Image analysis – Image enhancement or restoration – Image filter
Reexamination Certificate
2001-09-07
2004-07-13
Patel, Jayanti K. (Department: 2621)
Image analysis
Image enhancement or restoration
Image filter
C382S145000
Reexamination Certificate
active
06763142
ABSTRACT:
TECHNICAL FIELD
The present invention relates in general to imaging systems and more particularly to filtering or noise removal methods for use with imaging systems.
BACKGROUND
Imaging systems are used in a variety of applications including the inspection of semiconductor wafers. Such imaging systems may be used to optically scan or capture images of the surface of a target such as a semiconductor wafer to measure the topography of a target surface. This topography may then be analyzed to identify manufacturing or material defects existing on the target. Such analysis is critical in diagnosing manufacturing problems to maintain a desired manufacturing throughput.
One problem that hampers the effectiveness of imaging systems is noise. Noise typically includes unwanted electrical or optical signals that distort and degrade the quality of the data collected by an imaging or inspection system. Noise may occur randomly as various external events may interfere with an imaging system. Noise may also recur regularly as some external events regularly effect an imaging system in the same manner, producing the same pattern of noise. This recurring noise may also be referred to as fixed pattern noise.
There are a several major sources of noise that effect imaging systems. These include camera and sensor noise, nonlinear response noise, optically created noise, photo statistical noise, misregistration noise, and optical aberration noise. Fixed pattern noise may result from a one or more of these types of noise. Other sources of noise may also contribute to fixed pattern noise. Characteristics of fixed pattern noise may include a similar pattern in all image data, a spatially fixed location of the noise, and a time varying complex phasor representing both the magnitude and phase of the noise.
Because noise represents an erroneous signal, noise significantly reduces the effectiveness of imaging and inspection systems. Accordingly noise can severely hamper the ability to identify and remedy manufacturing and material defects, negatively effecting manufacturing throughput and yield.
SUMMARY
Therefore a need has arisen for a system and method for reducing fixed pattern noise in imaging systems.
A further need has arisen for a system and method for increasing the effectiveness of imaging systems used to identify defects in semiconductor manufacturing and materials.
In accordance with teachings of the present disclosure, a system and method are described for removing fixed pattern noise. The system includes a positioning system the can hold and position a target, such as a semiconductor wafer. An optical system is positioned proximate the target to capture images thereof and is also linked to a fixed pattern noise removal engine. The fixed pattern noise removal engine receives complete object wave data of both a reference image and a target image captured by the optical system. The fixed pattern noise removal engine utilizes a filter for fixed pattern noise removal by dividing the cross power spectral density of the reference image and the target image by the power spectral density of the reference image.
More particularly, the fixed pattern noise removal engine may identify fixed pattern noise by applying the filter to the reference image. Further, the fixed pattern noise removal engine may also remove the identified fixed pattern noise from the target image by subtraction on a pixel-by-pixel basis.
More particularly, the fixed pattern noise removal engine generates a Fast Fourier Transform (FFT) and a complex conjugate of the FFT of the reference image. The fixed pattern noise removal engine then generates the power spectral density of the reference image. The fixed pattern noise removal engine also generates a Fast Fourier Transform (FFT) and a complex conjugate of the FFT of the target image. The fixed pattern noise removal engine then generates the power spectral density of the target image. Finally, the fixed pattern noise removal engine calculates the cross power spectral density utilizing the power spectral density of the target image and the power spectral density of the reference image.
The present disclosure includes a number of important technical advantages. One important technical advantage is identifying the fixed pattern noise within the reference image and the target image by dividing the cross power spectral density of the reference image and the target image by the power spectral density of the reference image. This allows the system to identify fixed pattern noise and therefore removing fixed pattern noise. This method also acts to increase the effectiveness and sensitivity of imaging systems used to identify defects in semiconductor manufacturing and materials.
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Dai X. Long
Hunt Martin A.
Baker & Botts L.L.P.
nLine Corporation
Patel Jayanti K.
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