Abstract: The present disclosure provides methods for treating a subject having a HER2 non-amplified (HER2-negative) cancer or low HER2 expressing (HER2-low) cancer, comprising detecting expression of a BCAR4 gene fusion in a biological sample from the subject. If expression of the BCAR4 gene fusion is detected, a HER2-targeted cancer treatment is administered to the subject; if expression of the BCAR4 gene fusion is not detected, a cancer treatment that is not HER2-targeted is administered to the subject. Methods of predicting response to a HER2-targeted cancer treatment and detecting BCAR4 or BCAR4 gene fusion activation are also provided.

Abstract: Disclosed here in are methods and kits for identifying a prostate cancer treatment for a subject. The methods include obtaining a fluid sample from the subject, the fluid sample comprising noncellular DNA (ncDNA) from the subject, transforming the ncDNA into a plurality of genomic variations to determine if the ncDNA contains castration-resistant structural variations including at least one of a genomic alteration in AR encoding an androgen receptor and a genomic alteration of an AR enhancer; and identifying the prostate cancer treatment for the subject based on the plurality of genomic variations.

Abstract: Among the various aspects of the present disclosure is the provision of a methods and compositions for modulating IncRNAs and methods of treatment based on IncRNA expression.

Abstract: Methods and systems for discovery of defects of interest (DOI) buried within three dimensional semiconductor structures and recipe optimization are described herein. The volume of a semiconductor wafer subject to defect discovery and verification is reduced by storing images associated with a subset of the total depth of the semiconductor structures under measurement. Image patches associated with defect locations at one or more focus planes or focus ranges are recorded. The number of optical modes under consideration is reduced based on any of a comparison of one or more measured wafer level defect signatures and one or more expected wafer level defect signatures, measured defect signal to noise ratio, and defects verified without de-processing. Furthermore, verified defects and recorded images are employed to train a nuisance filter and optimize the measurement recipe. The trained nuisance filter is applied to defect images to select the optimal optical mode for production.

Abstract: A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Abstract: Methods and systems for discovery of defects of interest (DOI) buried within three dimensional semiconductor structures and recipe optimization are described herein. The volume of a semiconductor wafer subject to defect discovery and verification is reduced by storing images associated with a subset of the total depth of the semiconductor structures under measurement. Image patches associated with defect locations at one or more focus planes or focus ranges are recorded. The number of optical modes under consideration is reduced based on any of a comparison of one or more measured wafer level defect signatures and one or more expected wafer level defect signatures, measured defect signal to noise ratio, and defects verified without de-processing. Furthermore, verified defects and recorded images are employed to train a nuisance filter and optimize the measurement recipe. The trained nuisance filter is applied to defect images to select the optimal optical mode for production.

Abstract: An inspection tool includes a controller that is configured to generate a scan pattern for an electron beam to image areas of interest on the wafer. The scan pattern minimizes dwell time of the electron beam on the surface of the wafer between the areas of interest. At least one stage speed and at least one raster pattern can be selected based on the areas of interest. The controller sends instructions to electron beam optics to direct the electron beam at the areas of interest on the surface of the wafer using the scan pattern.

Abstract: A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Abstract: An inspection tool includes a controller that is configured to generate a scan pattern for an electron beam to image areas of interest on the wafer. The scan pattern minimizes dwell time of the electron beam on the surface of the wafer between the areas of interest. At least one stage speed and at least one raster pattern can be selected based on the areas of interest. The controller sends instructions to electron beam optics to direct the electron beam at the areas of interest on the surface of the wafer using the scan pattern.

Abstract: The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.

Abstract: The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.