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Constructing freeform source through the combination of neural network and binary ant colony optimization

Source Optimization is a key techniques to enhance the process window in ArF 193i-nm lithography process. This paper proposes a source optimization algorithm combining metaheuristic-based binary ant colony source optimization (BACO) and an artificial neural network (ANN). The purpose of this study is to establish the optimal freeform source for improving the process window of the critical patterns and maintaining the quality of aerial images. The source plane is pixelated and divided into 25 sectors. In this study, a set of the input data for training the ANN includes the pattern edge contours resulting from the various process conditions with respect to each searching agent at each iteration. The trained ANN selects sectors with effective pixel sources illuminating the target pattern to enhance the aerial image quality and improve the process window. The combination of the B-ACO and ANN methods decreases the searching space and speed up the convergence of the B-ACO. PROLITHTM (KLA-Tencor) is used to calculate the aerial image when using the optimized freeform source. The developed algorithm is tested using the 17 clips of 1D line/space pattern with various line widths, pitches and line orientations. The testing pattern includes nine horizontal line features and eight vertical line features. The minimum line width is 40 nm with a pitch of 80nm, and the maximum linewidth is 120nm with a pitch of 500nm. An optimized freeform source is simultaneously constructed for these 17 clips. The imaging performance for these 17 clips is presented.