its a survey on the Topic : Test vector compression in Digital Testing I hope any one use it to ask God to my parents ,thanks

1. Presented to:
Prof.Dr. : Ihab Talkhan
by
Eng. Amr Abd El latief Abd El Al
Eng. Mohammad Ahmed Hamed

2. Agenda
Introduction
Benefits of data Compression
Why does compression works (on testing vector)
Test vector compression schemes Categories
Data compression techniques
Hufman Coding method

3. Agenda
Geometric methods.
Conclusion
References

4. Introduction
• Test Levels: System Testing, Chip Testing

Board Testing.
• Increasing integration density results in:
•
larger designs
• more scan cells
• more faults.

5. Introduction (Cont.)
• external testing: involves storing all test vectors and test
response on an external tester.
• Tester Challenges:
•
•
•
•
limited speed
memory,
I/O channels
Bandwidth.

6. Introduction (Cont.)
 Overcoming approaches :
stand-alone BIST:

But it has a low fault detection probability due to
RPR Faults
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hybrid BIST:
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use a combination of BIST and test vectors
test data compression. (Described Here)

7. DataCompression Benefits
• First,
it reduces the amount of data stored on the tester,
•Second,
it can reduces the test time for a given test data bandwidth

8. TestVectors Characteristics


Test vectors are highly compressible because typically
only 1% to 5% of their bits are specified (care)bits. The
rest are don’t-cares.
because faults are structurally related in the circuit test
cubes also tend to be highly correlated

9. The compression technique categories
1 . Run-length based codes.
2 . Dictionary codes.
3 . Statistical codes (Huffman coding)
4 . Linear decompression.
5 . Broadcast scan methods
6 . Geometric methods.

10. Run-length-based codes
• simple form of data compression
• runs of data means line of data
• Good method for data that contains many such runs

11. Run-length-based codes
 Note:
Careful ordering of the test cubes maximizes the number of 0s in the
difference vectors, thereby improving the effectiveness of run-length
coding.


12. Dictionary codes.
1 -partitions the original data into n-bit symbols.
2 - Uses a dictionary to store each unique symbol.
3 - encoding each n-bits using a b-bit code word
corresponds to the symbol’s index in the dictionary
(b<n)

13. Dictionary codes.

14. Hoffman Method Example

15. Huffman Algorithm

initialization : put all symbols on a list sorted according to their frequency count


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repeat until the list has only one symbol left:
from the list pick two symbols with the lowest frequency counts
form a Huffman sub-tree that has these two symbols as a child nodes
and create a parent node
Assign the sum of the children's freuency counts to the parent and insert it into the list such that
order is maintained


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Delete the children from the list
Assign a code word for each leaf based on the path from the root

16. Apply on the Test Vector pattern

17. Hoffman Method Implementation

18. Linear decompression
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The technique is based on a linear decompressor which consists of wires,XOR gates and flip-flops.
It has two types
a) Static reseeding
Compute a seed for each cube, the seed is loaded in the LFSR and it produces the test vectors, so we
store only the seed.
it has two disadvantages
- It must be as large as the test vector length.
- The circuit is idle during the vector generation
b) Dynamic reseeding
Solves the problem of static reseeding, it uses a network that expands the output to fill n output scan
chains while creating the result

19. Linear decompression

20. Broadcast scan method
 Uses the fact that many bits are don't care, it can be either independent or
dependent
 Independent case
 -------------- 1) Apply ATPG TO Both circuits.
 2) NOw we have a set of patterns to detect CUT-2 and part of CUT-1 Faults.
 3) Apply the don't care bits to detect CUT-1 Faults
 4) NOw we have a `minimized set` with appropriate fault coverage/

21. Dependent Broadcast scan method
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To allow the compression of the test vectors and to avoid the problem of equal cells in the scan chain,
we apply Illinois scan based compression technique.
In this technique instead of applying the test output of the ATPG to the scan chain, we partition it to
few partitions, we then either apply it as (broadcast - in parallel) and take the output of all stages, or
apply it serially(the output of stage i is the input of stage i+1) and so on, based on the partitioning
mechanism this technique is divided into
1) Static reconfiguration (uses a multiplexer to get the set of scan chains)
2) Dynamic reconfiguration (The configuration change every slice which is more flexible)
Illionis scan uses two modes of operations:
- Broadcast : Broadcasts one tester channel to multiple chains
- Scan : applies them in serial
The configuration can be done using a multiplexer to choose which channels the tester channel will
broadcast to.

22. Dependent Broadcast scan method

23. Geometric method
 The geometric method uses a loseless compression technique.
 It depends on four main shapes: point - line - traingle - rectangle
 The algorithm goes like this
 1) Start with a random test vector as a start point
 2) Sort all of the other vectors depending on their correlation with the first vector.
 3) Use shape covering algorithm to choose the largest shape that cover a group of zeros or
ones.
 4) Choose the optimal result of covering shapes
 5) Encode the results

24. Geometric method(Cont.)

25. Comparison of different techniques
 While most techniques discussed have good performance on commercial use
there are few drawbacks
 1) Linear decompression has the simplest structure (only XOR Gates and
registers) for cases of multiple scan chains we need to compress/decompress
each chain independently which takes more time and not parallelizable.
 2) Broadcast scan is also simple to implement, but it has a redundancy issue
(i.e. many scan chains may have the same bit value at the same location)
 3) Geometric method is less efficient and only experimentally tested, and
require more computation than other methods

26. References
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[1] Survey of Test Vector Compression Techniques,Nur A. Touba
[ 2]An Efficient Test Vector Compression Scheme Using Selective Huffman Coding
3] Using a Single Input to Support Multiple Scan Chains,Kuen-Jong Lee Jih-Jeen Chen,Cheng-Hua
Huang
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[4] LFSR-Based Test-Data Compression with Self-Stoppable Seeds, M. Koutsoupia E. Kalligeros X.
Kavousianos D. Nikolos
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[5] An Efficient Test Vector Compression Technique Based on Geometric Shapes , Saif al Zahir, Aiman
El-Maleh, and Esam Khan

[6] Reconfiguration Technique for Reducing Test Time and Test
Data Volume in Illinois Scan Architecture Based Designs, Amit R. Pandey† and Janak H. Patel


27. Questions

28. thanks