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Zero Defect and Risk Mitigation with
Advanced Analytics
Joy Gandhi, CQO
Anil Gandhi, Ph.D. President and Chief Data Scientist
Qualicent Analytics, Inc
Agenda
• Qualicent Introduction
• Relevant Trends in the Automotive Industry
• Role of Data and Advanced Analytics
• Technical Goal and the Analytics Process
• Case studies
• Advanced Analytics for Risk Mitigation in the APQP
• Enhanced Manufacturing Anomaly Detection through Analytics
• Summary
Qualicent Introduction
• Services – Advanced Analytics
– Quality Engineering/Failure Diagnostics
– Big Data/IoT Data Integration
• Software – ZeroDefectMiner® software for all industries
– ZeroXMiner for healthcare and IoT
• ABATE Risk Software-Service package
Source: McKinsey
http://www.mckinsey.com/client_service/semi
conductors/latest_thinking
Electronics in Automotive…
…are pervasive
A modern navigation and control panel in a high-end connected car. These type of cars have as many as “50-75 ECUs making them truly distributed computers on
wheels.” (T.Johnson et.al, Univ of Texas Dept of Computer Science and Engineering)
Recall Trend
Significant increase in the recalls
Source: 2015 Automotive Warranty and Recall Report, Stout, Risius and Ross
Note: SRR defines electricals as ignition module and switch, starter assembly, battery, instrument panel, various wiring
EWR Trend - Electricals
Significant increase in the EWRs with injury/death
from electricals
Source: 2015 Automotive Warranty and Recall Report, Stout, Risius and Ross
Confirmed by other Researchers: T. Johnson, et.al. University of Texas Dept of Computer Science and Engineering) and University of Waterloo (Dept of ECE) Paper confirms the clear
rise in the electronic/electrical hazards and risk related notifications in motor vehicles in US, Canada and Europe.
The Solution is in your Data
Evolution of Advanced Analytics
Why is it difficult to achieve Zero Defects?
Business Problem = Reducing Risk
RISK COST
Manufacture
Risk from manufacturing
Risk from bad design
Field Failure
…this presentation
Human Errors
Systemic
Reducing Risk from Manufacturing
RISK COST
Contain
Prevent @Process @ Suppliers
@Manufacturing
Manufacture Field Failure
Marginalities = Units that pass SPC for each and all tests
…but with all tests taken together the unit might be at…
Predictors for large excursions / large effects not difficult to source…BUT
× Biggest field failure losses are from marginal effects and/or intermittent deviations over extended periods
× Marginal effects are difficult to detect with standard methods because of high dimensionality, noise, small # of fails, …
14
OUTLIER 6 s 2 s
6 s 6 s
2 s 2 s __
?
Need advanced methods to detect anomalous parts
o 1000s of components 10,000s of solder points, 100s part SKUs & suppliers
o Each parameter could be within tolerance but combination of parameters may be an outlier
o Lots of available multi-variate combinations which can make the unit an outlier
Source: Mentor Graphics, 2012
Complex devices = Large number of influences / dimensions
Interactions
thickness reflectivity
resistance
capacitance
settling
time
….
Impossible to model on physics
(too many interaction possibilities)
(skewed dataset)
Small number of fails
Tolerances based on individual parameters
Ship marginal product 6 s 2 s
6 s 6 s
2 s 2 s __
?
Yes!
Analytics Process Summary
Traditional: ANOVA, t-test
screen / coarse reduce
Composite distance
cluster analysis
visualization / client
Machine learning model
1. Operating and exclusion
zones for design
2. Anomaly detection
Case Study 1
Field Failure KPI
Composite Distance How
Detect field failures with high class
purity
Result
Who Automotive Semiconductors
VarZ
VarY VarX
Outlier
yes no
Co
mp
osite
Dis
tan
ce
pass fail
pass 6,974 15 6,989
fail 0 2 2
6,974 17 6,991
pass fail
pass 6,981 8 6,989
fail 1 1 2
6,982 9 6,991
@7 @6
Yield Hit = 0.2%
predicted
actu
al
predicted
actu
al
Co
mp
osite
Dis
tan
ce
pass fail
pass 6,974 15 6,989
fail 0 2 2
6,974 17 6,991
@6
Yield Hit = 0.2%
Topmost parameter
Co
mp
osite
Dis
tan
ce
Incumbent Method – Risk Assessment
Project the number of units that will likely fail in the field in the next 10 years
Distance Method – Risk Assessment
“These” units that will likely fail in the field in the next 10 years
?
