This paper presents a method of digitally removing or correcting Chromatic Aberration (CA) of lens, which
generally occurs in an edge region of image. Based on the information of the lens's and sensor's features in camera, it
determines CA level and the dominant chrominance of CA and efficiently removes extreme CA such as purple fringe
and blooming artifacts, as well as a general CA to be generated at an edge in an image captured by a camera. Firstly, this
method includes a CA region sensing part analyzing a luminance signal of an input image and sensing a region having
CA. Secondly, the CA level sensing part calculates the weight, which indicates a degree of CA, based on a difference
between gradients of color components of the input image. Thirdly, for removing the extreme CA such as purple fringe
and blooming artifact which caused by the feature of lens and sensor, it uses 1-D Gaussian filters having different sigma
values to get the weight. The sigma value indicates the feature of lens and sensor. And, for removing the general CA, it
includes the adaptive filter, based on luminance signal. Finally, by using these weights, final filter will be produced
adaptively with the level of CA and lens's and sensor's features. Experimental results show the effectiveness of this
proposed method.
In this paper, we propose a space variant image restoration method where the each different local regions of a given image are de-blurred by each different estimated de-convolution filter locally. The depth of each local blocks are estimated roughly on the optical module representing different indices of refraction for different wavelengths of light. Following the depth, each different region of an image is restored based on the sharpest channel among 3 channels (Red, Green, Blue). Then, in order to prevent discontinuities between the differently restored image regions, we use the piecewise linear interpolation on overlapping regions. Also, practically, this method is applied to 3Mega camera module in order to confirm the effect of proposed algorithm.
The purpose of this study is to examine gray matching between dark and ambient condition and to improve visibility
using result of gray matching experiment in mobile display and target luminance is 30000 lux for experiment. First of
all, for measuring visibility on ambient condition, the patch count experiment is conducted by investigating that how
many patches can be seen at original images under the ambient light. The visibility in ambient condition was significant
in comparison to dark condition. Next, the gray matching experiment is conducted by comparing gray patches between
dark and ambient condition using method of adjustment. The participants responded that the white or bright gray patch
could not find same brightness patch under ambient condition. To confirm the visibility improvement through the result
of gray matching experiment, visibility is measured under the ambient light after simple implementation. It was same
procedure of the first visibility experiment. After applying the gray matching curve, visibility was more improvement.
Statistic T test result between patches applied gray curve and maximum of dark condition was not significant. It means
that visibility was not different between original patches of dark condition and patches applied curve of ambient
condition.
We develop a methodology to find the optimal memory color (colors of familiar objects) boundary in YCbCr color space and a local image adjustment technique called preferred color reproduction (PCR) to improve image quality. The optimal memory color boundary covers most familiar object colors taken under various viewing conditions. The PCR algorithm is developed based on the idea that colors of familiar objects (memory colors) are key factors in judging the naturalness of an image. The PCR algorithm is applied to pixels detected as having a memory color. Memory color detection is conducted using color information by checking if an input color is inside the predetermined memory color boundary. The PCR algorithm transforms colors inside the memory color boundary to be shifted toward the boundary of constant interval in the center. The PCR algorithm is applied to skin colors, and psychophysical experiments using real images were conducted to determine the best parameters for the algorithm resulting in the most preferred image.
MEEF (Mask Error Enhancement Factor) is the most representative index which CD (Critical Dimension) variation in wafer is amplified by real specific mask CD variation. Already, as it was announced through other papers, MEEF is increased by small k1 or pattern pitch. Illumination system, just like lens aberration or stage defocus affects directly MEEF value, but the leveling or species of substrate and the resist performance are also deeply related to MEEF value. Actually, when the engineers set up the photo process of shrink structure in current device makers, they established minimum shot uniformity target such as MEEF value within wafer uniformity and wafer to wafer uniformity, besides UDOF (Usable Depth of Focus) or EL (Exposure Latitude) margin.
We examined MEEF reduction by checking the difference in resist parameters and tried to correlate the results between experiment and simulation. Solid-C was used for simulation tool. The target node was dense L/S (Line/Space) of sub-80 nm and we fix the same illumination conditions. We calculated MEEF values by comparing to original mask uniformity through the optical parameters of each resist type. NILS (Normalized Image Log Slope) shows us some points of the saturation value with pupil mesh points and the aberration was not considered. We used four different type resists and changed resist optical properties (i.e. n, k refractive index; A, B, and C Dill exposure parameters). It was very difficult to measure the kinetic phenomenon, so we choose Fickian model in PEB (Post Exposure Bake) and Weiss model in development. In this paper, we tried to suggest another direction of photoresist improvement by comparing the resist parameters to MEEF value of different pitches.
The most important issue in lithography as a semiconductor process is to obtain the minimum resolution. In order to obtain the minimum resolution with processible depth of focus, the numerical aperture is gradually increased and the exposure wavelength is also decreased. The effect of aberration is also increased as a result. It was not much needed to consider the aberration effects for the critical dimensions (CD) greater than around 300 nm. However, it is greatly necessary to consider the effect of aberration for CDs smaller than 100 nm in order to obtain the best process condition. The purpose of this study is to evaluate the aberration effect of the projection system for the specified node and shape of pattern. Evaluation is made by comparing the various aberration effects for the different exposure wavelengths, different shapes such as isolated, line and space, contact hole and L-shaped patterns, and also for the duty ratio by using commercial lithography simulator, SOLID-C [1].
MEEF (Mask Error Enhancement Factor) is the most representative index which CD (Critical Dimension) variation in wafer is amplified by real specific mask CD variation. Already, as it was announced through other papers, MEEF is increased by small k1 or pattern pitch. Illumination system, just like lens aberration or stage defocus affects directly MEEF value, but the leveling or species of substrate and the resist performance are also deeply related to MEEF value. Actually, when the engineers set up the photo process of shrink structure in current device makers, they established minimum shot uniformity target such as MEEF value within wafer uniformity and wafer to wafer uniformity, besides UDOF (Usable Depth of Focus) or EL (Exposure Latitude) margin. We examined MEEF reduction by checking the difference in resist parameters and tried to correlate the results between experiment and simulation. Solid-C was used for simulation tool. The target node was dense L/S (Line/Space) of sub-80 nm and we fix the same illumination conditions. We calculated MEEF values by comparing to original mask uniformity through the optical parameters of each resist type. NILS (Normalized Image Log Slope) shows us some points of the saturation value with pupil mesh points and the aberration was not considered. We used four different type resists and changed resist optical properties (i.e. n, k refractive index; A, B, and C Dill exposure parameters). It was very difficult to measure the kinetic phenomenon, so we choose Fickian model in PEB (Post Exposure Bake) and Weiss model in development. In this paper, we tried to suggest another direction of photoresist improvement by comparing the resist parameters to MEEF value of different pitches.
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