Study on Expansion of Convolutional Compactors over Galois Field

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概要

• 論文の詳細を見る

• Convolutional compactors offer a promising technique of compacting test responses. In this study we expand the architecture of convolutional compactor onto a Galois field in order to improve compaction ratio as well as reduce X-masking probability, namely, the probability that an error is masked by unknown values. While each scan chain is independently connected by EOR gates in the conventional arrangement, the proposed scheme treats q signals as an element over GF(2q), and the connections are configured on the same field. We show the arrangement of the proposed compactors and the equivalent expression over GF(2). We then evaluate the effectiveness of the proposed expansion in terms of X-masking probability by simulations with uniform distribution of X-values, as well as reduction of hardware overheads. Furthermore, we evaluate a multi-weight arrangement of the proposed compactors for non-uniform X distributions.

• （社）電子情報通信学会の論文
• 2008-03-01

著者

• ARAI Masayuki

  Tokyo Metropolitan University

• IWASAKI Kazuhiko

  Tokyo Metropolitan University

• Fukumoto Satoshi

  Tokyo Metropolitan Univ. Hachioji‐shi Jpn

• Fukumoto Satoshi

  Graduate School Of Engineering Tokyo Metropolitan University

• Iwasaki K

  Tokyo Metropolitan Univ. Tokyo Jpn

• Iwasaki Kazuhiko

  Graduate School Of Engineering Tokyo Metropolitan University

• Iwasaki Kazuhiko

  Faculty Of System Design Tokyo Metropolitan University

• Arai Masayuki

  Tokyo Metropolitan Univ. Tokyo Jpn

• ARAI Masayuki

  Graduate School of Engineering, Tokyo Metropolitan University

• Fukumoto Satoshi

  Graduate School Of Engineering Hiroshima University

• Arai Masayuki

  Graduate School Of Engineering Tokyo Metropolitan University

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