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Test Data Compression for ScanBased Testing

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Example: Using Direct Compatibility. Test Vector 1. Test Vector 2. Test Vector 3. 6/17/09 ... the longer the chains, the lesser the compatibility ... – PowerPoint PPT presentation

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Title: Test Data Compression for ScanBased Testing


1
Test Data Compression for Scan-Based Testing
  • Presented by Mustafa Imran Ali
  • Research Assistant COE

2
Presentation Outline
  • Issue/Motivation
  • Necessary Background
  • Existing Approaches
  • Proposed Approach

3
The Issue Test Data Volume
  • Exhaustive IC chip testing after manufacture is
    critical to ensure product quality
  • Full Scan based IC testing using Automatic Test
    Equipment (ATE) the most widely used approach
  • test data volume ? IC complexity
  • A typical SoC ASIC may require 2.5 Gbits of test
    data
  • manufacturing costs ? test data volume
  • ATE memory capacity test application time
    dictate cost
  • Hence the need for reducing IC test data volume

4
Digital Testing Basics
open
  • Logical Modeling of Faults
  • Structural Faults ? Single Stuck-Fault Model
  • Faults List
  • Test Generation Process determining test
    stimuli
  • Automatic Test Pattern/Vector Generation (CAD)
  • Fault Coverage Percentage

stuck-at
5
Continued…
  • Combinational vs. Sequential Circuits
  • Combinational Circuits
  • A fault detected by a single input vector
  • 100 fault coverage easy
  • Sequential Circuits
  • An ordered sequence of vectors required per fault
  • 100 faulty coverage hard!

6
What is Scan-Based Testing?
  • Converts sequential circuit test generation
    problem into a combinational circuit test!
  • Guarantees 100 fault coverage
  • Internal flip-flop values (state) can be set and
    read (or scanned in and scanned out)
  • NO need for sequence of vectors to detect a
    fault!
  • Test vectors can be applied in any sequence
  • Each test vector is independent of others (allows
    reordering)

7
How Does it Work?
  • Scan data is serially shifted in and out when
    device is in test mode

Circuit Under Test (CUT)
Primary Outputs
Primary Inputs
Test Mode Select
Scan Data In
Scan Data Out
Clock
8
Continued…
9
Test data volume problem
  • Test data volume can be calculated as
  • Test data volume scan cells scan patterns
  • Number of scan cells and scan patterns are
    related to complexity of designs
  • 10M gates, 1 scan cell/20 gates ? 0.5M scan cells
  • System-on-a-Chip designs require a large number
    of patterns to test each individual core e.g. up
    to 10,000 patterns
  • Multiple scan-chains can reduce test application
    time but not test data volume
  • Pins limitations still limit maximum scan inputs
    feasible

10
ATE Limitations
  • Both requirements growing rapidly
  • test data storage, and
  • test data bandwidth requirements between the
    tester and chip
  • ATE capability cannot scale indefinitely for
    increasingly complex IC designs
  • New testers with more memory, channels, and
    higher speed of operation not a good solution
  • the cost of a high-speed tester will exceed 20
    million by 2010 (ITRS Annual Report)

11
Solutions!
  • Eliminate the costly ATE! Use BIST
  • Built-in-Self-Testing generate test patterns on
    chip
  • Use Test Resource Partitioning (TRP) Solutions to
    ease burden on ATE
  • some hardware added on-chip, working in
    conjunction with external tester
  • helps reduce the test data and/or
  • the test application time

12
Built-In-Self-Testing (BIST)
  • Uses on-chip test pattern generation
  • Linear Feedback Shift Registers (LFSRs)
  • Limitations! Random Pattern Resistant Faults
  • Fault coverage is less than 100
  • Very long test sequences required
  • Cores have to be BIST-ready
  • Solution Mixed-mode BIST
  • Uses external testing BIST
  • Lots of ongoing work in this area!

13
TRP Using on-chip decompression
  • Test sets contain a large number of dont care
    values
  • Up to 98 bits can be dont cares in industrial
    circuits
  • Approaches based on various compression encodings
  • Run-length encoding
  • Statistical coding
  • Golomb coding
  • Runlength Huffman coding
  • Arithmetic coding
  • Dictionary based compression (LZ77, LZW)
  • Approaches based on LFSR seeding
  • LFSR used to generate scan chains
  • Different seeds values used for different test
    vectors

14
Compression using codes
  • Usually built on top of run-length encoding
  • Dont care values are specified to maximize
    compression
  • Maximizing runs of 1s and 0s
  • Further compression achieved by encoding run
    values using variable length codes such as
    Huffman coding
  • Some schemes operate on set of difference vectors
    instead of original test set
  • Difference vector contain fewer 1s

15
Compression Code Examples
16
LFSR Reseeding based Approaches
  • LFSR is run in autonomous mode to fill scan chain
    with the test vector
  • LFSR seed is the starting state of an LFSR
  • Idea compute a set of seeds that when expanded
    by LFSR will produce the test vectors
  • Different LFSR seeds will produce different test
    vectors

17
Continued…
  • The set of seeds are stored on the tester
  • transferred to the LFSR one at a time
  • seeds much smaller than the full test vectors
  • LFSR size depends upon max. dont cares/vector

18
(Scan) Inputs Width Compression
  • Idea driving multiple scan inputs with the same
    data values
  • only the common input data need to stored
  • Such inputs are grouped into a compatible class
  • Different types of compatibility have been
    identified
  • Direct
  • Inverse
  • Combinational
  • Compression Ratio
  • external chains / internal chains

19
Example Using Direct Compatibility
Test Vector 1
Test Vector 2
Test Vector 3
20
Continued…
21
Logic (Combinational) Compatibility
22
Example Adding Logic Compatibility
23
Proposed Scheme Few Observations
  • Extent of compatibility depends on specified bits
    per scan vector
  • lesser the specified bits, the lesser the
    conflicts, resulting in greater compatibility
  • the longer the chains, the lesser the
    compatibility
  • Compatibility analysis per vector gives a lower
    bound on achievable reduction
  • Compatibility will increase as dont care bits
    per vector increases

24
Test Set Partitioning Grouping
  • Partitioning the test set into acceptable
    bottleneck vectors
  • A desired compression threshold set
  • Vectors satisfying the threshold called
    acceptable
  • Acceptable vectors grouped such that any group
    still satisfies the threshold
  • best case ?1 group only
  • Bottleneck vectors relaxed to increase dont
    cares

25
Relaxation based on Decomposition
  • Each scan vector can be decomposed into multiple
    vectors
  • each having greater unspecified bits than the
    original vector
  • each vector now detects lesser faults than the
    original more specified vector.
  • bottleneck vectors are de-composed until the
    resulting vectors satisfy the threshold
  • vector that cover faults already covered by other
    vectors (redundant) is dropped
  • relaxed vectors are then made members of existing
    partition (s)

26
An Example 50 compression
27
Continued…
28
Decoder Hardware
  • Consists of MUXs to handle different partitions
  • MUXs required depends upon partitions created
    to satisfy the given compression requirement
  • Thus minimizing partitions minimizes the area
    overhead

29
Partitioning Results
  • Each Test Set is configured into 64 internal scan
    chains and 75 compression targetted

30
Conclusion
  • The proposed scheme aims to target user specified
    compression ratios
  • With an associated partitioning arrangement
  • H/W cost associated with extent of partitioning
  • Benefits of partitioning have been demonstrated
    on actual test sets
  • Decomposition-based test vector relaxation will
    (hopefully) lead to user specified compression
    ratios
  • Under implementation
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