MS Thesis Defense Presentation by Mustafa Imran Ali COE Department

An Efficient Relaxation-based Test Width Compression Technique for Multiple Scan Chain Testing
MS Thesis Defense Presentation by Mustafa Imran Ali COE Department Advisor: Dr. Aimane H. El-Maleh Tuesday, April 16, 2019 MS Thesis Defense

Presentation Outline Motivation Compression Approaches
Proposed Approach Experiments Comparison Future Work Tuesday, April 16, 2019 MS Thesis Defense

The Issue: Test Data Volume
Exhaustive IC testing critical to ensure product quality Full Scan based IC testing using Automatic Test Equipment (ATE) the most widely used approach A typical SoC ASIC may require 2.5 Gbits of test data ATE memory capacity & test application time dictate cost test data volume  IC complexity manufacturing costs  test data volume Tuesday, April 16, 2019 MS Thesis Defense

Test data volume problem
Test data volume can be calculated as: Test data volume ≈ #scan cells × #scan patterns #scan cells and #scan patterns related to design complexity 10M gates, 1 scan cell/20 gates  0.5M scan cells Complex designs require a large number of patterns e.g. ≥ 10,000 patterns Tuesday, April 16, 2019 MS Thesis Defense

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 Tuesday, April 16, 2019 MS Thesis Defense

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 Tuesday, April 16, 2019 MS Thesis Defense

TRP: Using on-chip decompression
Test sets contain a large number of don’t care values Up to 98% bits can be don’t cares in industrial circuits Different Classes of Techniques Exist Code-based Schemes Linear Decompressor Based Schemes Combinational Linear Decompressor Sequential Linear Decompressor Broadcast-Scan Based Schemes Reconfigurable Broadcast Schemes Static Reconfiguration Dynamic Reconfiguration Tuesday, April 16, 2019 MS Thesis Defense

Another Classification
Uses Structural Information Uses fault simulation Requires custom ATPG Decompression Hardware Input Dependent Independent Number of scan inputs/chains Single Multiple Tuesday, April 16, 2019 MS Thesis Defense

(Scan) Inputs Width Compression
Idea: driving multiple scan chains with same values only common data need to be stored Such chains are form a ‘compatible class’ Types of compatibility Direct Inverse Combinational Compression Ratio = (#internal chains - #external chains) / #internal chains Tuesday, April 16, 2019 MS Thesis Defense

Example: Using Direct Compatibility
Scan Chains = 8 Test Vector 1 1 Test Vector 2 Test Vector 3 Tuesday, April 16, 2019 MS Thesis Defense

Continued… 1 2 3 Decompressor Representative Scan Chains = 3
Decompressor Representative Scan Chains = 3 Tuesday, April 16, 2019 MS Thesis Defense

Key Observations Extent of compatibility/width compression depends upon length of chains the longer the chains, greater the conflicting bits Percentage of don’t care bits more Xs give less conflicts, resulting in greater compatibility Relative positions of don’t care bits Tuesday, April 16, 2019 MS Thesis Defense

Key Observations Some vectors need more colors than others
limiting the reduction if multiple vectors are analyzed together Two or more vectors achieving the same number of colors can still have different compatibility groups Compatibility analysis per vector gives a lower bound on achievable reduction Tuesday, April 16, 2019 MS Thesis Defense

An Example: 50% reduction
Tuesday, April 16, 2019 MS Thesis Defense

Test Set Partitioning Identifying acceptable & bottleneck vectors
A desired coloring threshold is targeted Vectors satisfying the threshold are acceptable Put non-conflicting vectors in a partition Members satisfy the threshold colored together Bottleneck vectors decomposed (relaxed) to derive new acceptable vectors having greater don’t cares Tuesday, April 16, 2019 MS Thesis Defense

Partitioning Algorithm
sort acceptable vectors on colors in descending order create a (default) partition with first vector For each vector in list compatibility analyze with vectors in all existing partition(s). If threshold not exceeded, include in that partition If no such partition, create new partition for current vector Tuesday, April 16, 2019 MS Thesis Defense

Decomposition based on Relaxation
Compatibility can increase as don’t care bits per vector increase Each vector can be decomposed into multiple vectors each having greater Xs than the original vector each detects a subset of faults of the original bottleneck vectors are decomposed until the resulting vectors satisfy the threshold These new relaxed vectors are partitioned Tuesday, April 16, 2019 MS Thesis Defense

An Example of Decomposition

Algorithm’s Input and Output
N >> M nV’ >= nV Tuesday, April 16, 2019 MS Thesis Defense

Compression Algorithm
Objectives Minimize decomposition required To maximize compression To minimize partitions To minimize test application time Minimize Partitions To minimize hardware cost Constraint Maintain the fault coverage of the original test set Tuesday, April 16, 2019 MS Thesis Defense

(O)1 X X 0 X 0 X 0 0 X 0 X 1 X 1 X X 0 X X X X 0 1 1 0 X X 0 X X 1
Approach Used Decomposition can be minimized if faults per bottleneck vector are minimized A representative vector obtained after coloring is more specified than the original vector (O)1 X X 0 X 0 X 0 0 X 0 X 1 X 1 X X 0 X X X X X X 0 X X 1 (R)1 X X X 0 X X X 0 X X 0 X 0 1 Fault simulate each representative vector obtained to drop detected faults Decompose bottleneck vector to only target remaining faults Tuesday, April 16, 2019 MS Thesis Defense

