Core Competency In Semiconductor Technology: 3...

Core Competency In Semiconductor Technology:3. RELIABILITY

3.1 PART I: IC AND COMPONENT RELIABILITY

Dr. Theodore (Ted) DellinChief Scientist of the Microsystems Center (retired), Sandia National Labs

Reliability Lead (retired), Intl. Technology Roadmap for SemiconductorsQuick Start Micro Training LLC, [email protected], SemiconductorTutorials.com

© 2016, Dellin, All Rights Reserved.

SAMPLE SLIDES & COURSE OUTLINE

A Easy, Effective, Impactful Working Knowledge™ of Component Reliability.Recommended for everyone who works with, or depends on, Semiconductor Technologies

Integrated With Other Core Competency Courses: 1. Devices & 2. Fabrication12 Hour Narrated eLearning or 14 Hour Live Class

Available Learning Formats: Live, Webinar, Narrated eLearning, Course Notes & PowerPoint Slides.

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Impactful Working Knowledge™Core Competency & Digging DeeperDevices, Fabrication & Reliability

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6. ACCELERATED AGING6.1 True Accelerated Aging6.2 Accelerated Aging Models6.3 Determining the Accelerated Aging Model and Model Parameters7. PREDICTING RELIABILITY7. 1 Predicting the Intrinsic Reliability of a Single Failure Mechanism, Part I7.2 Part II7.3 Predicting constant failure rate7.4 Predicting Multiple Failure Mechanisms7.5 Predicting Reliability With Redundancy8. QUALIFICATION8.1 Acceptance Testing8.2 Technology Qualification8.3 Product Qualification9. REPAIRABLE COMPONENTS9.1 Renewal and Non‐Renewal9.2 Predicting Reliability of Renewal Components

1. INTRODUCTION1.1 Introduction1.2 Course Overview2. DESCRIBING RELIABILITY2.1 Reliability Definition2.2 Quantifying Reliability2.3 Bathtub Curve2.4 Describing a Failure Mechanism3. MAKING HIGH REL COMPONENTS3.1 Competitive Reliability3.2 Achieving High Reliability, Part I3.3 Achieving High Reliability, Part II4. TIME TO FAILURE DISTRIBUTIONS4.1 Distribution of Times to Failure4.2 Exponential Distribution4.3 Weibull & Lognormal Distributions4.4 Plotting Times To Failure5. MEASURING TIME TO FAILURE5.1 Types of Measurements5.2 Determining the Parameters of the Time to Failure Distribution5.3 Confidence Limits5.4 Importance of Using the Correct Distribution

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12‐Hour NARRATED eLEARNING CourseCore Competency In Semiconductor Technology:3.1 PART I: IC AND COMPONENT RELIABILITY

Dr. Theodore (Ted) Dellin© 2015, Dellin, All Rights Reserved.

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CORE COMPETENCY CERTIFICATIONIN SEMICONDUCTOR TECHNOLOGIES

DEVICETUTORIALS‐ Semiconductors‐ Junctions‐MOS Transistor‐ IC & Scaling‐ Photodiode & Solar Cell‐ LED and Laser

FABRICATIONTUTORIALS‐Microfabrication Techniques‐Making Devices‐ Packaging‐Micromachining & Microsystems

RELIABILITYTUTORIALS‐ Integrated Circuit and Component Reliability‐ CMOS IC Failure Mechanisms

INTEGRATED “DIGGING DEEPER” COURSESDEVICE‐ Semiconductors‐ Junctions‐ Transistor‐ Optoelectronics

FABRICATION‐ Unit Processes‐ CMOS IC Technology‐Materials

RELIABILITY‐ Failure Mechanisms‐ Rel Engineering‐ Prob. & Statistics

FORMATS

Narrated eLearning

Webinars

PowerPoint Slides

In‐Person Tutorials

UNIQUE FEATURES

Focused on real world needs of tech people

Easy to understand. Picture how things work instead of focusing on equations

Seamless integration of all tutorials

• Chief Scientist of Microsystems Research, Technology & Components Center, Sandia National Lab

• Reliability Lead International Technology Roadmap for Semiconductors

• Reliability Technical Advisory Board, Sematech

• External Review Panel, NASA NEPP

• Tech. & Genl. Chair, IEEE Nonvolatile Memory Technology Symposium

• FLC Award for Technology Transfer

• 6 Tutorials at IEEE Reliability Physics Symp.

