半導體在生產各段製程上皆使用化學品作業，過程中不能受到一點汙染，否則會造成晶圓上的缺陷(defect)，影響良率。隨著製程演進、晶圓的線寬(Line-width)越來越小，汙染物影響製程機率越來越高，故汙染物的控制是未來半導體目標與挑戰。本研究主要以無塵室內氣體分子汙染(Airborne Molecular Contaminant, AMC)為研究主題，找出其影響良率不良之因素。
目前AMC與良率的相互影響性，單純確認關鍵設備與製程參數無法完整呈現其關聯性，有研究表示在製程中使用多種化學品的複雜製程不一定會在環境中產生高濃度AMC風險；晶片生產環境過程中高濃度AMC不一定會導致產品不良；相反地，材料及非生產過程問題也有可能會導致產品檢測失敗的機率。因此應把環境變數納入考量，而生產環境是即時性變化不容易發現，因此本研究利用失效模式與效應分析(Failure modes and effects analysis, FMEA)從過去案例找到關鍵失效原因，分析其解決方式。
失效模式與效應分析(Failure modes and effects analysis, FMEA)，是一個有系統的分析方式用來找出潛在的「失效」因子，主要目標是預防「失效」的發生，能達到最小化失誤的機率。因此選擇此方法來探討發生原因，歸納出影響整體性之環境變數，提供相關領域人員參考。
本研究透過相關文獻與業界專家訪談，利用「特性要因法」找出過去所忽略的潛在失效模式，將失效模式分等級，並說明對製造程序的影響，接著由業界專家客觀評估各個失效因子嚴重度(S)、發生度(O)、偵測度(D)的等級評估，數據結果經過公式計算求得風險優先指數(Rish Priority Number, RPN)，最後將得到的數據與專家討論出問題分析模式。

Semiconductors use chemicals in all stages of the production process. As the process evolves, and the line width of the wafer becomes increasingly smaller, the probability of pollutants affecting the process becomes accordingly higher.The present study was focused on airborne molecular contaminants (AMCs) in a clean room to determine the factors that affect the yield rate.
At present, the correlation between AMC and semiconductor yields cannot be fully defined simply based on key equipment and process parameters.Due to real-time changes in the production environment, it is not easy to immediately find the cause of such failures.
FMEA is a systematic analytical approach generally used to identify potential failure factors, with the main goal of preventing or minimizing failure occurrence. In this particular study, this approach was applied to explore the causes of failure occurrence and summarize the environmental variables affecting the overall situation.
Collecting information through expert interviews, a cause and effect analysis is conducted to identify potential failure modes that have been neglected in the past, rank the failure modes, and explain their impact on the manufacturing process. Then, industry experts objectively evaluate the risk level of each failure factor, including Severity (S), Occurrence (O), Detection, and (D) grade evaluations. The results of the data analysis are calculated using a formula to obtain the Risk Priority Number (RPN), and finally the obtained data are discussed with the experts to analyze the problem modes.

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