為滿足近年來電子產品的快速發展，勢必要縮短電子封裝研發量產時間。本篇論文著重於電子封裝的可靠度分析與機器學習之結合應用。目前，電子封裝的壽命分析法有兩種，第一個為業界常用的溫度循環負載測試(Thermal cyclic test，TCT)，第二個是使用有限單元法之模擬分析。
雖然模擬已證實可取代可靠度真實實驗來減少冗長的電子封裝測試時間，然而每當有新數據產生，這兩種電子封裝可靠度測試方法必須藉由繁複的流程來重新建立一組新的模型或實驗。若我們能夠結合過去大量的經驗資料建立一組數據庫搭配機器學習，如此便可省去那些繁雜的製作程序避免實驗與模擬誤差，讓電腦自行做出快速又精準的預測，同時又可減少對可靠度測試及硬體所需的昂貴費用，可謂經濟又實惠。
本研究目的是希望藉由可靠的數據庫搭配機器學習快速預測封裝壽命來追求整體最大效益。本研究結合機器學習提供新穎的封裝壽命預測方法，針對晶圓級尺寸封裝(Wafer Level Chip Scale Packaging，WLCSP)的壽命資料庫搭配隨機森林演算法，目的在於短時間內得到準確的封裝壽命預估。由於溫度循環負載測試的資料不易取得，資料量不足以用於機器學習。因此，本研究的訓練數據集是使用可靠的模擬壽命產生足夠資料點以利進行機器學習應用。最終本研究證實此方法確實可行，快速且精準的取得與驗證模擬相同的預估壽命。

For the sake of the rapid growth of electronic products, it is imperative to shorten the development time of electronic packaging. This research applies machine learning to do the reliability analysis of packaging. nowadays, there were two types of reliability analysis methods for electronic packages. The first one was the board level thermal cyclic test (TCT) which is the commonly used in the industry, and the second was the simulation analysis using the finite element method.
Packaging simulation has been proved to replace reliability experiment to reduce time. However, both of simulation and experiment have to re-build a set of new model for the unknown electronic package through the complicated processes. If we combine a large amount of experienced data to build a database with machine learning, then we can pass the complicated procedure to avoid experimental and simulation errors. But also let the computer make fast and accurate predictions.
The purpose of this study is using machine learning to get quickly packaging lifetime prediction. This study applies machine learning to provide a novel packaging lifetime prediction method. The Wafer Level Chip Scale Packaging (WLCSP) lifetime database applied with random forest algorithm to obtain accurate packaging lifetime prediction in a short time. Since the amount of experimental data is not sufficient for machine learning, the training dataset of this study will use reliable FEM model to generate enough data points for machine learning.This study confirms that this method is indeed feasible and achieves the accurately lifetime prediction as the testing data.

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