高可靠度時代已來臨，人們對產品的耐用度要求越來越高，進而衍生出所謂的高可靠度產品。在現今多樣化產品中，產品可靠度已成為消費者挑選產品之關鍵影響因素，而製造商也必要即時地提供顧客有關產品可靠度的資訊，以提升產品市場競爭力。為確保產品品質能符合消費者之長期需求，如何進行一有效的預燒試驗以及評估產品之壽命相關資訊，是所有生產者皆須面臨之重要決策問題。針對高可靠度產品，若存在一品質特徵值(quality characteristic, QC)，並且其衰變量與對產品壽命具有高度相關，則可藉由所收集到的衰變資料來建構衰變模型，將是一個實際可行且有效率的方法，以解決失效樣本不足的問題。如今衰變模型已廣泛地被用來評估高可靠度產品的壽命資訊，而在本篇研究中，是以Tseng & Peng (2001)研究為發想，使用Wiener過程來描述產品品質特性的連續衰變路徑，考慮正常與瑕疵產品之衰變路徑變異不同下，應用判別分析提出一種分類方法，且以最小化錯誤分類成本為目標，求得一最佳分類切點，制定出判別產品良莠之分類準則。最後，修改以Tseng & Peng (2003)中的接觸式影像感測器(CIS)中的LED進行模擬，以亮度作為其品質特徵值，並說明其完整分類過程。此研究之成果，將有助於製造商節省預燒成本與縮短預燒時間。

For high-reliability products, if there exists a quality characteristic (QC) whose degradation is highly correlated with product life, constructing a degradation model based on collected degradation data is a practical and efficient method to address the problem of insufficient failure samples. In this paper, we use the Wiener process to describe the continuous degradation path of product quality characteristics. This study is inspired by the research of Tseng & Peng (2001), considering the variance differences in the Wiener degradation model between normal and defective products, applies Discriminant Analysis to propose two classification methods, aiming to minimize misclassification cost (MC) and to determine an optimal cut-point for establishing classification criteria between normal and defective products, thus saving Burn-In costs and shortening Burn-In time. Finally, a simulation is performed using the LED of a contact image sensor from Tseng & Peng's study (2003), with brightness as its quality characteristic, and the complete classification process is illustrated.

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