Test data compression using Lingering Component Reduction technique for system-on-a-chip applications

Chakrapani, K.; Muthaiah, R.

doi:10.1016/j.compeleceng.2017.09.002

Cited by 2 publications

(2 citation statements)

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“…Therefore, the optimization process performs well by searching a suitable optimal solution utilizing those design parameters as variables [5,15]. Sometimes, the process tends to be inadequate due to long search process [2,16]. However, the later advancements in VLSI played huge attention on design objectives and constraints in optimizing the problem of test data volume reduction with better compression ratio [17,20].…”

Section: Introductionmentioning

confidence: 99%

“…With the help of congestion-awareness, disco has been exposed in the appraisal to integrate the compression solution of memory sequence, dramatically reduce a compression overload of isolated methods as well as substantially increase the efficiency of data moving as well as storage. Chakrapani et al [2] have presented an effective method of compression of test data depending on stored set named Lingering Component Reduction (LCR) Code. The test set covers a huge count of don't-care that may be used to develop the TDC.…”

Section: Introductionmentioning

confidence: 99%

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On Reducing Test Data Volume for Circular Scan Architecture Using Modified Shuffled Shepherd Optimization

et al. 2021

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In this manuscript, a novel test data compression (TDC) system is proposes for reducing a test data volume (TDV) for circular scan (CS) architecture. A modified version of meta-heuristic population based optimization approach, hence it is called shuffled shepherd optimization (SSO) and it is used to optimize the conflicting bit minimization (CBM) problem. In CS framework, TDC is reached by upgrading only the conflicting bits among template pattern, real test pattern. Reduced test data volume and test application time are achieved by diminishing the Hamming distance between the currently captured response and the subsequent test vector. The CBM issue is formulated by a traveling salesman problem (TSP). Here, the test vectors are assumed by cities, the HD among test vectors a pair is assumed by distance of interurban. A modified version of the SSO algorithm called as MSSO is applied in combination using mutation operator for solving the issue of CBM. Experimental outcomes display the proposed TDC method using MSSO achieves an average development of 8.36% in compression ratio (CR) as well as 6.77% in TAT is attained by reducing a TDV. This proposed approach outperforms other state-of-art test data compression schemes by providing improved CR, TAT reduction and TDV reduction.

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