Using syndrome compression for memory built-in self-diagnosis

J. F. Li; R. S. Tzeng; C. W. Wu

Using syndrome compression for memory built-in self-diagnosis

J. F. Li, R. S. Tzeng, C. W. Wu

電機工程學系

研究成果: 會議貢獻類型 › 會議論文 › 同行評審

11 引文斯高帕斯（Scopus）

摘要

Due to the pin-count limitation, built-in self-diagnosis (BISD) for embedded RAMs usually exports diagnosis information serially, which results in the overhead of diagnostic time. This paper describes a tree-based compression technique for word-oriented memories. The technique can speed up the transmission of diagnosis data from the embedded RAM with BISD support. By using a simplified Huffman coding scheme and partitioning each 256-bit Hamming syndrome into fixed-size symbols, the average compression ratio is reduced to about 10%, assuming 16-bit symbols. The proposed compression technique reduces the time for diagnostic test, as well as the tester storage requirement.

原文	???core.languages.en_GB???
頁面	303-306
頁數	4
出版狀態	已出版 - 2001
事件	2001 International Symposium on VLSI Technology, Systems, and Applications, Proceedings - Hsinchu, Taiwan 持續時間: 18 4月 2001 → 20 4月 2001

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???event.eventtypes.event.conference???	2001 International Symposium on VLSI Technology, Systems, and Applications, Proceedings
國家/地區	Taiwan
城市	Hsinchu
期間	18/04/01 → 20/04/01

引用此

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AB - Due to the pin-count limitation, built-in self-diagnosis (BISD) for embedded RAMs usually exports diagnosis information serially, which results in the overhead of diagnostic time. This paper describes a tree-based compression technique for word-oriented memories. The technique can speed up the transmission of diagnosis data from the embedded RAM with BISD support. By using a simplified Huffman coding scheme and partitioning each 256-bit Hamming syndrome into fixed-size symbols, the average compression ratio is reduced to about 10%, assuming 16-bit symbols. The proposed compression technique reduces the time for diagnostic test, as well as the tester storage requirement.

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Using syndrome compression for memory built-in self-diagnosis

摘要

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