Adaptive technology based two-dimensional plane sampling method

Abstract

The invention relates to an adaptive technology based two-dimensional plane sampling method. The method includes firstly, using a uniform sparse grid for performing first sampling, evaluating sampling results by the aid of an evaluation algorithm to obtain sub-regions of different frequency ranges, and replacing grid measuring points of each sub-region; secondly, using a newly generated grid for performing second sampling on the surface of a sampled object, using the evaluation algorithm for evaluating measuring results again, and modifying the gird according to frequency magnitude; thirdly, modifying repeatedly in the way until the grid passes the evaluation algorithm; and finally sampling the surface of an object needing sampling by a measuring system according to the measuring grid. By the method, the number of sampling points can be guaranteed to be minimized, and sampling effect can be guaranteed to be optimal under the condition of the same number of sampling points.

Description

technical field [0001] The invention relates to data collection and processing technology, in particular to a two-dimensional plane sub-area sampling method based on self-adaptive technology. Background technique [0002] The sharp improvement in the performance of lithography machines requires the use of larger numerical apertures and shorter wavelengths, resulting in a reduction in the depth of focus. The depth of focus of the widely used immersion lithography machine can only be maintained at the order of hundreds of nanometers, which has greatly exceeded the changes caused by vacuum adsorption warpage and substrate flatness, so it is required to improve the focus detection accuracy, and the silicon wafer Topography reconstruction is one of the core technologies, and high-speed and high-precision silicon wafer topography reconstruction has become the biggest difficulty. [0003] In order to solve this problem, sampling equipment and algorithms with high speed and high pr...

AI Technical Summary

Problems solved by technology

In actual use, the sampling period determines the quality of the sampling signal. If the period is too small, the number of sampling points will increase sharply, resulting in data redundancy; if the sampling period is too large, some information of the analog signal will be lost and distortion will occur.

The uniform grid sampling point sampling technology that is widely used now uses uniform frequency sampling for all areas. The sampling frequency depends on the highest frequency in ...

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