Machine learning based error classification for curvilinear designs
Curvilinear design layout poses new challenges to computational lithography tools that were developed mainly to handle Manhattan geometries. The geometry-based error classification used in the Optical Proximity Correction (OPC) verification flow is limited in it's ability to support curvilinear designs. In this paper, we present our innovative work using Siemens EDA Calibre® OPCVerify Machine Learning (ML) Classify technology to classify error markers in the feature vector space instead of traditional pattern’s vertices and edges geometries.
In our experiments, ML Classify is successful in classifying OPC verification error markers in curvilinear designs. A drawn silicon photonics layout with 837,072 raw error locations has demonstrated our ML Classify tool’s capability to reduce the unique class count from 221,085 -- based on conventional geometry-based classify approach -- down to 51. We also developed a feature to further sub-classify results by edge types, convex, concave, or straight line, and by polygon’s internal width and external space to neighboring polygons. The 51 unique class count becomes 2493 after the further sub-classify process. This methodology is not only good for silicon photonics application, but also good for other curvilinear photomask applications, like CL MPCV, MEMS, on-chip metasurface optics, and in general even Manhattan designs.