This paper presents an efficient diagnosis scheme for RAMs. Three March-based algorithms are proposed to diagnose simple functional faults of RAMs. A March-15N algorithm is used for locating and partially diagnosing faults of bit-oriented or word-oriented memories, where N represents the address number. Then a 3N March-like algorithm is used for locating the aggressor words (bits) of coupling faults (CFs) in word-oriented (bit-oriented) memories. It also can distinguish the faults which cannot be identified by the March-15N algorithm. Thus, the proposed diagnosis scheme can achieve full diagnosis and locate aggressors with (15N + 3mN) Read/Write operations for a bit-oriented RAM with m CFs. For word-oriented RAMs, a March-like algorithm is also proposed to locate the aggressor bit in the aggressor word with 4 log2B Read/Write operations, where B is the word width. Analysis results show that the proposed diagnosis scheme has higher diagnostic resolution and lower time complexity than the previous fault location and fault diagnosis approaches. A programmable built-in self-diagnosis (BISD) design is also implemented to perform the proposed diagnosis algorithms. Experimental results show that the area overhead of the BISD is small--only about 2.17% and 0.42% for 16 K
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Jin-Fu LI, Chao-Da HUANG, "An Efficient Diagnosis Scheme for RAMs with Simple Functional Faults" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 12, pp. 2703-2711, December 2007, doi: 10.1093/ietfec/e90-a.12.2703.
Abstract: This paper presents an efficient diagnosis scheme for RAMs. Three March-based algorithms are proposed to diagnose simple functional faults of RAMs. A March-15N algorithm is used for locating and partially diagnosing faults of bit-oriented or word-oriented memories, where N represents the address number. Then a 3N March-like algorithm is used for locating the aggressor words (bits) of coupling faults (CFs) in word-oriented (bit-oriented) memories. It also can distinguish the faults which cannot be identified by the March-15N algorithm. Thus, the proposed diagnosis scheme can achieve full diagnosis and locate aggressors with (15N + 3mN) Read/Write operations for a bit-oriented RAM with m CFs. For word-oriented RAMs, a March-like algorithm is also proposed to locate the aggressor bit in the aggressor word with 4 log2B Read/Write operations, where B is the word width. Analysis results show that the proposed diagnosis scheme has higher diagnostic resolution and lower time complexity than the previous fault location and fault diagnosis approaches. A programmable built-in self-diagnosis (BISD) design is also implemented to perform the proposed diagnosis algorithms. Experimental results show that the area overhead of the BISD is small--only about 2.17% and 0.42% for 16 K
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.12.2703/_p
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@ARTICLE{e90-a_12_2703,
author={Jin-Fu LI, Chao-Da HUANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Efficient Diagnosis Scheme for RAMs with Simple Functional Faults},
year={2007},
volume={E90-A},
number={12},
pages={2703-2711},
abstract={This paper presents an efficient diagnosis scheme for RAMs. Three March-based algorithms are proposed to diagnose simple functional faults of RAMs. A March-15N algorithm is used for locating and partially diagnosing faults of bit-oriented or word-oriented memories, where N represents the address number. Then a 3N March-like algorithm is used for locating the aggressor words (bits) of coupling faults (CFs) in word-oriented (bit-oriented) memories. It also can distinguish the faults which cannot be identified by the March-15N algorithm. Thus, the proposed diagnosis scheme can achieve full diagnosis and locate aggressors with (15N + 3mN) Read/Write operations for a bit-oriented RAM with m CFs. For word-oriented RAMs, a March-like algorithm is also proposed to locate the aggressor bit in the aggressor word with 4 log2B Read/Write operations, where B is the word width. Analysis results show that the proposed diagnosis scheme has higher diagnostic resolution and lower time complexity than the previous fault location and fault diagnosis approaches. A programmable built-in self-diagnosis (BISD) design is also implemented to perform the proposed diagnosis algorithms. Experimental results show that the area overhead of the BISD is small--only about 2.17% and 0.42% for 16 K
keywords={},
doi={10.1093/ietfec/e90-a.12.2703},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - An Efficient Diagnosis Scheme for RAMs with Simple Functional Faults
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2703
EP - 2711
AU - Jin-Fu LI
AU - Chao-Da HUANG
PY - 2007
DO - 10.1093/ietfec/e90-a.12.2703
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2007
AB - This paper presents an efficient diagnosis scheme for RAMs. Three March-based algorithms are proposed to diagnose simple functional faults of RAMs. A March-15N algorithm is used for locating and partially diagnosing faults of bit-oriented or word-oriented memories, where N represents the address number. Then a 3N March-like algorithm is used for locating the aggressor words (bits) of coupling faults (CFs) in word-oriented (bit-oriented) memories. It also can distinguish the faults which cannot be identified by the March-15N algorithm. Thus, the proposed diagnosis scheme can achieve full diagnosis and locate aggressors with (15N + 3mN) Read/Write operations for a bit-oriented RAM with m CFs. For word-oriented RAMs, a March-like algorithm is also proposed to locate the aggressor bit in the aggressor word with 4 log2B Read/Write operations, where B is the word width. Analysis results show that the proposed diagnosis scheme has higher diagnostic resolution and lower time complexity than the previous fault location and fault diagnosis approaches. A programmable built-in self-diagnosis (BISD) design is also implemented to perform the proposed diagnosis algorithms. Experimental results show that the area overhead of the BISD is small--only about 2.17% and 0.42% for 16 K
ER -