隨著IC先進封裝技術的演進，如何提升封裝產品的製程良率與可靠度是一項挑戰，對製造商而言，使用實驗測試與驗證方式需耗費大量時間、金錢以及占用生產設備，近年來為了降低研發和測試成本，在生產前進行製程的預測分析成了一項可達成的目標。因此本研究將利用Moldex3D軟體針對System-in-Package封裝體進行分析，並結合完整製程架構的數位雙生概念並將其延伸應用於考慮交互作用之參數最佳化分析，進而建立一套具系統性與高實用性的製程最佳化流程。
本研究在對製程優化時使用多種模型描述封裝時的模流及變形行為，在模擬過程中採用Cross-Castro-Macosko黏度模型與Kamal’s反應動力模型，模擬不同溫度與熟化度對EMC流動行為的影響。而翹曲預測則使用後熟化求解器，結合Two-domain modified Tait P-V-T-C模型及Dual shift factor model黏彈模型，考慮固化收縮、熱收縮及黏彈行為，以更精確預測封裝體在後熟化製程中的應力鬆弛與變形現象，透過以上模型能夠更精準的進行模擬預測。本研究對包封、翹曲及von-Mises stress的數值及分布進行準確預測，並以此結果作為後續多目標優化的依據。
本研究針對多目標優化進行深入探討，分為「未考慮交互作用」與「考慮交互作用」兩個部分進行分析。首先，在未考慮交互作用的階段，選取包封數量、後熟化翹曲量及平均von-Mises stress作為關鍵品質指標，運用田口方法與灰關聯分析進行參數最佳化，並探討不同製程參數對品質特性的影響。此階段採用低解析度的三級直交表進行初步分析，結果發現各品質特性的最佳參數組並不一致，顯示多品質特性間存在取捨關係。透過灰關聯分析，綜合三項品質特性，獲得整體最佳化參數組。由於低解析度直交表容易造成主效應與交互作用的混淆，進而影響結果的準確性，因此本研究在下一階段納入交互作用考量，並使用四級解析度直交表進行更細緻的分析。
在考慮交互作用的情境下，品質特性的最佳參數組與初步分析結果有所不同，且成功降低所有品質指標的數值，驗證模擬結果亦顯示最佳化參數符合預期。分析結果顯示，單獨的後熟化時長對品質特性的影響不顯著，但其與Solder mask與Prepreg比例及模溫間存在顯著交互作用，進一步證明主效應與交互作用共同影響品質特性。因此，考慮交互作用對於製程參數的最佳化具有關鍵性。
本研究透過結合Moldex3D與田口方法，提升系統級封裝製程最佳化的準確性與可靠性，並針對製程參數間的交互作用進行詳細探討。相較於傳統忽略交互作用的研究模式，本研究填補了此領域的重要缺口。最終結果證實，納入交互作用的分析不僅能提升模擬預測的精度，也為未來機器學習模型的訓練提供參數權重的參考基礎，展現其在封裝製程預測與優化上的潛力與應用價值。
本研究亦自前端參數設計階段延伸至後段成型與熟化模擬分析，實現完整製程之虛擬建模，展現出優異的資料整合與參數診斷能力，為數位雙生技術的導入與落地應用奠定堅實基礎。藉由此虛實整合平台，不僅可於開發初期即進行封裝品質與潛在風險之預測，更能進一步應用於製程最佳化問題求解，並具備與製程監控系統及自動化控制機制整合之潛力，實現高精度、智慧化之製程決策支援與品質控制。

With the rapid advancement of IC advanced packaging, ensuring high yield and reliability has become increasingly challenging. To reduce the time and cost of experimental validation, simulation-based analysis has emerged as a key tool. This study employs Moldex3D to analyze system-in-package structure, integrating Taguchi methods for optimization, while also evaluating the impact of considering interaction effects.
The Cross-Castro-Macosko viscosity model and Kamal’s cure kinetics model are used to simulate EMC flow, and the post-mold curing solver incorporates the P-V-T-C model and viscoelastic behavior for accurate warpage prediction. Simulation results show air traps commonly occur behind large chips due to uniform flow fronts. Warpage and von-Mises stress distributions are accurately predicted to support optimization.
In this study, an initial analysis that excluded interaction effects was conducted to evaluate the influence of process parameters on quality characteristics and to determine the optimal settings. Grey Relational Analysis is applied to integrate the three response outcomes into a single grey relational grade. By normalizing and combining these grades, we achieve a balanced assessment of all quality indicators and thus determine a parameter set that simultaneously mitigates air traps, warpage and stress. However, verification results revealed noticeable discrepancies between the predicted and simulated outcomes. In contrast, the analysis incorporating interaction effects demonstrated that considering these effects improves the accuracy of optimization. These findings underscore the importance of incorporating interaction effects in the optimization process to enhance prediction reliability.

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