應用倒傳遞類神經網路構建積體電路之良率模式

Abstract

晶圓的良率(yield)是積體電路產業獲利的一個重要指標，影響良率高低的因素有很多，其中晶圓上缺陷點數(defect)的多寡及缺陷點群聚(clustering)的嚴重程度又是決定晶圓良率高低的兩個重要因素。隨著晶圓製程技術的精進，晶圓面積不斷增大，使得晶圓上缺陷點出現群聚的現象，因而導致傳統的卜瓦松良率模式(Poisson yield model)預測不準確。針對這個問題中外文獻也提供了一些修正的卜瓦松良率模式(modified yield model)或其他良率模式，但是這些良率模式仍有一些缺失。因此本研究目的即是以卜瓦松良率為基礎，利用倒傳遞類神經網路(Back-Propagation Neural Network)及群聚指標(clustering index)，構建一個新的晶圓良率預測模式，此模式不僅能解決傳統卜瓦松良率模式未考慮到缺陷點群聚的問題，且不需構建複雜的統計模式，即能精確的預測出晶圓的真實良率。本研究最後以新竹科學園區某積體電路公司之實際晶圓資料來說明如何建構本研究所發展的良率模式，並證明本研究所發展之良率模式比現有其他良率模式之預測能力上更準且應用較簡單。

For integrated circuit (IC) manufacturers, the wafer yield is a key index to evaluate their profit. There are two major factors affecting the wafer yield. One factor is the number of defects on a wafer and the other factor is the degree of defect clustering. As the wafer size increases, the clustering phenomenon of the defects becomes increasingly apparent. In this case, the conventional Poisson yield model will frequently underestimate the actual wafer yield. Although many modified yield models have been developed, these models are too complicated for practical use. In this study, Back-Propagation Neural Network is employed to amend the conventional Poisson Yield model with a clustering index. A case provided by Taiwan IC Company is also presented to demonstrate the effectiveness of the proposed approach. Comparisons are also made among the conventional Poisson yield model, modified yield models and the proposed yield model.