Test VectorCompression

Presented to:

Prof.Dr. : Ihab Talkhanby Eng. Amr Abd El latief Abd El Al Eng. Mohammad Ahmed Hamed 

AgendaIntroduction Benefits of data Compression Why does compression works (on testing

vector)Test vector compression schemes CategoriesData compression techniques Hufman Coding method

Agenda Geometric methods.

Conclusion

References

Introduction • Test Levels: System Testing, Chip Testing

Board Testing.

• Increasing integration density results in:•larger designs •more scan cells •more faults.

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.

Introduction (Cont.)Overcoming approaches :

stand-alone BIST:But it has a low fault detection probability

due to RPR Faultshybrid BIST:

use a combination of BIST and test vectorstest data compression. (Described

Here)

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

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

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.

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

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.

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)

Dictionary codes.

Hoffman Method Example

Huffman Algorithm initialization : put all symbols on a list sorted according to their frequency count

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

Delete the children from the list Assign a code word for each leaf based on the path from the root

Apply on the Test Vector pattern

Hoffman Method Implementation

Linear decompression 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

Linear decompression

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/

Dependent Broadcast scan method 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.

Dependent Broadcast scan method

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

Geometric method(Cont.)

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

References [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

[4] LFSR-Based Test-Data Compression with Self-Stoppable Seeds, M. Koutsoupia E. Kalligeros X. Kavousianos D. Nikolos

[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 TestData Volume in Illinois Scan Architecture Based Designs, Amit R. Pandey† and Janak H. Patel

Questions

thanks