Machine learning based model builder and its applications for pattern transferring in semiconductor manufacturing

A method of optimizing an optical proximity correction (OPC) model for a mask pattern of a photo mask, the method comprising:

• obtaining selected measurement data from a characteristic data selector that sorts a plurality of measurement data in terms of impact to the OPC model and selects one or more measurement data having most significant impact on the OPC model as the selected measurement data;
• adding the selected measurement data into the OPC model;
• iteratively optimizing the OPC model by adjusting the characteristic data selector using the selected measurement data as initial information until performance of the OPC model with the selected measurement data obtained by the adjusted characteristic data selector meets a design requirement and the OPC model with the added measurement data is obtained as an optimized OPC model;
• and
• performing an OPC operation on the mask pattern using the optimized OPC model.

A system of optimizing an optical proximity correction (OPC) model for a mask pattern of a photo mask, comprising:

• a memory that stores computer executable program;
• and
• a processor that executes computer executable components stored in the memory, wherein the computer executable program, when executed by the processor, causes the processor to:
• generate random terms in a M-dimensional space;
• classify the random terms to clusters by applying feature selection, subset selection, and dimensionality reduction;
• determine a representative subset of the random terms, wherein the representative subset comprises representative terms from the classified clusters, and wherein the representative terms are selected based on an impact of the representative terms on the OPC model;
• and
• iteratively optimize the OPC model by using the representative subset as initial information.