微影製程有兩項重要指標，一能將光罩圖案潛像(Aerial Image)正確的轉移至光阻基材上，二提供足夠製程視窗(Process Window)以供應穩定的製程良率。本研究目的在針對投影式微影系統開發光源模態最佳化(Source Shape Optimization)方法，對不同線路圖案光罩以模型式光學鄰近修正做處理，提供對應修正後圖案專屬的曝光光源模態，使原有的微影系統提升其曝光解析度(Resolution)能力及製程容許度(Process Latitude)。並以曝光光源波長13.5nm數值孔徑0.25遠紫外光(Extreme Ultraviolet，EUV)微影系統及曝光光源波長193nm數值孔徑1.35浸潤式(Immersion)微影系統為例測試所開發之演算法。優於現有以梯度為主的模態最佳化方法，本研究在光源模態最佳化技術上不需初始光源模態且不受線路圖案的限制，基於像素方式做組合可使光源模態有足夠的變化性提供最大的自由度。為因應最佳化問題複雜度提升，本文在組合最佳化光源模態像素時採用啟發式概念的蟻群最佳化(Ant Colony Optimization，ACO)及粒子群最佳化兩(Particle Swarm Optimization，PSO)種演算法，透過仿生物群聚的行為分別對光源像素位置探討順序做規劃，當增加的光源像素位置使曝光效果提升而保留，使曝光效果變差則取消，演算法將產生一像素位置探討順序可組合出最佳化光源模態，增強微影系統的曝光效能。

The purpose of this study is to investigate the pixel-based source mask optimization (SMO) algorithm for a projection optical lithography system. SMO algorithm in this research uses model-based optical proximity correction (OPC) to modify mask layout patterns and provides the optimized source shape for the layout patterns. Two significant metrics are used to evaluate the modified source performance after circulating the optimization steps: the aerial image (AI) intensity and process window (PW). The pixel-based SMO algorithm in this research is superior to the conventional gradient-based SMO algorithm limited by the initial source shape and circuit pattern layout. We realize full free form source shapes using a heuristic ant colony optimization (ACO) and a particle swarm optimization (PSO) methods to update each pixel source on the pupil plane. The updating criterion is to verify whether AI intensity of each pixel source can reach the nominal threshold value. Two lithography systems are used for the study of the developed optimization algorithm in this thesis: extreme ultraviolet lithography system (NA=0.25 wavelength= 13.5nm) and immersion lithography system (NA=1.35 wavelength= 193nm). The results indicate the enhanced aerial image quality and the improved depth of focus after applying the developed SMO algorithm.

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