UCL = Median + x * robust sigma
Accuracy Purity
Composite distance
Top Parameter
Case Study 2
Field Failure KPI
Composite Distance How
Detect almost all field failures with
high class purity
Result
Who Electronic Manufacturing
Co
mp
osite
Dis
tan
ce
To
pm
ost p
ara
me
ter
median + 6*robust s
USL
Five out of seven field failures are detected by Composite Distance…at low cost
Co
mp
osite
Dis
tan
ce
To
pm
ost p
ara
me
ter
pass fail
pass 18,399 5 18,404
fail 2 5 7
18,401 10 18,411
predicted
actu
al
pass fail
pass 18,288 116 18,404
fail 3 4 7
18,291 120 18,411
predicted
actu
al
Composite Distance offers significant improvement over single parameter controls
Pattern Discovery
Deductive Reasoning
Inductive Reasoning
1. Make a hypothesis based on prior knowledge
2. Test the hypothesis
1. Discover patterns, discover hypothesis
2. Check if patterns have material meaning
DISCOVER PATTERNS IMPOSSIBLE TO HYPOTHESIZE
Machine Learning
Traditional Statistics
Field fail, yield, quality,
safety and
effectiveness metrics,
…
Thickness, resistance
capacitance, time,…
Strategic
Tactical
Model Discovery
Anomaly Detection
y = f(x)
x = x’
INPUTS OUTPUTS
When: Development, Pre-launch, Early production, HVM
Why: Process optimization, Exclusion Zones
How: Exact Models based on machine learning
Class: Supervised
When : HVM
Why: Containment, feedback to suppliers for prevention
How: Iterative Distance methods
Class: Unsupervised
Case Study 3
Large Semiconductor Company Who
Yield KPI
Machine learning algorithms How
Revenue increase by > $ MM/quarter Result
Rule Discovery
Variables M, Q and T individually have no influence on Metric of Interest (MOI)
Data is normalized, scaled and transformed
Variable M Variable Q Variable T
0.0
0.2
0.4
0.6
0.8
Yield = 0 Yield = 1
100 150 200 250 300 700 750 800 850 900 9950 10000 10050 10100
M < 191
Q < 812
T > 10,006
100 150 200 250 300 700 750 800 850 900 9950 10000 10050 10100
0 1
+ +
Variables M, Q and T interactively strongly influence the output
Variable M Variable Q Variable T
Rule Discovery / Machine learning
RESULT:
EXCLUSION ZONE
Case Study 4
PV Solar Company Who
Cell Efficiency KPI
Machine learning algorithms How
Prevent cell efficiency loss by 30% Result
Solar Panel Line Flow
I J K L
Measurement at four sites all passing inspection but low cell efficiency
Algorithms discovered that it’s the ratio that matters
= PATTERN DISCOVERY
Parameters A, B, C, D fully in control and within normal distribution
E F G H
A B C D
Case Study 4
Before Date X
After Date X A
C
Machine learning algorithms discover ratio of A/C as critical parameter (not predicted
by domain experts, but later successfully explained by experts)
EXCLUSION ZONE: Y - low process metric readings (< 24.5) X -low in line measure(< 81) Z (date) > something
Case Study 4: Solar
Machine learning model predicts ~31% reduction in EFF in exclusion zone
Advanced Analytics in the Entire
Product Lifecycle
Proactive Analytics for Risk Mitigation at every APQP Phase
Solution: Analytics in the Product Lifecycle
Advanced
Analytics Product and
Process data
Warranty and
supplier data
Pattern discovery Anomaly detection
Pre-prototype Phases
Concept Product Design
Process Design
Verification
Define requirements
Select materials, suppliers
Identify similar products
Get relevant historical data
Model Discovery
Rules discovery
Adjust process or design to rules for zero defect
Design product, process
FMEA and DFX
Supplier qual data
Anomaly detection on supplier material and process data
CA on material and process
Optimize product and process design
Test device function
Predictive modeling functional and application data; anomaly detection
Optimize product and process design based
