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Reseeding-based Test Set Embedding with Reduced Test Sequences

Reseeding-based Test Set Embedding with Reduced Test Sequences. E. Kalligeros 1, 2 , D. Kaseridis 1, 2 , X. Kavousianos 3 and D. Nikolos 1, 2 1 Computer Engineering & Informatics Dept., University of Patras, Greece 2 Research Academic Computer Technology Institute, Greece

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Reseeding-based Test Set Embedding with Reduced Test Sequences

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  1. Reseeding-based Test Set Embedding with Reduced Test Sequences E. Kalligeros1, 2, D. Kaseridis1, 2, X. Kavousianos3 and D. Nikolos1, 2 1Computer Engineering & Informatics Dept., University of Patras, Greece 2Research Academic Computer Technology Institute, Greece 3Computer Science Dept., University of Ioannina, Greece

  2. Motivation • Core-oriented way of designing contemporary Systems on Chip (SoCs) is placing a severe burden on the Automatic Test Equipment (ATE) • This designing style leads to larger and denser circuits that require greater test data volumes and longer test-application times

  3. Motivation • IP protected systems (unknown structure): ways of test • Deterministic test set generation: TPG for precisely reproduction of test set • Test-pattern compression: Compressed vectors stored on tester and decompressed on chip with small built-in circuit • Test set embedding: Encodes test patterns in longer sequences • Therefore Test-set embedding techniques with short test sequences are required

  4. Outline of the presentation • Seed-selection algorithm • Test-sequence reduction scheme • Evaluation-Comparisons

  5. Outline of the presentation • Seed-selection algorithm • Test-sequence reduction scheme • Evaluation-Comparisons

  6. Seed-selection algorithm • We consider the classical LFSR-based reseeding scheme: LFSR of length n and a Vector Counter • The algorithm receives as input the size L of the window that each seed expands to and a set of test cubes T • For determining a new seed the seed-algorithm makes uses of the well-known concept of solving systems of linear equations (i.e. assuming Gauss-Jordan elimination) [Koenemann ETC’91] • If each bit of the seed of the LFSR were replaced by a binary variable then each one of the L windowstates would be a symbolic vector

  7. Seed-selection algorithm • The algorithm examines all possible linear systems and chooses one to solve • If no system can be solved for any of the remaining test cubes the above procedure stops and a new window has been determined Since at each step of algorithm, linear systems corresponding to more than one test cubes will be solvable at more than one positions of the window, as set of heuristics is needed for selecting the system that will be actually solved

  8. Seed-selection algorithm Five heuristics are utilized • Three basic of the algorithm proposed in [Kalligeros et al, ISQED’02] • Two new heuristics that significantly refine the selection process (improved encoding ability of proposed algorithm)

  9. Seed-selection algorithm • First criterion At each step of the algorithm the system that is actually selected to be solved is the one that leads to the replacement of the fewest variables in the initial window state • Second criterion The cube containing the maximum number of defined bits is the first to be selected for each new seed • Third criterion Test cube set is split in two different groups: • High priority: Cubes that contain many defined bits • Low priority: Rest cubes of set T At each step of the algorithm, cubes of 1 are targeted first and only if no such cube can be covered then the cubes of 2 are targeted

  10. Seed-selection algorithm • Although the application of the previous heuristics leads to good result in terms of resulting seed volumes they are not elaborate enough • At each step of the algorithm there are many solvable systems that require the same minimum number of variables to be replaced • The choice among them is a random choice that does not improve the efficiency of the algorithm. For that reason two new criteria are introduced

  11. Seed-selection algorithm • Fourth criterion From the systems that require the elimination of the same minimum number of variables, those corresponding to the test cubes with the maximum number of defined bits are preferred • Fifth criterion In the case the fourth criterion gives more than one cube then the systems corresponding to the cube which can be covered at the fewest positions within the L-state window are preferred The addition of those two criteria improves significantly the encoding ability of the seed-selection algorithm resulting in 25% smaller seed sets on average

  12. Outline of the presentation • Seed-selection algorithm • Test-sequence reduction scheme • Evaluation-Comparisons

  13. Test-sequence reduction scheme • Seed-selection algorithm assumes a L state window for every seed • Only some of these states of each window are actually being used for reproducing a test cube of T • If the last state of a window is not a useful one then all states from the last useful one to the last state of each window are redundant • As more seeds are selected by the algorithm, more variables are replaced and thus fewer cubes are encoded in each seed  more useless states at the end of these windows

  14. Test-sequence reduction scheme • This problem is much more important in the case of test set embedding since the increased number of seeds leads to much longer test sequences • Most efficient way (in terms of test-sequence length): “Maximum reduction” approach which stops the expansion of each seed after the clock cycle in which the last useful state was generated by the LFSR  number of redundant states will be zero Problem: Vector Counter has to be initialized with a different value at each reseeding  Excessive test data storage

