Abstract: A pattern grouping method may include receiving an image of a first pattern, generating a first fixed-dimensional feature vector using trained model parameters applying to the received image, and assigning the first fixed-dimensional feature vector a first bucket ID. The method may further include creating a new bucket ID for the first fixed-dimensional feature vector in response to determining that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns, or mapping the first fixed-dimensional feature vector to the first bucket ID in response to determining that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.

Abstract: A pattern grouping method may include receiving an image of a first pattern, generating a first fixed-dimensional feature vector using trained model parameters applying to the received image, and assigning the first fixed-dimensional feature vector a first bucket ID. The method may further include creating a new bucket ID for the first fixed-dimensional feature vector in response to determining that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns, or mapping the first fixed-dimensional feature vector to the first bucket ID in response to determining that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.

Abstract: A pattern grouping method may include receiving an image of a first pattern, generating a first fixed-dimensional feature vector using trained model parameters applying to the received image, and assigning the first fixed-dimensional feature vector a first bucket ID. The method may further include creating a new bucket ID for the first fixed-dimensional feature vector in response to determining that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns, or mapping the first fixed-dimensional feature vector to the first bucket ID in response to determining that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.

Abstract: An imaging system as disclosed can include multiple bistatic radar sensors configured to transmit electromagnetic waves towards a surface of a target object and configured to measure the electromagnetic waves reflected from the surface of the target object. The imaging system includes a computing device that determines time of flight estimates based on the measured waves. The computing device can draw, within an image model for the target object, multiple candidate surface portions of the surface of the target object based on the TOF estimates and predetermined positions of the bistatic radar sensors. Further, the computing device can assign weights to the candidate surface portions. The computing device can determine points where the candidate surface portions meet with a predetermined probability based on the weights. The computing device is configured to define an estimated surface of the target object in the image model based on the determined points.

Abstract: Rapid radio frequency (RF) microwave devices and methods are disclosed. According to an aspect, a waveguide includes a body having first and second components that are attachable together to form an interior having a surface. Further, the waveguide includes a conductive material formed on the interior surface and shaped to convey electromagnetic waves.

Abstract: Rapid radio frequency (RF) microwave devices and methods are disclosed. According to an aspect, a waveguide includes a body having first and second components that are attachable together to form an interior having a surface. Further, the waveguide includes a conductive material formed on the interior surface and shaped to convey electromagnetic waves.

Abstract: An imaging system as disclosed can include multiple bistatic radar sensors configured to transmit electromagnetic waves towards a surface of a target object and configured to measure the electromagnetic waves reflected from the surface of the target object. The imaging system includes a computing device that determines time of flight estimates based on the measured waves. The computing device can draw, within an image model for the target object, multiple candidate surface portions of the surface of the target object based on the TOF estimates and predetermined positions of the bistatic radar sensors. Further, the computing device can assign weights to the candidate surface portions. The computing device can determine points where the candidate surface portions meet with a predetermined probability based on the weights. The computing device is configured to define an estimated surface of the target object in the image model based on the determined points.