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Hung-Mao Lin

dblp:45/1530 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 93% Hardware reliability and fault tolerance · 7%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › hardware verification and test › design for testability › built-in self-test
BIST diagnosis
0.112007
Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis · IEEE Trans. Computers 2007
Electronic design automation › hardware verification and test › design for testability
built-in self-test
0.112007
Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis · IEEE Trans. Computers 2007
Electronic design automation
hardware verification and test
0.112007
Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis · IEEE Trans. Computers 2007
Electronic design automation › hardware verification and test › fault diagnosis › logic diagnosis
scan chain diagnosis
0.112007
Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis · IEEE Trans. Computers 2007

Methods — techniques the papers use, named apart from their topics

error distribution modeling · 0.1column parity row selection · 0.1LFSR-based row selection · 0.1
YearPublicationVenuePosition
2007 Cyclic-CPRS : A Diagnosis Technique for BISTed Circuits for Nano-meter Technologies
abstract
A cyclic-CPRS (column parity row selection) technique is presented to diagnose built-in self tested (BISTed) circuits, even in the presence of many unknowns and transient errors. The novel cyclic scan chains retain the transient errors and unknowns in the CUT until they are fully diagnosed. Instead of masking the unknowns, Cyclic-CPRS directly diagnoses the unknowns as if they were errors. Direct diagnosis of unknowns not only eliminates the masking circuitry but also enhances the diagnosis resolution. Experimental results show that Cyclic-CPRS is very successful even in the presence of 10% errors and unknowns. The proposed technique is especially suitable for nanometer technologies, in which transient errors and systematic defects are becoming serious problems.
Chun-Yi Lee, Hung-Mao Lin, Fang-Min Wang, Chien-Mo James Li
ASP-DAC2
2007 Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis
abstract
Column selection row parity (CPRS) diagnosis is an X-tolerant and low aliasing technique that is suitable for the BIST environment. A row selection LFSR randomly selects outputs of multiple scan chains so that unknowns can be tolerated. Column and row parities of selected outputs are observed to solve linear equations for the error positions. Experimental data show that CPRS achieves nearly perfect diagnosis, even in the presence of 1 percent unknowns. CPRS compresses the diagnosis data because only parities of circuit responses, instead of responses themselves, are observed. Two error distribution models (scattered and clustered) are developed and analyzed to show the effectiveness of CPRS. The analytical results are demonstrated to be accurate by more than 10,000 experiments
Chien-Mo James Li, Hung-Mao Lin, Fang-Min Wang
IEEE Trans. Computers2
2005 Column parity and row selection (CPRS): a BIST diagnosis technique for multiple errors in multiple scan chains
abstract
A BIST diagnosis technique is presented to diagnose multiple errors in multiple scan chains. An LFSR randomly selects outputs of multiple scan chains every scan cycle. The column parity and row parity of the selected scan outputs are observed every scan cycle and every scan unload, respectively. Compared with other techniques, which diagnose no more than 15% errors, CPRS correctly diagnoses all errors in the presence of 1% unknowns. The cost of this technique is area overhead and one additional output observed every scan cycle.
Hung-Mao Lin, Chien-Mo James Li
ITC1