  15. Test-sequence reduction scheme • An intermediate approach has to be followed Proposed approach: • Each window is segmented into a number (m) of equal-sized groups of LFSR states • The useful states of the window are included in the first k segments and thus the remaining m-k segments contain redundant states and can be dropped during test generation • Segment-Vectors Counter: Generate the states of each group (counts from Segment_Size-1 to 0)

  16. Test-sequence reduction scheme

  17. Test-sequence reduction scheme • With proper selection of Segment_Size, the distance between the last useful state and the end of last useful segment can be minimized • The Upper limit of number of eliminated redundant states is the one of the “Maximum reduction” approach A low hardware-overhead solution for generating the useful segment of each window is required

  18. Test-sequence reduction scheme Rearrangement technique Main idea: • Order the seeds according to the number of useful segments • if these volumes for every two successive windows differs at most by one Only a single extra bit per seed is needed for indicating this relation. Extra bit=0  Same number of useful segment Extra bit=1  One segment difference Problem: The difference in the number of useful segment between two successive (ordered) seeds may be greater than one Solution: Some useless segments should be maintained in the window with the smaller number of useful segments

  19. Test-sequence reduction scheme Example of rearrangement technique

  20. Test-sequence reduction scheme Load Counter : Down counter that maintains the necessary number of segments for each window • Initially loaded with maximum number of segments required among all windows • The value of the extra bit of each window determines the operation of the counter Extra bit=1  Load counter decreases by one Extra bit=0  Load counter stays unchanged

  21. Test-sequence reduction scheme In order to actually control the generation of the patterns of a window, two counters are need: • Segment-Vectors Counter : Counts from Segment_Size-1 to 0 and controls the generation of the vectors of a segment • Segment Counter : Counts the requiring number of segments in each window and thus initialized with the value of Load Counter at the beginning of a new window These two counters constitute a combined counter Segment Counter’s value is decreased by one every time Segment-VectorsCounter signals that Segment_Size patterns have been applied to the CUT

  22. Test-sequence reduction scheme • New window generation: • Next stored seed is loaded into LFSR • Segment Counter is loaded with current Load Counter’s Value • Load Counter is triggered (or not) according to value of seed’s stored extra bit • Segment-Vectors Counter is enabled

  23. Test-sequence reduction scheme The previous process is repeated until all the seeds have been expanded to their corresponding vector-segments Extra hardware overhead of Control Logic: • The combination of Segment and Segment-Vectors Counters is equal to the Vector Counter of the classical reseeding approach • Therefore H/W overhead is only the Load Counter

  24. Outline of the presentation • Seed-selection algorithm • Test-sequence reduction scheme • Evaluation-Comparisons

  25. Evaluation-Comparisons • First Important issue: Choice of window size L Affects both number of final selected seeds and the length of the resulting test sequences As ↑ L  # of required seeds ↓ But # of required seeds is gradually saturated as ↑ L resulting in larger test sequences with small benefits for the # of seeds

  26. Second Important issue: Choice of Segment_Size Can becalculated easily by a very-fast brute force procedure This procedure tests all possible segment_size values and chooses the best one with respect to: Number of allowed redundant segments in seed’s windows Number of redundant vectors in the last useful segment of each window Running time on Pentium 2.6 GHz workstation was less than 2 sec Evaluation-Comparisons

  27. Evaluation-Comparisons Results of proposed technique: ()Reduction of test-sequence lengths of segmentation-rearrangement technique compared to unreduced sequences of the seed-selection algorithm () Percentage of test-sequence reduction over those of “maximum reduction” technique

  28. Evaluation-Comparisons Comparison with Twisted-Ring Counters approach Resulting Test Sequences: () Twisted-Ring with Mintest () Twisted-Ring with Atalanta () Proposed technique Proposed technique

  29. Evaluation-Comparisons Comparison with Twisted-Ring Counters approach Test Data Storage: () ROM bits of Twisted-Ring with Mintest () ROM bits of Twisted-Ring with Atalanta () ROM bits of Proposed technique

  30. Conclusions • Segmentation-rearrangement techniques achieves on average 20,17% reduction in final test-sequence lengths having on average 74,76% of the reduction of the “maximum reduction” approach • The combination of the two proposed techniques (seed-selection algorithm + segmentation-rearrangement techn.) requires on average 85,39% and 74,04% fewer test vectors than Twisted-Ring Counter for Mintest and Atalanta cases respectively • On average, compared to Twisted-Ring Counters our technique requires 27,79% and 39,94% less test-data storage for Mintest and Atalanta cases respectively

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