A new multiple weight set calculation algorithm

Hong Sik Kim, Jin Kyue Lee, Sungho Kang

Research output: Contribution to journalConference article

10 Citations (Scopus)

Abstract

The number of weighted random patterns depends on the sampling probability of the corresponding deterministic test pattern. Therefore if the weight set is extracted from the deterministic pattern set with high sampling probabilities, the test length can be shortened. In this paper we present a new multiple weight set generation algorithm that generates high performance weight sets by removing deterministic patterns with low sampling probabilities. In addition, the weight set that makes the variance of sampling probabilities for deterministic test patterns small, reduces the number of the deterministic test patterns with low sampling probability. Henceforth we present a new weight set calculation algorithm that uses the optimal candidate list and reduces the variance of the sampling probability. The Results on ISCAS 85 and ISCAS 89 benchmark circuits prove the effectiveness of the new weight set calculation algorithm.

Original languageEnglish
Pages (from-to)878-884
Number of pages7
JournalIEEE International Test Conference (TC)
Publication statusPublished - 2001 Dec 1
EventInternational Test Conference 2001 Proceedings - Baltimore, MD, United States
Duration: 2001 Oct 302001 Nov 1

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Sampling
Set theory
High Performance
Benchmark
Networks (circuits)

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

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A new multiple weight set calculation algorithm. / Kim, Hong Sik; Lee, Jin Kyue; Kang, Sungho.

In: IEEE International Test Conference (TC), 01.12.2001, p. 878-884.

Research output: Contribution to journalConference article

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N2 - The number of weighted random patterns depends on the sampling probability of the corresponding deterministic test pattern. Therefore if the weight set is extracted from the deterministic pattern set with high sampling probabilities, the test length can be shortened. In this paper we present a new multiple weight set generation algorithm that generates high performance weight sets by removing deterministic patterns with low sampling probabilities. In addition, the weight set that makes the variance of sampling probabilities for deterministic test patterns small, reduces the number of the deterministic test patterns with low sampling probability. Henceforth we present a new weight set calculation algorithm that uses the optimal candidate list and reduces the variance of the sampling probability. The Results on ISCAS 85 and ISCAS 89 benchmark circuits prove the effectiveness of the new weight set calculation algorithm.

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