Product lifecycle analytics Eran Rousseau, VP Product Solutions for Semiconductors Month 2019
Product lifecycle analytics
Eran Rousseau, VP Product
Solutions for Semiconductors
Month 2019
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It’s a changed worldTechnological innovation has transformed our lives.
Products and devices are more intelligent and connected.
These products rely on thousands of electronic components that must be more reliable than ever before.
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1) Automotive change drivers for the next Decade, EY, 20162) BMW - AEC Automotive electronics reliability workshop, 20173) NHTSA Recall Data4) Audi, DVCon Munich, 2017
90%
22%
3x
car innovations & new features are driven by electronics1
warranty costs related to electronics & semiconductors2
car recall increase from 2014-2016 due to electronics3
Autonomous car will drive 20x more than traditional car4
Automotive
innovation
exemplifies
the challenge
Reliable electronics is a must
1 car failure
every hour4
Audi says
$10Bn50Bn cars
Toyota’s gas pedals recall
Ford’s failure-to-park recall
Takata’s air bag recall
20x
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Our roleWe deliver unprecedented manufacturing efficiency and reliability to the Automotive, Semiconductor and Electronics supply chains.
We provide actionable insights through unique data analytics to prevent bad products from reaching the market.
We call it lifecycle analytics
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Analyzing huge volumes of data
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100bn+devices per year
Semiconductor Automotive Electronics
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Trusted by leading brands
6
Customers
Supply chain
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Providing innovative solutions
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Collect Detect Act• Data harmonization
• Product, machine and process data
• Prescriptive analytics
• AI / Machine learning
• 24x7 analytics engine
• Real-time
• Automatic
• Distributed
• Controlled
O+ solution
Customer methods
• Collect lots of data
• Use it primarily when there is a problem: Bad Yield, RMA (Returns), Etc.
• Find the problem but frequently not the root cause
• Process is often manual and reactive, not proactive
• Use of many tools, but not an integrated solution
A unique, automated and proactive integrated solution
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Through the entire product lifecycle
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Chip Board Product In use ReturnsECU modules
Machine • Process • Test • Rework • Genealogy • Performance • Reliability • Usage • Warranty
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Semiconductor solution overview
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Semiconductor solutions
Yield Productivity & Efficiency
Quality & Reliability
Ramp, NPI
Open platform
Big data highway
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Cloud or on-prem.
O+ central analytics24x7 rule execution & orchestration
O+ data platformCentral repository
Central
System architecture
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Factory C
Edge (factory floors)
Factory B
Factory A (internal or outsourced)
Actionable insights across all manufacturing and test processes
Tester C
Fab data
Other equipment & data sources
Tester A
Tester B
Assy. data
Portal+
Control Room+
PLM, ERP, CRM
Action
Data
MES
Action
Data
Action
Data
Actio
n
Data
O+ proxy
O+ proxy
O+ proxy
Action
Data
Action/R
ule
s
Data
Data
Data
O+ edge analytics24x7 rule execution & orchestration
O+ data platformEdge repository
Action
Data
Action/Rules
Data
© OptimalPlus 2019. All rights reserved.
Cloud or on-prem.