• Unique training courses for industry & gov’t.

35 years experience in semiconductor technologies, reliability and training including:

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©2015, Dellin, All Rights ReservedSemiconductorTutorials.com

Core Competency Tutorial:IC & COMPONENT RELIABILITYDr. Ted Dellin

Dellin Semiconductor TutorialsDevices, Fabrication & Reliability Made Easy

Dellin Semiconductor Tutorials, SemiconductorTutorials.com Sample Slides

MODULES SECTIONS

1. Introduction2. Describing Reliability

3. Making High Rel Components

4. Time to Failure Distributions

5. Measuring Time to Failure

6. Accelerated Aging

7. Predicting Reliability

8. Reliability Qualification

9. Repairable Components

Core Competency Tutorial:Integrated Circuit and Component Reliability Dr. Ted Dellin

1.1 IntroductionAuthor’s BioAcknowledgements

1.2 Course OverviewAdditional Resources

1



High reliability requirements are hard to realize and hard to verify

Need to control multiple failure mechanisms

Reliability is impacted by all aspects of making a product

Often involves difficult tradeoffs (e.g., reliability vs. performance)

Long lifetimes (~years) necessitates accelerated aging

Pressure to do faster, cheaper reliability

2

Why is High Reliability So Challenging? A Partial List


Design Manuf.Test &Screen

DeliveredProduct

Distribution of Times to

Failure

ReliabilityLevel

1. SpecificationsStresses Lifetime

2. Design for Rel.

3.Building

In Rel

5.Rel

Screen

6.Testing In Rel.

4.Improve Quality

Ability to Identify & Measure/Characterize Each Mechanism

Model for Each Failure Mechanism:1. Failure Time Distribution

2. Physics of Failure

3

Success I: Across-the-Board, Proactive Approach Rooted in Reliability R&D


• Target: All tech people who work with, or use, components

• No prior rel knowledge required – only college-level intro classes in physics & math

• Unique, easy-to-understand, efficient presentation of most useful material

• Students will have acquired a basic, useful, intuitive understanding of reliability Focused on “seeing” how things work

and how things fit together, not on equations

How their work impacts reliability How to critically assess reliability

specifications & qualifications If needed, students have a solid

foundation for in-depth courses

4

Course Goals


This One of a Series of Integrated Core Competency Tutorials in Semi Technology

DEVICESMADE EASY

FABRICATIONMADE EASY

RELIABILITYMADE EASY

• Semiconductors• Junctions• MOS Transistor• CMOS IC• Photodiode &

Solar Cell• LED and Laser

Dellin Semiconductor Tutorials

CORECOMPETEN

CY TUTORIALS

Easy to understand core knowledge for

everyone working with

semiconductors

• Microfabrication Techniques

• How Devices Are Made

• Packaging• Micromachining

& Microsystems

• INTEGRATED CIRCUIT AND COMPONENT RELIABILITY

• Failure Mechanisms

Also Integrated Set of More In-Depth TutorialsMore detail, but still easy-to-understand

5 Dellin Semiconductor Tutorials, SemiconductorTutorials.com Sample Slides

2. Describing Reliability

1. Introduction






8. Qualification


Only ConsiderNon-RepairableComponents In These Modules

Extend Material to Repairable Components

6

Outline of Course Modules






1. Introduction








2.1 Defining Reliability

2.2 Quantifying Reliability (Units)

2.3 Bathtub Curve

2.4 Describing a Failure Mechanism


MODULES SECTIONS


7 Dellin Semiconductor Tutorials, SemiconductorTutorials.com Sample Slides 8

Reliability Is Determined BOTH By The Component and By Its Specifications

RELIBILITY

COMPONENT• Design• Materials• Processing• …

SPECIFICATIONS• Failure criteria• Stresses• Lifetime• …

Change the component or its specifications and you change reliability


• Total fraction of parts that FAILED by time t: F(t)

– Called Cumulative Distribution Function, CDF

– Increases with time

• Most common way to show distribution of times to failure is to plot F(t) vs. t

• Always: F(t) + R(t) = 19

Equivalent Ways of Specifying Reliability II:Cumulative Failure, F(t)

