由於極紫外光微影系統技術尚未廣泛應用，目前生產線上仍大部分為ArF-193nm浸潤式微影系統，因此有許多研究者尋找此系統優化方法，其中可利用光源光罩最佳化程序搜尋最佳的光罩圖案設計與對應的最佳光源模態，進而改善微影成像品質的有效方法為本研究的研究方向。在本論文中，以DRAM光罩圖案設計為目標開發Interference Map Lithography Differential Evolution Optimization (IMLDEO)演算法，並結合機器學習加快演算法計算速度。在IMLDEO中利用IM分佈圖(Interference Map)設計潛在的次級解析輔助特徵，並對目標圖案進行光學鄰近修正，其中光學鄰近修正使用已開發之Model-based OPC(MBOPC)，而MBOPC中有許多參數需調整，不同參數的設定將會影響MBOPC的結果，因此本研究使用模擬退火法來搜尋最佳參數，完成修正的光罩將由機器學習進行分群並預測目標函數，最後選擇較佳的光罩利用PROLITH微影光學軟體進行空間潛像之計算，如此程序持續至目標函數進入容忍範圍內。在機器學習模型中包含了稀疏自編碼器與k平均演算法，稀疏自編碼器的目的為壓縮並提取次級解析輔助特徵，再將壓縮後資料交由k平均演算法進行分群，主要目的為加快光罩圖案設計程序的速度，避免大量光罩進行光學模擬。最後使用光罩圖案測試本論文所開發的程序，量測其曝光成像品質，利用其結果分析結合機器學習前後之差異性，並與PROLITH微影光學軟體進行比較。在Contact_Hole與Line_Space_Array二維光罩測試中，結合機器學習成功加速了計算時間分別為20%與12%，而在曝光寬容度EL = 5%的情況下，聚焦深度分別為110 [nm]與142.5 [nm]，均比PROLITH微影光學軟體所計算的75.6 [nm]與60.4 [nm]較大，代表本研究成功開發機器學習結合光罩圖案設計程序。

Extreme ultraviolet lithography systems have not been widely used in the semiconductor production line; therefore, many researchers are searching for solutions to improve the lithography process quality of ArF-193nm immersion lithography systems. The source mask optimization (SMO) technique is well known for its capability to improve lithography imaging. This research aims to develop the interference map lithography differential evolution optimization (IMLDEO) for DRAM patterns based on ArF-193nm immersion lithography systems, and combined with machine learning to reduce running time. In IMLDEO, an interference map is calculated to design possible sub-resolution assist features (SRAFs), and apply model-based optical proximity correction (MBOPC) on main features. Since in the MBOPC many parameters need adjustments and different parameter settings will affect MPOPC results, this research uses simulated annealing to search for the best OPC parameters. After pattern correction, the mask will be grouped and predicted edge placement error (EPE) by sparse autoencoder and k-means algorithm. Finally, use PROLITH to estimate aerial images and repeat above-mentioned steps until the objective function satisfies stopping criterion. In the 2D contact hole and line space array mask tests, combined with machine learning successfully reduce running time by 20% and 12%. In the case of EL = 5%, the depth of focus is up to 110 [nm] and 142.5 [nm] respectively, better than 75.6 [nm] and 60.4 [nm] calculated by PROLITH. Based on these testing results, it explains that machine learning based IMLDEO is able to successfully apply to the DRAM pattern design.

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