伴隨著人類對電子產品日益增長的需求，電子封裝逐漸向著微型化、高密度的方向發展。本篇論文所探討的晶圓級尺寸封裝(Wafer Level Chip Scale Package, WLPCSP)，其最顯著的特點就在於能夠有效減小封裝的體積。WLSCP自2000年以來經過長遠而迅速的發展,便成為了目前市場上主流的電子封裝形式之一。有別於早期傳統封裝技術，其基本的工藝思路是直接在晶圓上進行封裝製程，最後切割晶圓直接得到封裝成品。
電子封裝的可靠性評估便是本篇論文的研究目的。對於WLCSP，晶片通過錫球和基板進行連接，在實際工作期間需要經受一定週期的高低溫溫度循環，器件中不同材料間的熱膨脹係數(CTE)的失配導致錫球產生了一定的熱應力和熱應變，造成了應變能的積累，最終導致了封裝的失效。所以說，錫球的熱-機械可靠性對封裝可靠度評估的影響尤為顯著。傳統封裝可靠性評估的重要手段之一便是熱循環負載測試(Thermal cyclic test, TCT)，但由於每一次的熱循環負載測試會花費數月之久，從而大大增加時間成本，降低產品研發速率，不利於產品的市場化競爭。為了降低時間成本，一般會於封裝研發過程中採用有限單元模擬的方法來代替TCT。
雖然有限單元法(FEM)相較於傳統TCT大大地降低了時間成本，但是另一方面FEM並沒有傳統實驗方法統一規定的流程，不同研究人員由於其自身能力以及建模思路和側重不同，造成相當程度上的模擬誤差。為解決這一問題，並進一步減少FEM中建模與驗證的時間成本，本論文研究利用核嶺回歸（KRR）機器學習演算法，對晶圓級尺寸封裝進行可靠度評估。同時進一步用聚類（Cluster）算法解決在大規模數據集下，KRR機器學習演算法的CPU時間成本問題

With the increasing demand for electronic products, electronic packaging is gradually developing in the direction of miniaturization and high density. The most significant advantage of Wafer Level Chip Scale Package (WLCSP) is that it can effectively reduce the volume of the package. The basic process idea is to directly perform the packaging process on the wafer, and finally cut the wafer to directly obtain the packaged product.
The reliability evaluation of electronic packaging is the research purpose of this paper. For WLCSP, the wafer is connected to the substrate through solder balls and needs to undergo a certain period of high and low temperature cycles during actual work. The mismatch of the coefficient of thermal expansion (CTE) between different materials leads to the failure of the package. Therefore, the thermal-mechanical reliability of the solder ball has a particularly significant impact on the reliability evaluation of the package. One of the important methods of traditional package reliability evaluation is the thermal cycle load test (TCT). However, each thermal cycle load test will take several months, which greatly increases the time cost, reduces the product development rate, and it is not conducive to product market competition. In order to reduce the time cost, the finite element method (FEM) is generally used to replace TCT in the process of packaging development.
Although FEM greatly reduces the time cost compared with TCT, FEM does not have a unified procedure for the traditional experimental method. Different researchers due to their own abilities and different modeling ideas and emphasis, cause the considerable degree of simulation error. In order to solve this problem and further reduce the time cost of modeling and verification in FEM, this paper studies the use of Kernel Ridge Regression (KRR) machine learning algorithm to evaluate the reliability of wafer-level packaging. At the same time, the cluster algorithm is further used to solve the CPU time cost problem of the KRR machine learning algorithm under large-scale data sets.

摘要---------------------------------------I
Abstract----------------------------------II
圖目錄-------------------------------------V
表目錄------------------------------------VII
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--------------------------------5
第2章 基礎理論-----------------------------7
2.1錫球外型預測----------------------------7
2.2有限單元法的基本理論---------------------9
2.2.1線彈性有限單元理論--------------------10
2.2.2材料非線性理論------------------------12
2.2.3數值方法及收斂準則--------------------14
2.3CHABOCHE 模型--------------------------15
2.4封裝結構可靠度預測方法-------------------17
2.4.1Coffin-Manson 應變法------------------17
2.4.2Darveaux 能量密度法-------------------17
2.4.3修正型能量密度法-----------------------18
2.5機器學習概述-----------------------------19
2.6機器學習流程與算法------------------------20
2.6.1 機器學習流程--------------------------20
2.6.2 資料預處理----------------------------21
2.6.3 KRR算法原理---------------------------23
2.6.4 K-means算法原理-----------------------28
2.7機器學習模型超參數優化--------------------32
2.7.1 網格搜索法----------------------------32
2.7.2 隨機搜索法----------------------------33
2.7.3 貝葉斯優化法--------------------------34
第3章 有限元素模型建立-----------------------39
3.1有限單元模型基本假設----------------------40
3.2材料參數設定-----------------------------45
3.3邊界條件設定-----------------------------47
3.4溫度負載設定-----------------------------47
3.5ANSYS®模擬之驗證-------------------------48
第4章 結果與討論----------------------------50
4.1訓練及測試資料---------------------------50
4.2核嶺回歸模型-----------------------------53
4.2.1基於網格搜索的核嶺回歸的超參數選定-------53
4.2.2基於網格搜索的核嶺回歸模型訓練結果-------56
4.2.3基於貝葉斯優化的核嶺回歸的超參數選定-----60
4.2.1基於貝葉斯優化的核嶺回歸模型訓練結果-----60
4.3K-MEANS與核嶺回歸混合模型----------------67
第5章 結論與未來工作------------------------73
參考文獻-----------------------------------75

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