کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
384347 660846 2012 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel design of wafer yield model for semiconductor using a GMDH polynomial and principal component analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A novel design of wafer yield model for semiconductor using a GMDH polynomial and principal component analysis
چکیده انگلیسی

According to previous studies, the Poisson model and negative binomial model could not accurately estimate the wafer yield. Numerous mathematical models proposed in past years were very complicated. Furthermore, other neural networks models can not provide a certain equation for managers to use. Thus, a novel design of this paper is to construct a new wafer yield model with a handy polynomial by using group method of data handling (GMDH). In addition to defect cluster index (CIM), 12 critical electrical test parameters are also considered simultaneously. Because the number of input variables for GMDH is inadvisable to be too many, principal component analysis (PCA) is used to reduce the dimensions of 12 critical electrical test parameters to a manageable few without much loss of information. The proposed approach is validated by a case obtained in a DRAM company in Taiwan.


► A novel design of this paper is to construct a new wafer yield model by using GMDH.
► PCA is used to reduce the dimensions of 12 critical electrical test parameters.
► The proposed model can help IC manufacturers to evaluate the process capability.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 8, 15 June 2012, Pages 6665–6671
نویسندگان
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