Intelligent model-based OPC

作者：Wen‐Chun Huang, Ching‐Ming Lai, B. Luo, C.-K. Tsai, Mingchang Chih, Chien‐Wen Lai, C. C. Kuo, Ruitong Liu, H.T. Lin · 发表于：Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 年份：2006 · DOI：10.1117/12.657792 · 被引用次数：19 · 研究领域：Advancements in Photolithography Techniques、Industrial Vision Systems and Defect Detection、Surface Roughness and Optical Measurements

Optical proximity correction is the technique of pre-distorting mask layouts so that the printed patterns are as close to the desired shapes as possible. For model-based optical proximity correction, a lithographic model to predict the edge position (contour) of patterns on the wafer after lithographic processing is needed. Generally, segmentation of edges is performed prior to the correction. Pattern edges are dissected into several small segments with corresponding target points. During the correction, the edges are moved back and forth from the initial drawn position, assisted by the lithographic model, to finally settle on the proper positions. When the correction converges, the intensity predicted by the model in every target points hits the model-specific threshold value. Several iterations are required to achieve the convergence and the computation time increases with the increase of the required iterations. An artificial neural network is an information-processing paradigm inspired by biological nervous systems, such as how the brain processes information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. A neural network can be a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The network can accurately predict the behavior of a system via the learning procedure. A radial basis function network, a variant of artificial neural netwo...