近年來電子產品蓬勃發展，要求產品在提升性能的同時也能縮小整體尺寸。然而隨著摩爾定律逐漸達到瓶頸，晶片的設計及整合面臨挑戰，此時扇出型封裝的出現成為一項解決方案。其中面板級扇出型封裝（Fan-Out Panel Level Packaging, FO-PLP）因具備高使用效率及低生產成本等優勢，成為市場上備受矚目的先進封裝技術。
然而，在面板級封裝的製程中，會依序經過壓模成型（Compression Molding）、後熟化（Post Mold Cure）、剝離（Debonding）等製程，這些製程將使得封裝體內部反覆升降溫，並產生翹曲。主要有兩種原因會導致翹曲發生，分別為材料間熱膨脹係數不匹配以及封膠所使用的熱固性材料環氧樹脂（Epoxy Molding Compound）因受熱後導致固化收縮。而過大的翹曲量，將會影響良率及產生可靠度問題。故本研究將利用有限元素法（Finite Element Method, FEM）來針對面板級封裝在製程下的翹曲情形進行模擬並進行分析。
本研究使用有限元素軟體進行320 mm x 320 mm 及500 mm x 500 mm面板級封裝模型的建立。考量到環氧樹脂材料固化收縮等化學現象建立上較為繁複，材料參數則是參考先前文獻經實驗驗證後的等效熱膨脹係數（Equivalent CTE）進行設定。在真實面板級封裝製程中，晶片在室溫下放置於載板上，並將其升溫至特定溫度完成環氧樹脂的固化反應。而為了使得模擬更貼近真實，故加入製程模擬法來呈現實際的物理行為。
眾多文獻結果顯示在面板級封裝製程中，其翹曲經常為非對稱圖形，而諸多原因造成此現象，以本研究的面板級封裝為例，在壓模成型時會透過將模具加熱，以熱傳導的方式使得封裝體溫度上升並進行環氧樹脂的固化，此時因為整體面積相當龐大導致中心散熱性能相較於周圍差，中心熱源集中使整體溫度分布不均，進而造成固化溫度不一致。另一方面，壓模成型的工作平台也可能有傾斜情形，使得環氧樹脂的厚度分布不均勻。
此外，相關文獻指出面板級封裝在剝離製程前須先將面板進行真空吸附，待載板完成剝離後進行真空釋放。為了模擬釋放路徑對於封裝體翹曲的影響，本研究也會對此進行探討。
即使有限元素模擬已大幅減少實驗所需成本與時間，但會根據研究者建模方式不同等因素導致模擬結果不一致。為了消弭此狀況，本研究將使用人工神經網路（Artificial Neural Network, ANN）演算法來預估面板級扇出型封裝的翹曲，其概念藉由有限元素法建立不同尺寸的模型並提取其翹曲值建立訓練資料庫，接著將這些資料庫透過機器學習訓練出一個合適的模型，而該模型將能在短時間獲得不同尺寸下的面板級封裝的翹曲量，避免模擬誤差的同時也縮減時間。

Moore's Law limitations have posed challenges to the design and integration of chips in electronic products, as they become smaller and more powerful. Fan-Out Panel Level Packaging（FO-PLP）has gained significant attention as a high-use efficiency and cost-effective solution in the advanced packaging technology market.
However, in Panel Level Packaging, sequential processes like Compression Molding, Post Mold Cure, and Debonding cause temperature fluctuations, leading to warpage in the inner package. Warpage occurs due to the Coefficient of Thermal Expansion（CTE） mismatch between different materials and the curing shrinkage of the thermosetting material, Epoxy Molding Compound（EMC）, during heating. Excessive warpage affects yield and reliability. Hence, this study aims to analyze and simulate warpage using the Finite Element Method（FEM） in the Panel Level Packaging process.
In this study, finite element software was used to model 320 mm x 320 mm and 500 mm x 500 mm PLP. Considering that the establishment of chemical phenomena such as curing shrinkage of EMC material is relatively complicated, the material parameters are set regarding the Equivalent CTE verified by experiments in previous literature. In the actual process of PLP, the chip is placed on a carrier at room temperature and then heated to a specific temperature to complete the curing reaction of EMC. Therefore, to make the simulation closer to reality, a Process Modeling Technology is added to present the actual physical behavior.
Numerous literature results indicate that in the panel-level packaging process, the warpage tends to be an asymmetric pattern, and there are many reasons for this phenomenon. Taking the PLP of this study as an example, during the molding process, the fixture will be heated causing the temperature of the package to increase, and the EMC will be cured by thermal conduction at the same time. Since the overall area is quite large for the panel, the heat dissipation performance of the center is poor compared to the peripheral area, and the concentration of the heat source in the center makes the overall temperature distribution uneven, resulting in inconsistent curing temperature. On the other hand, compression molding work platform may also have an inclined situation, which makes the non-uniform thickness distribution of the EMC.
In addition, related literature also pointed out that the PLP must vacuum the panel before the debonding process, and the path of desorption after the debonding will affect the warpage behavior of the package. Therefore, this study will also discuss the debonding path effect.
This study also aims to address the issue of inconsistent simulation results in finite element analysis caused by varying modeling methods used by different researchers. To achieve this, an Artificial Neural Network（ANN）algorithm will be employed to estimate the warpage of PLP. By building finite element models of different sizes and creating a training dataset, a suitable model can be trained using machine learning techniques to accurately predict the warpage of PLP in less time than traditional methods, eliminating errors and reducing simulation time.

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