Optimized test pattern selection with machine learning method

Abstract

The technology node shrinks years after years. To guarantee the functionality and yield of IC production, the resolution enhancement technology becomes more and more important. Both optical proximity correction and inverse lithography technique need a precisely calibrated lithographic model. A mask of test patterns needs to be prepared and the lithographic experiment has to be done with it to obtain the CD SEM data for the model fitting. It is beneficial to select the test pattern efficiently. Fewer number of test patterns should be selected without compromising their coverage capability and the accuracy of the lithographic model. We present a machine learning method based on the convolutional autoencoder and core set selection method to achieve above goal. We optimize the existing test pattern mask by selecting parts of gauges out. The OPC models calibrated with the selected data are compared with the models calibrated with original test patterns to evaluate our method.