As semiconductor processing becomes more complicated and pattern sizes shrink, the overlay metrology has become one of the most important issues in the semiconductor industry. Therefore, in order to obtain correct, reliable overlay values in semiconductor fabrication facilities (fab), quantization methods for the efficient management and implementation of a measurement algorithm are required, as well as an understanding of the target structures in the semiconductor device. We implemented correct, reliable overlay values in the pattern using the image processing method. The quantization method, through correlation analysis and a new algorithm for target structures, were able to improve the sensitivity to misalignment in the pattern and enable more stable and credible in-line measurement by decreasing the distribution of the residuals in overlay values. Since overlay values of the pattern in the fab were measured and managed more reliably and quickly, it is expected that our study will be able to contribute to the yield enhancement of semiconductor companies.
We present a new formulation to solve a defect detection problem on images using multiple reference images. The reference images are defect-free images obtained from the same position of other products. The defect detection problem is reformulated as a binary labeling problem, where each pixel is labeled with "one" if it contains a defect and with "zero" otherwise. The formulation of the energy function used for the labeling problem is defined. Then, the graph-cuts algorithm is used to obtain the optimal label set minimizing the energy function that becomes the defect detection result. The presented approaches are robust to noises taken from several sources, including image-taking, transmission process, environmental lighting, and pattern variation. It does not suffer from the alignment problem for the conventional comparison methods using references. These approaches are illustrated with real data sets, semiconductor wafer images collected by scanning electron microscope equipment, and compared to other defect detection approach.
One of widely used methods to extract boundaries of objects in the image is the contour method based on the energy
models. Two well known energy models are the edge based and the region based model. Although each of the two
models works well with its own advantage, it has difficulty in the following situation: When the initial point the contour
evolving starts from is not located properly in the inside region of an object or some objects are partially overlapped so
that the intensity difference between boundary pixels on the overlapped area and their neighbors becomes relatively
small, the edge based model approach fails to produce good results. On the other hand, the region based model approach
fails to produce good results when more than two objects with different intensity averages exist. To overcome such
difficulty, we suggest the hybrid energy model approach constructed partially from each of the two approaches. In this
approach, some initial point the contour evolving starts from is randomly selected from the image. From the selected
initial point the contour starts evolving until it meets the boundary of either some object or background. Once the
boundary of one object or background is found, its region is removed from the image. On the rest of the image, the same
procedure is repeated until the boundaries of all objects and background are found. The suggested approach is illustrated
using SEM images of semiconductor wafers.