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Test Data Compression Using Selective Sparse Storage

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Abstract

For today’s very large scale integrated circuits, test data volume is recognized as a major contributor to the cost of manufacturing testing. Test data compression addresses this problem by reducing the test data volume without affecting the overall system performance. This paper proposes a new test data compression technique using selective sparse storage. Test sets are partitioned into four kinds of blocks of uniform length, all-0 blocks, all-1 blocks, sparse blocks and characterless blocks. Blocks are encoded appropriately based on the occurrence of them. They are encoded into 0, 10, 110 + number of the sparse bits + locations of all the sparse bits, and 111 + the block itself, respectively. Two algorithms are proposed for how to select the sparse blocks from test sets. A theoretical analysis for our selective sparse storage shows the new compression technique outperforms the conventional test data compression approaches. Experimental results illustrate the flexibility and efficiency of the new method, which is consistent with the theoretical analysis.

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Authors and Affiliations

  1. School of Computer and Communication, Hunan University, Changsha, China

    Ling Zhang, Ji-shun Kuang & Zhi-qiang You

  2. School of Computer Science, Huangshi Institute of Technology, Huangshi, China

    Ling Zhang

Authors
  1. Ling Zhang
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  2. Ji-shun Kuang
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  3. Zhi-qiang You
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Corresponding author

Correspondence to Ji-shun Kuang.

Additional information

Responsible Editor: S. Hellebrand

This research is supported by National Natural Science Foundation of China (NSFC) under grant No. 60773207 and 60673085

Appendix

Appendix

Test application time computation

The frequency of ATE is denoted as f ATE and the frequency of SoC scan is assumed as f scan . The test application time for the case of no compression(Tp) depends on the total number of input bits(−TD|) and ATE’s working frequency[31] where \( Tp = |{T_D}|/{f_{{ATE}}} \).

For selective sparse storage, the test application time T sparse depends on the occurrence frequency of each type block in every test pattern. We assume the number of pattern is N, the occurrence frequency of block type c j in pattern i is \( {f_{{i,{c_j}}}} \), and the corresponding length is \( {L_{{i,{c_j}}}} \). When decoder input \( {L_{{i,{c_j}}}} \)is entered into FSM, \( {L_{{i,{c_j}}}} \)ATEs and K system clocks are needed for applying Kj bits into scan chain. So, T sparse is computed by

$$ \begin{array}{*{20}{c}} {{T_{{sparse}}}} \hfill & { = \sum\limits_{{i = 1}}^N {\sum\limits_{{j = 1}}^4 {\left( {\frac{{{L_{{i,{c_j}}}}}}{{{f_{{ATE}}}}} + \frac{{{K_j}}}{{{f_{{scan}}}}}} \right)} } \times {f_{{i,{c_j}}}}} \hfill \\ {} \hfill & { = \sum\limits_{{i = 1}}^N {\left( {{f_{{i,{c_1}}}} \times \left( {\frac{1}{{f{}_{{ATE}}}} + \frac{{{K_j}}}{{{f_{{scan}}}}}} \right) + {f_{{i,{c_2}}}} \times \left( {\frac{2}{{f{}_{{ATE}}}} + \frac{{{K_j}}}{{{f_{{scan}}}}}} \right) + {f_{{i,{c_3}}}} \times \left( {\frac{{\left( {3 + {N_S} \times {{\log }_2}{K_j} + {{\log }_2}{N_s}} \right)}}{{f{}_{{ATE}}}} + \frac{{{K_j}}}{{{f_{{scan}}}}}} \right) + {f_{{i,{c_4}}}}\left( {\frac{{\left( {3 + {K_j}} \right)}}{{f{}_{{ATE}}}} + \frac{{{K_j}}}{{{f_{{scan}}}}}} \right)} \right)} } \hfill \\ \end{array} $$

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Zhang, L., Kuang, Js. & You, Zq. Test Data Compression Using Selective Sparse Storage. J Electron Test 27, 565–577 (2011). https://doi.org/10.1007/s10836-011-5234-7

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