在記憶體DRAM製造的過程中，透過微影製程技術將設計之光罩轉印至光阻為一項至關重要的技術，但隨著線寬持續微縮，193nm浸潤式微影製程上達到物理極限，必須透過解析度增強技術(Resolution Enhancement Technology, RET)來縮小製程尺寸。本文主要使用之解析度增強技術為放置次級解析輔助特徵(Sub-Resolution Assist Feature, SRAF)進行光罩最佳化，透過差分進化演算法(Differential Evolution, DE)針對目標圖案放置輔助特徵，並使用KLA Tencor微影模擬軟體PROLITH計算空間潛像(Aerial Image, AI)，依照空間潛像量測指標計算適應性做為演算法更新之依據，而其中PROLITH所花費計算時間較長，導致光罩最佳化流程效率較低，因此本文開發出CNN適應性預測模型並整合至DE演算法，針對二維光罩Line Space Array (LSA)與Contact Hole (CH)進行最佳化，機器學習整合DE演算法在不損失太多線寬誤差的情況下，節省演算法30%計算時間，並且在曝光容忍度(Exposure Latitude, EL) = 5%下，聚焦深度(Depth of Focus, DOF)相較於傳統DE演算法提升4 nm。利用Grad-CAM手法將模型參數回推以建構輔助特徵圖案之熱力圖分析其輔助特徵潛在曝光之位置，隨後透過本文所開發之修正程序對熱點位置之輔助特徵進行局部重新放置，本文針對兩張line space array與contact hole輔助特徵放置圖案進行測試，經過修正後之輔助特徵在光阻圖案上皆沒有發生曝光情況，並且平均線寬誤差皆低於5%，另外在EL = 5%時，DOF相較於未修正前皆有明顯的提升，表示本文成功透過熱點分析修正程序修正輔助特徵曝光情況並增進其曝光品質。

In the process of DRAM manufacturing, transferring the designed mask to the photoresist through the lithography process is a crucial technology, but as the line width continues to shrink, 193nm immersion lithography reaches the physical limit, the process size must be reduced through resolution enhancement technology (RET). The RET mainly used in this paper is placing the sub-resolution assist feature (SRAF) for mask optimization, and use the differential evolution (DE) algorithm to place the SRAF for the target pattern, use KLA Tencor PROLITH to calculate the aerial image, and derive the fitness according to the measurement indicator of the aerial image for the update of DE algorithm, but PROLITH spends a long time to calculate, which leads to the low efficiency of mask optimization, so this paper develops a CNN fitness prediction model and integrates to DE algorithm to optimize the 2D mask Line Space Array (LSA) and Contact Hole (CH). Machine learning and DE integration saves 30% calculation time without losing much line width error, and the depth of focus (DOF) increased 4 nm compared with the traditional DE algorithm under the exposure latitude = 5%. Using Grad-CAM for constructing heat map of the SRAF pattern to analyze the hotspots, and then relocate the SRAF at the hotspot position through the correction procedure developed in this paper. Use two LSA and two CH patterns for the correction procedure testing. SRAFs are not exposed on the resist after correction, and the average line width error is less than 5%. In addition, DOF has been improved compared with the uncorrected, indicating that this paper successfully corrected the SRAF exposure and improved its aerial image quality through the hotspot analysis correction procedure.

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