标题: 应用倒传递类神经网路构建积体电路之良率模式
Predicting the Wafer Yield Using Back-Propagation Neural Network
作者: 李静宜
Ching-Yi Lee
唐丽英
Lee-Ing Tong
工业工程与管理学系
关键字: 良率模式;积体电路;缺陷点;群聚现象;群聚指标;倒传递类神经网路;晶圆;yield model;integrate circuit;defect;cluster;clustering index;Back-Propagation Neural Network;wafer
公开日期: 2002
摘要: 晶圆的良率(yield)是积体电路产业获利的一个重要指标,影响良率高低的因素有很多,其中晶圆上缺陷点数(defect)的多寡及缺陷点群聚(clustering)的严重程度又是决定晶圆良率高低的两个重要因素。随着晶圆制程技术的精进,晶圆面积不断增大,使得晶圆上缺陷点出现群聚的现象,因而导致传统的卜瓦松良率模式(Poisson yield model)预测不准确。针对这个问题中外文献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,但是这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为基础,利用倒传递类神经网路(Back-Propagation Neural Network)及群聚指标(clustering index),构建一个新的晶圆良率预测模式,此模式不仅能解决传统卜瓦松良率模式未考虑到缺陷点群聚的问题,且不需构建复杂的统计模式,即能精确的预测出晶圆的真实良率。本研究最后以新竹科学园区某积体电路公司之实际晶圆资料来说明如何建构本研究所发展的良率模式,并证明本研究所发展之良率模式比现有其他良率模式之预测能力上更准且应用较简单。
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.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT910031010
http://hdl.handle.net/11536/69766
显示于类别:Thesis