O+ central analytics24x7 rule execution & orchestration
O+ data platformCentral repository
Central
System architecture
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Factory C
Edge (factory floors)
Factory B
Factory A (internal or outsourced)
Actionable insights across all manufacturing and test processes
Tester C
Fab data
Other equipment & data sources
Tester A
Tester B
Assy. data
Portal+
Control Room+
PLM, ERP, CRM
Action
Data
MES
Action
Data
Action
Data
Actio
n
Data
O+ proxy
O+ proxy
O+ proxy
Action
Data
Action/R
ule
s
Data
Data
Data
O+ edge analytics24x7 rule execution & orchestration
O+ data platformEdge repository
Action
Data
Action/Rules
Data
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Common challenges we solve
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• Minimize excursions
• Eliminate RMAs
• Protect your brand
• Product safety net
Yield Productivity & Efficiency
Quality & Reliability
• Overall yield
• Site-to-site yield
• Re-test policy
• Equipment & H/W performance issues
• Excessive index & pause times
• Test time variations per tester
• Inefficient retest policies & execution
• Inconsistent tester availability & utilization
• Quality & consistency of data
• Minimize time to market
• No traceability
• Expensive mask costs
Ramp, NPI
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Rules – Targeting challenges 24x7
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• Library of standard rules accommodate most of the challenges faced by our industry
• Custom rules available for unique monitors and actions including support for R and Python scripts
• Deployed at any level of your supply chain (central vs edge)
• Rules engine running 24x7
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Rules turning challenges into actions
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Action categories
Equipment actions
• Pause
• Engineering tool alert
Process actions
• Put materials on hold
• Re-binning
Recipe adjustments
• Re-test skip/add
• Adaptive testing
Data augmentation
• Feed-forward
• Feed-backward
• Virtual operation
Alerts
• Quality outlier alerts
• Yield alerts
• Predictive/Anomaly alerts
SolutionsDetails
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Quality & Reliability – Solutions
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• Outlier Detection
• Adaptive testing using Machine Learning
• Escape Prevention
• Test data reliability
• Minimize excursions
• Eliminate RMAs
• Protect your brand
• Product safety net
Quality & Reliability
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Driving quality improvements
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Approaches
• Yield impact budgetProactive outlier screening
• Cycles of learningRMAs
Best practices
• Escape preventionDeterministic
• Outlier detectionGeographical & Statistical
• Quality indexDie “goodness” score
• Data feed forwardDrift detection & ML scoring
• Auto-Hold
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Yield impact budget – Balancing quality and yield
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• Comprehensive and flexible library of algorithms, supporting R & Python
• Can be applied and adjusted to meet yield/quality targets
• Results are saved in the database which allows you to visualize and monitor the impact
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RMA/Quality cycles of learning
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Cycles of
learning
Data analysis
Monitoring recipe
execution
Publishing &
activating
Recipeconfiguration
Simulation &
validation
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Customer Use Case: Quality monitoring – Test escapes
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Device: Automotive
Problem: Possible quality returns
Issue: Penalties from customer
Problem discovered: No PRR monitoring & 12 possible test escapes
Fix: Rules & special binning
12 devices
Result: Insurance and improvement reports to customer
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Yield – Solutions
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• Baseline yield & SBL monitoring
• Test equipment performance
• Test and retest policies and execution
• Tests limits validation
• Cross-operation correlation
• Targets against any measure/KPI
Yield
• Overall yield
• Site-to-site yield
• Re-test policy
• Equipment & H/W performance issues
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Overall yield contributors
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DesignTest & Ops
Process
Currently and historicallyour main focus
Active PoC’s started forFab process data yield
Interest from design partners for our solution
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Customer Use Case: Operational yield – Site issue
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Device: Network
Problem: Yield loss
Issue: Yield by tester varies
Standard O+ rules foundWith no monitoring – Site-Site issue not detected – This case is 16 lots
Iteration 0
Iteration 1
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Customer Use Case: Operational yield – Site issue (continued)
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Operation issue: Same-site problem over 4 re-tests
Problem discovered: Potential Good devices tested as Bad
Fix: Rules monitoring catches issues on 1st pass
Iteration 2
Iteration 3
Result: Increased overall Yield by 2%
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Productivity & Efficiency – Solutions
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• Adaptive Testing using Machine Learning
• Test equipment performance
• Test and retest policies and execution
• Testers availability & utilization (OEE analysis)
• Classical Test Time Reduction (TTR analysis, ROA)
• Adaptive Test Time Reduction (ATTR)
• Cross-operation correlations
Productivity & Efficiency
• Excessive index & pause times
• Test time variations per tester
• Inefficient retest policies & execution
• Inconsistent tester availability & utilization
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Productivity & Efficiency opportunities
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Better resolution of time during test
Retest optimization
Test time consistency
Tester utilization
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Customer Use Case: Productivity & Efficiency – Increasing test time
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Device: Microcontroller with Flash
Problem: Capital avoidance
Issue: Needed 10 more test stations
Problem discovered: Issue with test program
Fix: Improved O+ rule for monitoring for all future testers/devicesResult: Saved 8 test stations = $12M in CapEx & OpEx Savings
Standard O+ rules foundTesters had different throughputsTest Time Increasing from 120 Sec to 300 Sec
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Ramp, NPI – Solutions
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• Data loading rules
• Load and create conditions
• Sandbox to edit metadata
• Datasets
• Virtual “workbench”• Shared analyses and data augmentation
• Full chain of custody
• Limits, Correlation & GR&R Applications
• Report generation
• Quality & consistency of data
• Minimize time to market
• No traceability
• Expensive mask costs
Ramp, NPI
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NPI areas of focus and flow
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• Minimize time to market
• Analyze split lots
• Determine production limits
• Identify design sensitivities
Datacollection
Sandbox Datasetmanagement
Analysis Reporting
• Proxy• Drop Box• STDF• OTDF• SAF
• Data Cleansing• Mapping• Validation
• Association• Augmentation• Attributes• Templates
• Static/Scheduled• Flexible• Intuitive• Customized
PVT Analysis
Correlation App
Limits App
GRR App
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Customer Use Case: Ramp, NPI – Limit Simulation App
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Device: Cell phone
Problem: Limits not optimized
Issue: Would not fail questionable measurements
Fix: Run analysis using limits application
Result: Immediate feedback = Faster product launch
O+ standard tools found: Limits too wide
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Partner with us to enhance your
Big data strategy with our open platformSynergetic with any data lake | Cloud & on-premise | Accessible optimized schema |
AI & machine learning | Collect & act anywhere | Enhance data scientist productivity
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Voice of the market
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“How can I combine, and do more with my siloed data systems?”
