Inverse Lithography Technology(ILT)has received much attention in recent years due to its outstanding performance in the new technology node development. Compared to conventional OPC technology, the all-angle nature of ILT makes modeling and simulation more challenging. In this study, we introduced a novel ILT modeling method that applies varied-angle gauges to enhance the model accuracy of patterning with a curvilinear mask. Initially, we designed a curvilinear mask pattern and collected the related CDSEM data after mask fabrication and wafer exposure. Subsequently, three kinds of gauge combinations for model calibration were created using gauge numbers of 252 (0°, 90°), 504 (0°, 90°, ±45°), and 1008 (0°, 90°, ±45°, ±30°, ±60°), incorporating different CD information extracted from the CDSEM image contours. Through rigorous model fitting and validation, the calibrated ILT model with all-angle gauges demonstrated superior performance compared to the model calibrated with conventional horizontal and vertical gauges, particularly for low-symmetry patterns that exhibit inherent anisotropy in the angular distribution of critical dimensions. Furthermore, a better simulation accuracy with Manhattanized ILT mask of different MRC levels was obtained with the model calibrated with the all-angle gauges.
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.
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