Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment.

Wafer alignment is the core technique of lithographic tools. Image-processing-based wafer alignment techniques are commonly used in lithographic tools. An alignment algorithm is used to analyze the alignment mark image for obtaining the mark position. The accuracy and speed of the alignment algorithm are very important for guaranteeing the overlay and throughput of lithographic tools. The most commonly used algorithm in image-processing-based alignment techniques is the self-correlation method. This method has a high accuracy, but the calculation is complex, and the calculation speed is slow. In this paper, we propose a sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation. The algorithm first reconstructs the alignment signal by sampling theorem interpolation and then obtains the sub-pixel position of the mark by Gaussian fitting. The accuracy and robustness of the algorithm are verified by testing the simulated marks and experimentally captured marks. The repeat accuracy can reach 1/100 pixels, which is in the same level with the self-correlation method. The calculation speed is highly improved compared with the self-correlation method, which needs only about 1/3 of even short calculation time.

Measurement algorithm for wafer alignment based on principal component analysis.

We propose a novel measurement algorithm for wafer alignment technology based on principal component analysis (PCA) of a mark image. The waveform of the mark is extracted from the enlarged mark image, which is collected by CCD. The position of the mark center on the CCD can be calculated based on the extracted waveform. By applying PCA to the mark image, the first principal component containing position information of the mark can be obtained. Therefore PCA can be used to extract the waveform from the mark image. Compared with the typical waveform extraction method (the summed projection (SP) method), the proposed PCA method can use the position information contained in the mark image more effectively. Through simulation and experiment, it is proved that the proposed PCA method can improve the contrast of the normalized waveform, and then improve the alignment accuracy.