NDLI: Test time reduction of successive approximation register A/D converter by selective code measurement

Content Provider	IEEE Xplore Digital Library
Author	Goyal, S. Chatterjee, A. Atia, M. Iglehart, H. Chung Yu Chen Shenouda, B. Khouzam, N. Haggag, H.
Copyright Year	2005
Description	Author affiliation: Georgia Inst. of Technol., Atlanta, GA (Goyal, S.; Chatterjee, A.)
Abstract	This paper proposes a novel methodology for reducing the static linearity test time of SAR A/D converters. Due to the low data conversion rate and high resolution, the test time required measuring the linearity specifications such as INL and DNL in SAR A/D converters can be as high as 40% of the total A/D converter test time. The proposed method is based on the fact that the non-idealities in the code widths of the converter are correlated to and are dominated by the manufacturing variations in specific components used in the A/D converter design. Therefore, by measuring a subset of the total set of code widths that are directly affected by manufacturing variations in these components, all the code widths are estimated accurately. As opposed to prior work, the proposed approach does not use linear error models and describes a method which directly measures the code widths using a piecewise linear ramp designed to extract test information accurately from the relevant codes. The proposed method has been applied to a SAR A/D converter in production with achieved test time reduction of more than 75%
Starting Page	8
Ending Page	225
File Size	369769
Page Count	218
File Format	PDF
ISBN	0780390385
DOI	10.1109/TEST.2005.1583979
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time measurement Linearity Manufacturing Data conversion Piecewise linear techniques Circuit testing Histograms Piecewise linear approximation Semiconductor device testing Data mining
Content Type	Text
Resource Type	Article

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