Decomposition Algorithm
Create a new vector for first undetected fault in faults list of a bottleneck vector Select next fault and check if covered. If it is, skip to next fault If it is not, get its atomic component and merge. Get coloring and if threshold not exceeded, go to step 2 If threshold exceeded, revert to the previous state and partition new vector Get its representative vector, fault simulate, drop newly covered faults If current bottleneck vector has remaining faults, goto step 1. Tuesday, April 16, 2019 MS Thesis Defense

Missing Faults Problem
Fault coverage linked to representative vectors Representative vectors linked to a partition’s coloring configuration Partitions can change to accommodate a new vector with re-coloring Representative vector changes if a partition’s coloring configuration changes Tuesday, April 16, 2019 MS Thesis Defense

Changing Fault Detection Problem
Partition's Compatibility Before change After change Representative Vector’s Specified Values 1 X X X 0 1 0 1 X X X 0 1 0 Faults Covered Tuesday, April 16, 2019 MS Thesis Defense

End Result A dropped fault may become undetected when its vector is modified later on If this fault is not covered by any subsequently created vector, this fault is left out This is more likely for essential faults Tuesday, April 16, 2019 MS Thesis Defense

Solution Approaches Do not allow any essential fault detection to change while partitioning Try without recoloring Select different partition accordingly or create new one Allow any fault to be disturbed but address all undetected faults at the end e.g. create new vectors Tuesday, April 16, 2019 MS Thesis Defense

Solution Outcomes Different results based on the approach used
First approach: tends to create more partitions Second approach: creates more vectors but may lead to less partitions The vectors created will have many don’t cares so are likely to fit in existing partitions Tuesday, April 16, 2019 MS Thesis Defense

Proposed Variations Three variations proposed for partitioning newly derived subvectors Do not disturb any previously covered fault Disturb a fault if it can be covered by current bottleneck vector Allow any faults to be disturbed but go for minimum disturbance Tuesday, April 16, 2019 MS Thesis Defense

Minimizing Additional Vectors
To minimize any additional vectors created Attempt to incrementally modify existing partitioned vectors Successful only if it doesn’t disturb the partition Create new vectors only as a last resort Bunch as many faults as possible in a single vector Tuesday, April 16, 2019 MS Thesis Defense

Compatibility Analyze
Complete Algorithm Mark Essential & Non-essential faults Create & Partition Addit. Vectors Compatibility Analyze All Vectors Perform Merging Partition Acceptables Fault Simulate Representative Vectors Decompose & Partition Tuesday, April 16, 2019 MS Thesis Defense

Prioritizing Essential Faults
Fourth variation attempted Begin compression with relaxed vectors targeting essential faults Has the potential to give reduced partitions Non-essential fault left to surreptitious detection by representative vectors Any undetected fault covered at the end by incremental merging or additional vectors Tuesday, April 16, 2019 MS Thesis Defense

Decompression H/W Log2(Max Partition Size) Log2(#Partition)

MUXs for Partitioning N MUXs for N Chains
Number of MUX inputs = Number of partitions Tuesday, April 16, 2019 MS Thesis Defense

Experiments Setup Algorithms implemented in C under Linux
HOPE simulator used for fault simulation Publicly available graph coloring algorithms used Algorithm by El-Maleh and Al-Suwaiyan used for test relaxation Full-scan version of 7 largest ISCAS-89 benchmarks used Test sets generated by MINTEST ATPG used Tuesday, April 16, 2019 MS Thesis Defense

Details of Test Sets Static compacted test sets used for all detailed results Dynamic compacted test sets used for comparison with other works Tuesday, April 16, 2019 MS Thesis Defense

Methodology Algorithm input parameters Parameters setup used
Selecting a scan chain length for each benchmark Selecting a desired number of ATE channels to target Parameters setup used Scan chain length giving near 64 scan chains for two smallest cases and 100 and 200 chains for largest five Desired ATE channels varied over a range to observe effect on achieved compression, test vector counts and number of partitions Tuesday, April 16, 2019 MS Thesis Defense

Test Set Characteristics

Color Histogram for Chain length 7

Color Histogram for Chain Length 4

Compression without Partitioning

Compression w/o decomposition

Results: 88 scan chains Tuesday, April 16, 2019 MS Thesis Defense

Results: 153 scan chains Tuesday, April 16, 2019 MS Thesis Defense

Compression vs. Scan Inputs

Vectors vs. Scan Inputs Tuesday, April 16, 2019 MS Thesis Defense

Partitions vs. Scan Inputs

Case with Large % of bottlenecks

s38417: 98 scan chains Tuesday, April 16, 2019 MS Thesis Defense

s38417: 185 scan chains Tuesday, April 16, 2019 MS Thesis Defense

Compression vs. Scan Inputs

Vectors vs. Scan Inputs Tuesday, April 16, 2019 MS Thesis Defense

Compression Levels Tuesday, April 16, 2019 MS Thesis Defense

Compression Level Tuesday, April 16, 2019 MS Thesis Defense

Hardware Costs Tuesday, April 16, 2019 MS Thesis Defense

Future Work Incorporating selective don’t care identification
Greater reduction by eliminating conflicting bit values Partitioning can be improved as well Existing relaxation technique can be modified Tuesday, April 16, 2019 MS Thesis Defense

Thank you. Questions? Tuesday, April 16, 2019 MS Thesis Defense

MS Thesis Defense Presentation by Mustafa Imran Ali COE Department

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