F(t)

R(t)0

1.0

Time

Example:Exponential Distribution


FailureRateh(t)

TimeProduct Delivery

LifetimeRequirement

Onset of Wear-Out

Early Life Failure Rate: Initially

Higher,But Decreasing

“Constant” Lower Failure Rate

End of Life Wear-Out Increasing

Failure Rate

10

The Bathtub Curve Has 3 Regions of Failure Rates


• Failure MECHANISM is a physical/chemical process that can produce the Failure Mode– Generally, there is more than one mechanism that potentially

could cause the failure mode

– Failure mode can result from a single dominant mechanism or from the interaction of several failure mechanisms

Failure Mode

Broken Metal Line

Failure Mechanisms

Stress Voiding

Electromigration

Corrosion

11

Failure Mechanism Is The Process Than Caused the Failure Mode


BuildingReliable Products

Ensuring Reliability Specifications Are Met

FOR EACH RELEVANT FAILURE MECHANISM

- Identify the Mechanism

- Develop Ability to Characterize the Mechanism

- Develop Models that Describe Time to Failure Distribution and How Mechanism Depends on Stresses

12

Component Reliability Engineering (and this Course) Are Based on Failure MECHANISMS






1. Introduction








3.1 Competitive Reliability

3.2 Achieving High Reliability, Part I

3.3 Part II

Dellin Semiconductor TutorialsTechnology Made Easy

MODULES SECTIONS



• Meet customer’s reliability expectations– In fact

– and in perception

• Eliminate unnecessarily large reliability margins– “Just Enough” reliability for the application

– Allows performance and/or functionality to be increased

– Increases the value of the product

• Faster, cheaper realization of reliability– Without compromising ability to achieve and

demonstrate 14

Goal: Competitive Reliability


Worse Cu Stress VoidingSingle Via Over Wide Metal

Initial LayoutHorizontal

Metal Stripe

Vertical Via

After Design For

Reliability

Eliminate or minimize number of these critical vias. Focus test structures on these critical vias.

15

Design for Reliability Example: Layout of Cu Lines and Vias


• Develop technology with no end of life wear-out during specified use

• Develop models for designing-in reliability

• Establish process controls and monitors to ensure reliability is maintained during product

• Monitor process for reliability changes

• Take corrective action if problems develop

16

Building-In Reliability (BIR)

Building In Reliability (BIR)

Technology Development Manufacturing


Constant Failure Rate(FIT)

With 60% Statistical

Confidence

With 99% Statistical

Confidence

# Parts Tested With 0 FailsRequired to “Prove” Failure Rate

% Of Parts

Failing In 10 Years

17

The Reliability Testing Crisis: Unacceptably Large Samples Needed for Very High Rel

1,000

100

10

1

~10%

~1%

~0.1%

~0.01%

11

110

1,100

11,000

53

530

5,300

53,000

Constant Failure Rate; Test time = Specified Lifetime; Numbers are RoundedInspired by a table presented by Dwight Crook, Intel



DeliveredProduct


Failure

1. Specifications

ReliabilityLevel

Stresses Lifetime

2. Design for Rel.

3.Building

In Rel

5.Rel

Screen

Ability to Identify & Measure/Characterize Each Mechanism

6.Testing In Rel.

4.Improve Quality

Model for Each Failure Mechanism:1. Failure Time Distribution

2. Physics of Failure

18

Underlying All Approaches Is the Need to Measure and Model Failure Mechanisms





SECTIONS


1. Introduction








4.1 Distribution of Time to Failure

4.2 Exponential Time to Failure Distribution

4.3 Weibull and Lognormal Time to Failure Distributions

4.4 Plotting Time to Failure Distributions

MODULES


19



Reliability Uses Sampling

• The “Population” is all the products we are concerned about

– e.g., all Integrated Circuits produced in a technology

• The “Sample” needs to be representative of the population

– e.g., 40 parts randomly drawn

• Measure the lifetimes of the sample parts to predict the lifetime distribution of the population

• There always is a degree of uncertainty when using a sample to predict a population

Population(All Parts

Manufactured)

