本研究目的為下壓力對於軟拋墊表面微突起接觸面積的影響與材料移除率預測模型應用於銅薄膜化學機械拋光製程。實際方法使用X-ray電腦斷層掃描儀進行軟拋墊的微觀形貌掃描，再透過三維分析軟體組建軟拋墊的微觀形貌模型，並結合有限元素分析軟體來評估 CMP 製程中軟拋墊與晶圓之間的接觸面積，以此對於材料移除率預測模型中實際接觸面積的參數進行修正。本研究分以三階段進行，先以9片40×40 mm^2 銅薄膜晶圓進行九組參數的CMP實驗，並透過CMP實驗結果與Preston’s Equation和材料移除率預測模型所分別計算出的結果進行驗證，得出Preston’s Equation的誤差量會落在1.42%~7.56%之間，而預測模型的誤差量會落在2.94%~5.23% 之間。再以三組下壓力分別進行30片銅薄膜晶圓CMP實驗中，透過拋光墊性能指標與晶圓MRR相關性分析得到下壓力為1 psi時，軟拋墊主要工作區域為 R_pk，當下壓力上升至2 psi與3 psi時，軟拋墊主要工作區域從 R_pk 轉變成 R_vk，此實驗結果與藉由模擬的壓縮高度所推測出的工作區域是相符合的。最後以9片8吋銅薄膜晶圓進行九組參數的CMP實驗，透過CMP實驗結果與材料移除率預測模型所計算出的結果進行驗證，誤差量會落在1.40%~3.73%之間。

This study aims to investigate down pressure on soft polishing pad asperity and develop a prediction model of material removal rate (MRR) in chemical mechanical polishing (CMP) of copper blanket film wafer. Surface topography model of soft polishing pad is performed with X-ray micro computed tomography (μ-CT) scan combined with finite element method simulation to evaluate the contact area between pad asperities and wafer during CMP process. The modifying parameters of estimated contact area can be used in the prediction model of MRR. The study has been conducted in three phases. First, MRR can be obtained from CMP experiments of nine pieces of 40×40 mm^2 (copper blanket film wafers. The prediction of MRR is verified by results of CMP experiments and results show that the error of Preston's Equation is within 1.42%~7.56% and the error of prediction model can achieve within 2.94%~5.23%. Secondly, the correlations between pad performance and MRR are discussed through CMP experiments of 30 pieces of copper blanket coupon wafers (40×40 mm^2) in three kinds of down pressure. Results show that when the down pressure is 1 psi, the main contact area is Rpk. Down pressure changes to 2 psi and 3 psi, and the main contact area is changed from Rpk to Rvk. Result of experiment is consistent with the working area inferred from results of simulated compression height. Finally, the MRR of 8-inch copper blanket obtained from CMP experiments of nine sets of parameters are compared with MRR calculated by prediction model. Results show that the error of the prediction model is within 1.40%~3.73%.

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