www.qualicent.net

Advanced Analytics for Zero Defects

Anil Gandhi / Data Scientist Joy Gandhi / Quality Consultant

GSA – Quality Working Group Meeting March 25, 2015

Agenda

• The case for Zero Defects • Problem Statement • Solution • Case studies • Analytics in the Product Lifecycle • Summary • Q & A

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Qualicent Introduction

• Services – Advanced Analytics – Quality Engineering/Management, Quality System

Standards – Big Data Implementation

• Software – ZeroDefectMiner® software for Automotive, Medical

Electronics, Aerospace

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http://www.pwc.com/gx/en/technology/publications/semiconductor-report-spotlight-on-automotive.jhtml

Semiconductor is a growing share of the automobile cost

Semiconductors in Automotive

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Source: McKinsey

http://www.mckinsey.com/client_service/semiconductors/latest_thinking

Semiconductors in Automotive…

…are pervasive

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Automotive High Cost of Field Failure

Source: CNN

Source: USA Today

Source: The Economist

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Automotive Recalls are Expensive

…VERY expensive

Semiconductor makers have to deliver products with zero defects

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Reducing Risk

RISK

Field Failure

COST

Design Manufacture

Contain

Resolve

Prevent @Product and Process Design

@Manufacturing

@Shipped @Manufacturing

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Advanced Analytics Algorithms

Advanced Anomaly Detection Detect out-of-pattern units/ decision support

unsupervised learning

Design Rules Operating zones /exclusion zones IFTTT / ML / supervised learning

Root Cause Non-linear explanatory approaches

ML /supervised learning

Contain

Resolve

Prevent

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Dashboards, Visualizations

Enterprise Class Infrastructure Hadoop, Big Data, Scalable

Advanced Analytics Algorithms … must accurately predict field failures

Advanced Anomaly Detection Detect out-of-pattern units/ decision support

unsupervised learning

Design Rules Operating zones /exclusion zones IFTTT / ML / supervised learning

Root Cause Non-linear explanatory approaches

ML /supervised learning

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The Problem

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, …

TECHNICAL GOAL: find multivariate marginality

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OUTLIER 6 σ 2 σ

6 σ 6 σ

2 σ 2 σ __ ?

CHALLENGE: Detect parts that are not similar to the rest of the population

The Problem

• 1000s of components • 1000s of solder points • 100,000s of vias • ~100s of part SKUs • ~10s of suppliers

o Lots of available multi-variate combinations = lot of opportunities for marginal units o Each parameter could be within tolerance but combination of parameters may be an outlier o Inability to detect multivariate problem process corners

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The Solution

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

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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

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Case Study 1

Large Electronics Manufacturer / Auto Who

Field Failure KPI

Composite Distance How

Detect field failures with high class purity

Result

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VarZ

VarY VarX

Anomaly Detection

Outlier yes no

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Com

posi

te D

ista

nce

Topm

ost p

aram

eter

Anomaly Detection

median + 6*robust σ

USL

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Five out of seven field failures are detected by Composite Distance…at low cost

Com

posi

te D

ista

nce

Topm

ost p

aram

eter

Anomaly Detection

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

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Composite Distance offers significant improvement over single parameter controls

UCL = Median + x * robust sigma

Accuracy Purity

Composite distance

Top Parameter

Detection Metrics

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Case Study 2

Large Semiconductor Company Who

Yield KPI

Machine learning algorithms How

Revenue increase by > $ MM/quarter Result

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Rule Discovery

Variables M, Q and T individually have no influence on Metric of Interest (MOI)

Data is normalized, scaled and transformed

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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

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Discover: Analytical Outputs

Response= 0.89653 - 0.916669 * BF1 - 0.012894523 * BF3 + 7.26853E-0059* BF4 + 2.847878 * BF6 - 1.023234 * BF7 + 3.0275966 * BF8; BF1 = max(0, X1 - 82.398); BF2 = max(0, 82.398 – X1); BF3 = max(0, X2 - 161.82) * BF2; BF4 = max(0, 161.82 – X2) * BF2; BF6 = max(0, 88.92 - TOP_X4); BF7 = max(0, X5 - 92.692) * BF6; BF8 = max(0, X6 - 38.109) * BF1;

• Ranked Variables of Importance • Non linear predictive model • Graphical Representations

100. Sub thresh leakage 96. Leff 95. CD 75. IDDQ…. 73. …

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Case Study 3

PV Solar Company Who

Cell Efficiency KPI

Machine learning algorithms How

Prevent cell efficiency loss by 30% Result

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Solar Panel Line Flow

A

B

C

D

Measurement at four sites all passing inspection but low cell efficiency

Algorithms discovered that it’s the ratio that matters = PATTERN DISCOVERY

Measures A, B, C, D fully in control and within normal distribution

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Case Study 3

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)

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EXCLUSION ZONE: Y - low process metric readings (< 24.5) X -low in line measure(< 81) Z (date) > something

Case Study 3: Solar

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Machine learning model predicts ~31% reduction in EFF in exclusion zone

Design Verification Validation HV Production

Zero Defect and the Product Lifecycle

Pre-proto-type A, B samples C, D Samples Production

• DPPM Forecast • Model Historical Data • Extract operating and

exclusion zones • Improve product and

process design

• 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 • Calculate DPPM • Improve process for

Safe Launch

• Ongoing Outlier Detection

• DPPM Monitoring • 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

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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

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THANK YOU!

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