本論文將粒子群多目標最佳化（MOPSO）演算法整合到光源和光罩同步優化（SMO）過程中，以增強極紫外（EUV）光刻成像的性能。本研究開發了同步處理光源和光罩圖案的方法。對於自由形式的光源結構基於像素化的優化過程，可在一般的個人電腦平台上進行。在此應用MOPSO演算法生成優化的自由形式光源。同時應用模型式的光學鄰近校正（OPC）來優化光罩佈局圖案。考慮到EUV光刻系統的特點，SMO搭配MOPSO演算法的開發受到兩個成本函數的條件限制：邊緣放置誤差（EPE）和水平/垂直偏差。先是使用一維的線/空間（L / S）圖案作為基線信息來測試所開發的SMO演算法其帕累托行為。然後再使用半間距為22nm的二維圖案評估所開發的SMO演算法。本研究提出的MOPSO可成功地解出自由形式光源的四個在帕累托前沿中的非支配解。可作為性能指標的製程窗口（PW）條件，包括潛像對比度，曝光寬容度（EL），焦深（DOF）和偏差誤差等。所提出的SMO演算法顯示，在稍微犧牲DOF的情況下， PW條件中的EL將普遍地獲得改善。在一維的 L / S下， 當EL增加5.26％則DOF損失11.34％；在二維的圖案下，EL和DOF分別提高了43.6％和18.11％。

This thesis integrates multi-objective particle swarm optimization (MOPSO) algorithm into the source and mask co-optimization (SMO) process to enhance the extreme ultraviolet (EUV) lithography imaging performance. A simultaneous source and reticle pattern process method is developed in this research. For the freeform source construction, a pixelated-based optimization process was performed on PC platform. The MOPSO algorithm was applied to generate freeform source. Model-based optical proximity correction (OPC) was applied to correct the mask layout patterns. Considering the characteristics of the EUV lithography system, the developed SMO with the MOPSO algorithm is constrained by two cost functions: the edge placement error (EPE) and horizontal/vertical bias. A one-dimensional line/space (L/S) pattern is used as the baseline information to test the Pareto behavior of the developed SMO algorithm. Then, the 2D pattern with half-pitch 22-nm was assessed using the developed algorithm. The proposed MOPSO algorithm succeeded to construct non-dominated solutions of freeform sources and Pareto front which four of those solutions are presented. The performance indicators include process windows (PW) condition such as the aerial image contrast, exposure latitude (EL), depth of focus (DOF), and bias errors. The proposed algorithm shows that the common PW conditions improved on EL while the DOF is slightly suffering. The EL increased for 5.26% and DOF suffers for 11.34% in 1D L/S and both EL and DOF increased for 43.6% and 18.11%, respectively for the 2D pattern.

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