abstract: A method and system for measuring bump height differences. The method comprises performing first measurements of height differences between bumps and corresponding areas of an upper surface of a layer, by illuminating the bumps and the corresponding areas with a first radiation. The method includes preforming second measurements of height differences between a subgroup of the bumps and a subgroup of the corresponding areas, by illuminating the subgroup of the bumps and the subgroup of the corresponding areas with a second radiation. The method further comprises determining first measurement errors, based on the first measurements and the second measurements, and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

Abstract: A method for measuring height differences between tops of multiple bumps of an upper surface of a layer, the method may include performing first measurements of the height differences between the bumps and the corresponding areas, by illuminating the bumps and the corresponding areas with first radiation; wherein the first measurements are subjected to first measurement errors; and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

Abstract: A self-referencing interferometric microscope uses near-common-path, common component beam separators to produce two beams that illuminate the sample at different angles. Two return beams collected from the sample interfere at the image plane to produce an interferometric image of the sample comprising fringes across the image. The image can be processed to determine the topography of the sample.

Abstract: A wafer inspection system employing reflected bright-field microscopy can be adapted with polarizing optics and a mirror to detect polarization-altering defects (such as micropipes) in semiconductor wafers. The polarization-altering defects can be located within the bulk of the semiconductor wafer and can be imaged as bright features on a darker background. The system can also be used for conventional bright-field inspection of non-polarization-altering defects such as contaminants and inclusions.

Abstract: An illumination module may include a laser diode array configured to emit laser radiation; a phosphor illumination unit that is configured to emit phosphor radiation following an exposure to the laser radiation; a multiple-angle illumination unit; and intermediate optics that is configured to convey the phosphor radiation to the multiple-angle illumination unit. The multiple-angle illumination unit is configured to receive the phosphor radiation and to dark field illuminate a region of a sample wafer from multiple angles during inspection of the wafer.

Abstract: A wafer inspection system employing reflected bright-field microscopy can be adapted with polarizing optics and a mirror to detect polarization-altering defects (such as micropipes) in semiconductor wafers. The polarization-altering defects can be located within the bulk of the semiconductor wafer and can be imaged as bright features on a darker background. The system can also be used for conventional bright-field inspection of non-polarization-altering defects such as contaminants and inclusions.

Abstract: The present disclosure provides method and system 100 for classifying defects in wafer using wafer defect images, based on deep learning network. Embodiments herein uses synergy between several modalities of the wafer defect images for the classification decision. Further, by adding a mixture of modalities, information may be obtained from different sources such as color image, ICI, the black and white image, to classify the defect image. In addition to mixture of modalities, a reference image may be used for each modality. The reference image of each modality image is provided to deep learning models to concentrate on the defect itself and not on the related underlying lithography of the defect image. Further, the reference image may be provided to the training process of the deep learning models that may significantly reduce the number of labelled images and the training epochs required for convergence of the deep learning model.

Abstract: A method that may include performing first measurements of the height differences between the bumps and the corresponding areas, by illuminating the bumps and the corresponding areas with first radiation; wherein the first measurements are subjected to first measurement errors resulting from a virtual penetration of the first illumination into the layer; wherein each bump has a corresponding area that is proximate to the bump; preforming second measurements of thickness of the layer at the corresponding areas; wherein at least some of the first measurements are executed in parallel to an executing of at least some of the second measurements; determining first measurement errors, based on the second measurements; and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

Abstract: An illumination module may include a laser diode array configured to emit laser radiation; a phosphor illumination unit that is configured to emit phosphor radiation following an exposure to the laser radiation; a multiple-angle illumination unit; and intermediate optics that is configured to convey the phosphor radiation to the multiple-angle illumination unit. The multiple-angle illumination unit is configured to receive the phosphor radiation and to dark field illuminate a region of a sample wafer from multiple angles during inspection of the wafer.

Abstract: A method for measuring height differences between tops of multiple bumps of an upper surface of a layer, the method may include performing first measurements of the height differences between the bumps and the corresponding areas, by illuminating the bumps and the corresponding areas with first radiation; wherein the first measurements are subjected to first measurement errors; and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

Abstract: There may be provided a method for inspecting a top redistribution layer conductors of an object. The top redistribution layer (RDL) is positioned above at least one lower RDL and above at least one other dielectric layer. The method may include (i) illuminating the object with radiation, the at least one lower dielectric layer significantly absorbs the radiation; (ii) generating, by a detector, detection signals that represent radiation reflected from the object, and (iii) processing, by a processor, the detection signal to provide information about the top RDL. The processing may include distinguishing detection signals related to the top RDL from detection signals related to the at least one lower RDL.

Abstract: A method for automatic defect classification, the method may include acquiring, by a first camera, at least one first image of at least one area of an object; processing the at least one first image to detect a group of suspected defects within the at least one area; performing a first classification process for initially classifying the group of suspected defects; determining whether a completion of a classification of the first subgroup of the suspected defects requires additional information from a second camera; when determining that the first subgroup of the suspected defects requires additional information from the second camera then: acquiring second images, by the second camera while applying image acquisition parameters of the second camera, to provide the additional information; and performing the second classification process for classifying the first subgroup of suspected defects.

Abstract: 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.

Abstract: An inspection system and a method for inspection an object. The method may include acquiring a defocused image of an area of an object, and processing the defocused image of the area to find a phase shift between optical paths associated with certain proximate points of the area. The phase shift may be indicative of a defect. The acquiring of the defocused image may include illuminating the area with a radiation beam that may be spatially coherent and collimated when impinging on the area. The illuminating may include passing the radiation beam through an aperture that may be defined by an aperture stop that may be positioned within an aperture stop plane. The size of the aperture may be a fraction of a size of the aperture stop.

Abstract: 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.

Abstract: A method for detecting defects in a thinned die, the method may include inspecting the thinned die with a two-dimensional inspection module, to find suspected defects that appear as non-reflecting regions that fulfill a size condition; measuring, using a depth measurement module, a depth of the suspected defects; and defining a suspected defects as a defects when the depth parameter exceeds a depth threshold.

Abstract: A method for determining a property of an object positioned on a photo-sensitive polyimide layer, wherein the photo-sensitive polyimide layer is positioned on a lower layer that is a radiation reflecting layer, the method may include illuminating, by an illumination unit, an area of the photo-sensitive polyimide layer with first ultraviolet radiation; sensing, by a first sensor, a first reflected ultraviolet radiation that was reflected from the area; and determining, by a processor, based at least in part on the first reflected ultraviolet radiation, the property of the object.

Abstract: 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.

Abstract: There may be provided a method for inspecting a top redistribution layer conductors of an object. The top redistribution layer (RDL) is positioned above at least one lower RDL and above at least one other dielectric layer. The method may include (i) illuminating the object with radiation, the at least one lower dielectric layer significantly absorbs the radiation; (ii) generating, by a detector, detection signals that represent radiation reflected from the object, and (iii) processing, by a processor, the detection signal to provide information about the top RDL. The processing may include distinguishing detection signals related to the top RDL from detection signals related to the at least one lower RDL.