In advanced IC manufacturing, as the gap increases between lithography optical wavelength and feature size, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which extracts appropriate feature vectors of hotspot and nonhotspot patterns. Our model can dynamically tune appropriate fuzzy regions around known hotspots. Based on this paper, we develop a fast algorithm for lithography hotspot detection with high accuracy of detection and low probability of false-alarm counts. In addition, since higher dimensional size of feature vectors can produce better accuracy but requires longer run time, this paper proposes a grid reduction technique to significantly reduce the CPU run time with very minor impact on the advantages of higher dimensional space. Our results are very encouraging, with average 94.5% accuracy and low false-alarm counts on a set of test benchmarks.

In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. Our results are very encouraging with under 0.56 CPU-hrs/mm2 runtime.

Removing noises while preserving and enhancing edges is one of the most fundamental operations of image/video processing. The difficulty is that usually the filtering will reduce the sharpness of the image. On the other hand, optical lens and some circuitry imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increases the noise level as a by-product. In this paper, we present a new integrated approach to address these issues. The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces. The experimental results show that the proposed approach can effectively reduce the noises while preserving and enhancing edges.

A new color image processing pipeline (IPP) is proposed, which processes the raw image data captured from CCD/CMOS sensors and converts to the final picture with color and exposure corrected. This pipeline bridges the gap between theoretic color imaging sciences and the practical IPP implementation of digital image capturing systems. The results demonstrated by processing different scenes of pictures show that the proposed pipeline runs well in diverse scenes and illuminants.

This paper presents a new color reproduction approach that combines auto white balance (AWB) and color correction to recover the spectral reflectance properties of the objects in the scene and the spectral power distribution of the incident illuminants. By fully utilizing the calibration result of CCD/CMOS sensors characteristics, the AWB algorithm can predict the color temperature of the illuminants with a very high success rate. The calibrated color correction matrix transforms the sensor tristimulus values into the standard RGB (sRGB) color space with a relatively good precision. The experimentation results on pictures captured by a real camera at different scenes show that the proposed white balance and color correction algorithm perform well in many diverse scenes and illuminants.