近年來，隨著消費市場擴大，電子產品日新月異，產品內的電子封裝可靠度越來越被受重視。本篇論文的研究目的為電子封裝的可靠性評估，而熱循環負載測試(Thermal cyclic test，TCT)是確保封裝可靠性的重要測試之一，由於使用熱循環負載測試所需時間可能長達數個月或數年，為了有效降低測試時間，我們時常使用有限單元法(Finite Element Method，FEM)代替TCT。
雖然FEM所需要的時間比TCT來的少，然而使用FEM方法還是需要花一定的時間，依照不同參數建立模型，並調整封裝的邊界條件，施加熱循環負載後，才能得到電子封裝的壽命預估數值。且不同的研究者依照相同的設計參數設計模型，可能得到的結果不盡相同。若我們可以依循著過去大量的經驗資料，建立一組有效數據庫使機器學習，讓機器快速的依照資料推估封裝壽命，不僅可以省下建立模型與驗證的時間，並且也可以避免不同研究員之間的模擬誤差。
本研究利用隨機森林(RF)機器學習演算法，對晶圓級尺寸封裝(Wafer Level Chip Scale Packaging，WLCSP)進行可靠度評估。利用FEM生成訓練數據庫，並將其與TCT實驗結果進行比較以驗證FEM模型。得到驗證後的FEM模型後，在相同的建模流程下，設計參數，生成多組不同數據量以及不同數據分布的數據庫，探討數據量以及數據分布對隨機森林(Random Forest , RF)模型的影響為何。

In recent years, due to the consumer market become larger and larger, the electronic equipment improves every day, we pay more and more attention to the reliability of electronic packaging. Our research aims to evaluate the reliability of electronic packaging, and Thermal Cycling Testing (TCT) is one of the important tests to ensure packaging reliability. The time required to perform a TCT can be as long as several months or years. In order to effectively reduce the test time, we often use the Finite Element Method (FEM) instead of TCT.
Although FEM takes less time than TCT, it still takes some time to use the FEM to get the simulation result. We build the model according to different parameters and fixed boundary conditions, and then apply the thermal cycle load on the model to obtain the prediction life value of the electronic package. Different researchers may lead to different results, even if they use the same model parameter.
If we use a large amount of validated FEM data to build a database for machine learning, then we can immediately evaluate the electronic package prediction life through machine learning methods. It not only save the time to build the model and validation, but also avoid the simulation error.
This research uses a random forest (RF) machine learning algorithm to evaluate the reliability of Wafer Level Chip Scale Packaging (WLCSP). The training database was generated by FEM and compared with the TCT experimental results to verify the FEM model. After obtaining the verified FEM model, in the same modeling process, we design feature levels to generate multiple data sets with different data volumes and different data distributions. Discuss the impact of data volume and data distribution on the Random Forest (RF) model.

摘要－－－－－－－－－－－－－－－－－－－－－－－－－－－－－I
ABSTRACT －－－－－－－－－－－－－－－－－－－－－－－－－－II
圖目錄－－－－－－－－－－－－－－－－－－－－－－－－－－－－VI
表目錄－－－－－－－－－－－－－－－－－－－－－－－－－－－－X
第１章　緒論－－－－－－－－－－－－－－－－－－－－－－－－－１
１．１　研究動機－－－－－－－－－－－－－－－－－－－－－－－１
１．２　文獻回顧－－－－－－－－－－－－－－－－－－－－－－－２
１．３　研究目標－－－－－－－－－－－－－－－－－－－－－－－４
第２章　基礎理論－－－－－－－－－－－－－－－－－－－－－－－６
２．１　錫球外型預測－－－－－－－－－－－－－－－－－－－－－６
２．２　有限單元法基礎理論－－－－－－－－－－－－－－－－－－８
２．２．１　線彈性有限單元理論－－－－－－－－－－－－－－－－８
２．２．２　材料非線性理論－－－－－－－－－－－－－－－－－－１１
２．２．３　數值方法及收斂準則－－－－－－－－－－－－－－－－１３
２．３　硬化法則－－－－－－－－－－－－－－－－－－－－－－－１４
２．３．１　等向硬化法則－－－－－－－－－－－－－－－－－－－１５
２．３．２　動態硬化法則－－－－－－－－－－－－－－－－－－－１５
２．４　CHABOCHE 模型－－－－－－－－－－－－－－－－－－－－１６
２．５　封裝結構可靠度預測方法－－－－－－－－－－－－－－－－１８
２．５．１　Coffin-Manson 應變法－－－－－－－－－－－－－－－１８
２．５．２　Darveaux 能量密度法－－－－－－－－－－－－－－－１９
２．５．３　修正型能量密度法－－－－－－－－－－－－－－－－－１９
２．６　機器學習－－－－－－－－－－－－－－－－－－－－－－－２０
２．６．１　資料分析－－－－－－－－－－－－－－－－－－－－－２１
２．６．２　過度適配(Overfitting) －－－－－－－－－－－－－－－２５
２．６．３　基礎演算法－－－－－－－－－－－－－－－－－－－－２８
２．６．４　集成學習 (Ensemble Learning) －－－－－－－－－－－３６
第３章　有限元素模型建立－－－－－－－－－－－－－－－－－－－４２
３．１　有限單元模型基本假設－－－－－－－－－－－－－－－－－４３
３．２　材料參數設定－－－－－－－－－－－－－－－－－－－－－４７
３．３　邊界條件設定－－－－－－－－－－－－－－－－－－－－－４９
３．４　溫度負載設定－－－－－－－－－－－－－－－－－－－－－４９
３．５　ANSYS® 模擬之驗證－－－－－－－－－－－－－－－－－－５０
第４章　結果與討論－－－－－－－－－－－－－－－－－－－－－－５３
４．１　訓練及測試資料－－－－－－－－－－－－－－－－－－－－５３
４．１．１　訓練資料時的注意事項－－－－－－－－－－－－－－－５３
４．１．２　資料數據庫生成－－－－－－－－－－－－－－－－－－５４
４．１．３　測試資料－－－－－－－－－－－－－－－－－－－－－６７
４．２　隨機森林回歸模型－－－－－－－－－－－－－－－－－－－６８
４．２．１　隨機森林的資料預處理－－－－－－－－－－－－－－－６８
４．２．２　隨機森林回歸模型參數最佳化－－－－－－－－－－－－７２
４．２．３　資料品質－－－－－－－－－－－－－－－－－－－－－７９
４．２．４　不同訓練資料庫對隨機森林表現之影響－－－－－－－－８４
４．３　隨機森林與人工類神經網路比較－－－－－－－－－－－－－８８
４．４　集成學習演算法－－－－－－－－－－－－－－－－－－－－９２
第５章　結論與未來工作－－－－－－－－－－－－－－－－－－－－１０４
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