幾十年來半導體工業仰賴微影光學將電路圖案之設計轉印至晶圓基板上，隨著電路設計圖樣日益縮小，目前已逼近至微影製程的光學極限，解析度增強技術(resolution enhancement technique, RET) 成為克服此極限的一種方式，而光源最佳化能克服因設計圖樣較為複雜所導致的微影缺陷，是其中一種重要的解析度增強技術。在此研究中，利用以像素為基準的光源最佳化使微影圖樣在不失真的情況下增加微影製程的容許範圍(process window)。首先，光源最佳化的架構是以次經驗法則(meta-heuristic)的螞蟻群體為模型去開發，螞蟻群體會建構最佳像素組合的Freeform光源，其中每個像素代表同調點光源(coherent point source)，利用螞蟻群體最佳化光源的其中一個優點是能保證達到收斂，但其收斂時間不固定，為了解決收斂時間不固定的問題，將次經驗蟻群演算法結合瞬時混沌類神經網路(transiently chaotic neural network, TCNN)應用在光源最佳化的架構上，類神經網路的目標是找到能夠增加微影製程容許範圍(process window)的適當光源區域，能有效縮減螞蟻群體所需要搜索的光源範圍，因而減少演算法到達收斂的時間。Freeform光源的微影曝光品質主要是依據電腦模擬出的空間潛像(aerial image, AI)做評估，以一維 L/S和Contact Hole為例能夠證明演算法有效縮短收斂時間，而在較複雜的圖樣下亦能證明利用此混合最佳化方法所產生的光源曝光品質優於參考光源，其中Five-bar L/S圖樣顯示此混合最佳化方法能夠同時增加Iso和Dense圖樣的曝光品質和焦距深度(depth of focus, DOF)，而在最後測試的22nm node SRAM圖樣中，以混合最佳化方法得到的最佳光源曝光品質表現優於參考光源，與其他例子相同，由此混合最佳化方法建構出來的光源亦能在改善在微影製程參數有微小變動情況下的曝光品質。

Source optimization (SO) is one of the important RET used to overcome the distortion occur in lithography as the complexity of the mask pattern increases. In this study, a pixelated SO is employed for improving the process window of lithography process while obtaining a good image fidelity. The SO framework is developed using metaheuristic binary ant colony (ACO) and neural network approach. The binary ACO constructs the freeform source by finding the optimal combination of pixels. Each pixel represents as coherent point source. Aside from the simplicity to implement the binary ACO to solve the combinatorial pixel’s source problem, another advantage of using binary ACO is the guarantee to reach the convergence state but time to convergence is uncertain. So to compensate the uncertain time of convergence, transiently chaotic neural network (TCNN) is applied in source optimization framework. The neural network has an objective to find the suitable sectors source that has benefit in improving the process window. Thus, the search space of the binary ant colony problem is reduced and leads to the faster convergence of the algorithm. The developed optimization framework does not depend on the formulation of the optical system, so it is can be easily adapted to the other lithography system such as EUV system. The lithography performance is evaluated in aerial image based exposed by the freeform source using proposed method in a PC platform. The improvement of the convergence speed is demonstrated using simple 1D L/S pattern and contact hole pattern. A more complex pattern such as five bars L/S pattern, contact layer and metal layer of 22nm SRAM node are used to demonstrate the performance of the hybrid method over reference source.

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