Artificial intelligent matching for scratches of semiconductor wafers based on a K-NN algorithm

Abstract

Scratches, those usually generated during polishing the silicon wafer surface, are one of the major yield loss factors in semiconductor manufacturing industry. In order to determine the source of the scratches in real time and reduce the yield loss, it is critical for manufacturers to match and identify the same type of scratches automatically. In this paper, an improved K nearest neighbors (KNN) algorithm to address this issue is presented. Firstly, a skeleton extraction method is used to depict the main lines of scratches. Then the clustering protocol is applied as a preliminary step to group these main lines so that some essential endpoints features of main lines, such as distance, slope and curvature, can be extracted. During feature extraction, a dynamic coordinate system is introduced and this greatly reduces the distortions arise due to the magnitude of tangent difference. An intelligent matching of similar scratches MSML-KNN algorithm is formulated. The experimental results show that the proposed matching method for wafer scratches has a good adaptability and robustness.

Classification of wafer map defect based on ResNeSt

“…The Edge-Loc type [9] is caused by uneven heating during diffusion in most cases. Scratch type [10] is generally caused by human error during transportation and handling or faults in chemical-mechanical polishing. All types in the dataset were labeled by experts and included a total of 811, 457 images, of which approximately 20% were annotated by experts, a total of 172, 950 images.…”

STMP at 10: shaping surface metrology, measurement and phenomena for a decade

“…The use of scatterometry as a fast and robust measurement of nano-textured surfaces can be found in [37]. Finally, artificial intelligent matching for scratches of semiconductor wafers is explored in [204].…”