積體電路(Integrated Circuit, IC)產業在經過四十多年來的發展，已經由早期快速的技術改變轉變成為注重製造的技術，雖然產品和製程的進步仍然是產業關注的焦點，但是與該產業相關的生產技術因素卻受到更多的重視，即如何降低生產成本和減短生產週期。在晶圓(wafer)製造的過程中，對製程控制與設備運作都需極高的精確度，只要絲毫的錯誤就會使晶圓失效，所以在製程中，使用了許多種量測方法來確保晶圓和製程的品質。當完成檢測找出缺陷晶圓後，若能在量測站點後面更進一步的加上一個診斷系統，則能更快速地找出問題所在，作故障原因的排除，達到降低成本和減短週期的目的。在整個IC製程中，微影(photolithography)製程無疑是整個IC製造流程的核心，因此本研究以模糊類神經網路(Fuzzy Neural Network, FNN)為方法，希望於晶圓圖案檢測後，建構一套微影製程機台故障診斷系統，當故障現象產生後，將之輸入該系統中，即可判斷出故障的導因，讓設備人員和製程人員能夠更快速的去維修設備或調整製程參數。經由研究結果發現，本故障診斷系統兼具類神經網路與模糊理論的優點，具有相當程度的可更新性、穩健性、正確度及解釋能力。

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中文部分
莊達人，2000年，VLSI製造技術，高立圖書有限公司
葉怡成，2001年，類神經網路模式應用與實作（七版），儒林圖書有限公司

英文部分
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