由於QFN(Quad Flat No-lead)構裝體的體積小、有良好的電性能與熱性能，且製程的成本與失效率低，近年來逐漸的受到市場上的重視，本文針對堆疊晶QFN構裝體為研究的對象，以產品的設計參數為考量，進行溫度循環的可靠度分析以及設計參數的敏感度分析，以提高構裝體的疲勞壽命與生產品質的穩定。
本文採用ANSYS10.0進行分析並搭配Global/Local分析法，在JEDEC規範下將堆疊晶QFN構裝體施予-40°C~125°C的溫度循環負載，而錫膏選用亞蘭德模型，然後根據Darveaux 所提出之能量法，以分析錫膏的黏塑性應變能密度分佈，並取平均值作為評估整體組件的可靠度的指標。
在進行最佳化之前，以單一因子實驗分析，檢視ANSYS模擬模型的可行性。接著以部分因子設計法篩選顯著性較大的因子，並建立反應曲面迴歸模型。再者，利用反應曲面迴歸模型結合基因演算法進行最佳化分析，其結果顯示可藉由減少晶片面積、印刷電路板厚度與熱脹膨係數，以及增加封膠熱脹膨係數皆可有效地改善堆疊晶QFN構裝體之可靠度。最後，利用區間式遺傳演算法分析，探討各參數對可靠度指標的敏感度，其大小順序為封膠熱膨脹係數>印刷電路板厚度>印刷電路板熱膨脹係數>晶片面積，未來在製程上將盡量提高較敏感參數之精度，以確保產品的品質穩定。

With the characteristics of miniature in size, good electric and thermal performance, low manufacture costs as well as low failure rate, the QFN has been paid attention gradually in the market. The stacked die QFN package is adopted to analyze the effects of the design parameters. Then the reliability analysis under the thermal cycle and the sensitivity analysis on the design parameters are conducted to enhance the fatigue life of the package and the stability of products.
ANSYS10.0 software with the Global/Local method is applied for analysis. Based on the JEDEC code, the stacked die QFN is subjected by a thermal cycle of -40°C~125°C. The viscoplastic property of solder paste is assumed to be Anand’s model. Darveaux’s energy concept is employed to analyze of the strain energy density distribution of the paste solder. Accordingly, an average value is treated as the reliability index for evaluating the package
Prior to the process of the optimal design on reliability of stacked die QFN package, one-factor-at-a-time analysis is conducted to investigate the feasibility of the ANSYS simulation model. Afterwards, the most significant factors are chosen by the fractional factorial design method and a regression model of the response surface is set up in which the genetic algorithm is introduced to obtained the optimal combination of parameters. It shows that the reliability of the QFN package can be effectively improved along with the reduction of the die size, thickness of PCB, CTE of PCB as well as the increase of CTE of mold compound. Finally, the interval genetic algorithm (IGA) is applied to analyze each parameter’s sensitivity to the reliability index. They can be ranked from the largest to the smallest as follows: CTE of mold compound, thickness of PCB, CTE of PCB and die size. It is expected to improve the accuracy of sensitive parameters in the manufacturing process so that the stability of the product quality can be ensured.

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