Smart Data Filtering for Enhancement of Model Accuracy
As integrated circuit technology advances and features shrink, the scale of critical dimension (CD) variations induced by lithography effects become comparable with the critical dimension of the design itself. At the same time, each technology node requires tighter margins for errors introduced in the lithography process. Optical and process models—the black boxes that simulate the pattern transfer onto silicon—are becoming more and more concerned with those different process errors. As a consequence, an optical proximity correction (OPC) model consists mainly of two parts; a physical part dealing with the physics of light and its behavior through the lithographical patterning process, and an empirical part to account for any process errors that might be introduced between writing the mask and sampling measurements of patterns on wafer.
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