“Our data retention is at least 10 years for our
automotive products.”
“We already have a corporate license of Tableau, can we use
this to visualize O+?”
“I want to leverage fab/assembly data (i.e. defect & inspection) to improve my quality”
“My teams are proficient in Python or R and I want to leverage this”
“Can we have programmatic access
to O+ data?”
“I know we need to do ML, we just don’t know how to get started”
“How can we store old data so it doesn’t take so long to
reload & use?”
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Consolidated challenges
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CTO/CIOs & IT professionals
Concerned about enterprise TCO (Total Cost of Ownership
Product, Quality & Yield Engineering teams
Need a solution providing analytics that scale
Data Scientists & Engineering teams
Need a collaborativeecosystem
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Platform goals
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1Support bi-directional data integrationwith any customer data lake
2Enable easy consumption of OptimalPlus data by 3rd
parties and BI tools
3Integrating with machine learning data science frameworks,leveraging OptimalPlus deployed infrastructure
4Boost developers innovation by leveraging OptimalPlus rich API’s, algorithms and infrastructure
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Column store databaseMetadata index +“Hot” cacheSQL over Hadoop
Optimal+ data pipeline
Portal+ desktop & web UI
Analytics engine & API
Data pipeline
Optimal+ platform
Customer data lake
IBM Streams
Customer platform
(public, private, hybrid)Cloud
Customer data
Industry focused open platform
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Synergetic with any big data strategy
Connected to existing infrastructure
Open for all kinds of data
Accelerates innovation
Extensible through both data and algorithms
Optimized“Manufacturing”data
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Summary
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Lifecycle analytics solutions
turning data into actions for immediate ROI
Product-centric approach
for improved quality & reliability and operational efficiency
End-to-end supply chain visibility
across operations and industries
Open platform
indusry focused for seamless integration with any big data strategy
Domain expertise
applying data science to solve industry challenges
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Significant business impact
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50% case avoidance
up to 25%cost savings
increase up to
10% NPI2% HVM
from weeks to days NPI, TTM, RCA
Yield Productivity & Efficiency
Quality & Reliability
Ramp, NPI
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Ask our customers
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“Escape Prevention enables us to identify specific manufacturing and test issues that drive advanced quality screening and comprehensive product management.”– Michael Campbell Senior VP of Engineering
“Global Ops for Electronics enables us to rapidly identify and respond to the source of any PCB and systems manufacturing issue, down to an operation, facility, line or station.”– Keith Katcher VP of Operations Engineering
“Escape Prevention helps enable Marvell to deliver best-in-class silicon solutions for the automotive market segment.”– Mark Jacobs VP Engineering Operations
“We went from ‘We can’t afford to do this [Optimal+] to ‘We can’t afford not to do this [Optimal+]”– Carl Bowen AMD Fellow
“Optimal+ gives us real-time visibility of our test operations, enabling us to monitor every critical parameter to ensure that every product is of the highest quality and performs as expected.”– Vincent Tong Senior VP, Global Operations & Quality
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Thank you