Corrective action on anomalies
Data Sources and Outcomes
Analytics DFM, Process and Design CA/CI
Relevant Historical
Warranty/Field Failure Data
Special/Critical Parts/Process
Data
Special/Critical Materials
Supplier Data DFMEA special function/dimensi
ons
Process FMEA special process characteristics
Datasheet Specification
Control
Composite Distance Machine Learning
Outcome Outcome
Techniques
Validation, Safe Launch and HVM
Validation Safe
Launch/SOP High Volume Production
Customer Qualification
Validation/Application Testing
Predictive modeling
Rules discovery
Anomaly detection
Optimize Design
Manufacturing process corrective actions
Optimize Yields
Anomaly detection on pilot
Predictive Model Refinement
Corrective Action on material and process
Optimize product and process design based on predictive model
Ongoing Production Test and Inspection
Anomaly detection on test data
Anomaly Detection on supplier data
Corrective action on anomalies
Corrective Action on maverick/high DPPM lots
The Composite Distance technique has been proven to accurately detect field
failures from manufacturing data.
COMPOSITE DISTANCE CHART
Data involved key OEMs, Tier 1, Tier 2 and Tier 3 suppliers
Worldwide Studies
Composite Distance: Cost Impact
• 7 out of 10 field failures have been detected
• Cost Analysis per Part in a Tier 2 supplier
– Typical electronic board
– 1 year period, 91 failures at Tier 1 and OEM
– Estimated total cost of failure handling =$1.7M
– Cost savings from detection of 70% of failures~$1M
• Impact to reputation and loss of business are not included
Composite Distance Use Cases for SQM
Supplier 1
Server
Data Mirror Data Mirror
Supplier 2
IQC In-process Test
Engineering Mfg
Early Warning Process for Containment
Sample OOC Action Plan
OOC detected
Put product on hold
Risk?
Perform FA
Onsite Eng Dispositions
Eliminate Root Cause
Purge or Recall
Low
High
Medium Stress to fail
Variables of Importance
Process is crucial for full
Issue Resolution
Design Verification Validation HV Production
Pre-proto-type A, B samples C, D Samples Production
• Model Historical Data • Extract operating and
exclusion zones • Improve product and
process design • Anomaly detection on
supplier data
• Model with A,B data • Extract operating and
exclusion zones • Calculate DPPM • Improve product and
process design
• Model with C, D data • Extract operating and
exclusion zones • Outlier Detection for Safe
Launch • Improve process for Safe
Launch
• Ongoing Outlier Detection
• Continuous improvement of Process/product
Prevent Prevent Prevent
Contain
Resolve
Contain
Resolve
Predictive Models Predictive Models
Anomaly Detection (Supplier Data)
Automotive
Sample
Phase
Advanced
Analytics
Goal
Predictive Models
Anomaly Detection
Explanatory Models
Anomaly Detection Rule Discovery
Summary
• Zero defect can be achieved using Advanced Analytics
– Anomaly Detection – unsupervised learning
– Machine Learning – supervised learning
• Contain high probability field failures using composite distance analysis
• Defect reduction and yield improvement can be achieved with predictive models
• Root cause identification with explanatory models
Advanced Analytics can be employed in the entire product life-cycle.
THANK YOU!
BACK-UP
Sample Data Stack for Analytics
Unit # Solder Volume
Reflow Temp
Gas Flow
R12 C48 Shorts Bridging Idd Leakage Func Field
1
0
1
Process Device Defect Inspection Final Test Field
Model/Pattern Discovery
• What are the predictors of DPPM?
Rules Discovery • What are the best operating or process
conditions to achieve low field DPPMs
Anomaly
Detection
• Which parts are highly likely to fail in the field?
Supervised Learning
Supervised Learning
Unsupervised Learning