NDLI: Intelligent yield and speed prediction models for high-speed microprocessors

Content Provider	IEEE Xplore Digital Library
Author	Tae Seon Kim
Copyright Year	2002
Description	Author affiliation: Sch. of Comput. Sci. & Electron. Eng., Catholic Univ. of Korea, Bucheon City, South Korea (Tae Seon Kim)
Abstract	In this paper, neural networks based intelligent yield and speed prediction models are proposed for high-speed microprocessors. Parametric neural prediction model was applied to predict manufacturing yield using wafer level electrical test (ET) data. Three layered error back propagation (BP) networks were used for modeling, and they showed only 5.4% of prediction root mean square (RMS) error and it shows 41.09% improvement results as compared to statistical prediction model using multiple regression (MR) method. For final chip speed prediction, another neural process models are developed with similar methodology, and the average speed difference between prediction results and real CPU speed was only 1.7%. Through residual analysis, it was shown that proposed speed model fits to entire speed ranges without any bias. These prediction results can be applied to scrap schemes for wafer level die sort before packaging step and it helps to reduce manufacturing cost and time by minimizing undesirable packaging cost and time.
Sponsorship	IEEE Components, Packaging, & Manuf. Technol. Soc. Electronic Components, Assemblies & Mater. Assoc
Starting Page	1158
Ending Page	1162
File Size	515306
Page Count	5
File Format	PDF
ISBN	0780374304
ISSN	05695503
DOI	10.1109/ECTC.2002.1008251
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Microprocessors Semiconductor device modeling Wafer scale integration Packaging Costs Neural networks Intelligent networks Virtual manufacturing Testing
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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