Sample of Parts

20


Requires a statistical function that describesshape of the distribution of times to failure

There always is adistribution in the

times-to-failurefor every

failure mechanism

Typically, we usereliability data froma sample of parts

to predict the reliabilityof all parts used

Quantify the UncertaintyDue to Sampling

Extrapolate Data ToLow Failure Percentiles

21

Two Reasons Why We Need to Use Statistical Distributions in Reliability

Dellin Semiconductor Tutorials, SemiconductorTutorials.com Sample Slides 22

Exponential Distribution: Constant Failure (Hazard) Rate, h(t)

• Lack of Memory Property: Failure rate is constant with time

– Looks like middle part of bathtub curve

Time

Failure Rate, h(t)


Why Are The Weibull and Lognormal Distributions So Widely Used in Reliability?

CommonAnswer

Each has 2 adjustable parameters (time scale and shape) that allow them to fit many time-to-failure distributions.

More Fundamental Answer

Each distribution has a theoretical basis that allows them to model two very common classes of failure mechanisms: weak link mechanisms (Weibull) and degradation mechanisms (Lognormal)


For Any Distribution The Axes Can Be Transformed So That F(t) Is a Straight Line

F(t)

t

Transformed F(t)

Transformed t

LINEARF(t) & t Axes

F(t) vs. t is a curve

TRANSFORMEDF(t) & t Axes

Axes are transformed in such a way that for the

given statistical function (e.g., lognormal) F(t) vs. t is a line













5.1 Types of Measurements

5.2 Determining the Parameters of Time to Failure Distributions

5.3 Confidence Limits Due to Sampling

5.4 Importance of Using the Correct Distribution

MODULES SECTIONS


1. Introduction

25



There Are A Lot of Choices inTime to Failure Measurements

TimesTo

FailureMeasurement

Actual Part or Surrogate?

Exact or Interval Failure Times?

Wait Until All Parts Fail?

Sample Size?

Degree of Accelerated Aging?

The choices made are based on what is the purpose of gathering the data


How To Determine the Parameters That Give The Best Fit?

Time To Failure

F(t)

“Best Fit” is one that makes best reliability predictions

There is no single, best answer.

There are several options for-How the data points are plotted-How the distribution parameters

are determined


What If the F(t) Data Is Curved On a PlotThat Should Give a Straight Line?

• Could be due to natural randomness when we select a finite number of samples from a distribution– Confidence limits (discussed later) can help

us decide if this is the cause

• Or it could be the wrong distribution– Be careful, need a very large number of

points to be sure

• Or it could be that we have an extrinsic (defect) distribution and intrinsic (main) distribution

• Or the sample is not uniform: – E.g., variation in oxide thickness

Ideally, would get this

F(t)

t

But, what if you get this?

F(t)

t


Time to Failure (Days)

F(t)

%

Failed

29

Higher Confidence = Lower Reliability Prediction (Fewer Days to X% Failing)

At 80% confidence,

t50% is greater than

117 days

But at 99.95%

confidence it is greater

than 49 days!


Choice of Statistical Distribution Makes a Big Difference in Time to Failure Predictions

C1

Perc

ent

1000100101

99.99

99

95

80

50

20

5

1

0.01

Loc

0.225

4.496Scale 0.5207N 20AD 0.467P-Value

Probability Plot of C1Lognormal - 95% CI

C1

Perc

ent

1000100101

99.99

9580

50

20

5

2

1

0.01

Shape

>0.250

2.523Scale 112.8N 20AD 0.191P-Value

Probability Plot of C1Weibull - 95% CI

Same Data Fit to Weibull Same Data Fit to Lognormal

Time Time

1-Sided 97.5%

1-Sided 97.5%

Notice that the predictions of the two distributions of the time to 0.01% failures differ by an order of magnitude!













6.1 True Accelerated Aging

6.2 Accelerated Aging Models

6.3 Determining Accelerated Aging Model and Model Parameters

MODULES SECTIONS


31


1. Introduction


We Need To Extrapolate from Accelerated Stresses to Specified Stresses

Fast TestSeconds - Weeks

Higher Stress

ModelHigh to Normal

Stress

Real LifetimeYears

Specified Stress

Reliability Prediction


There Is An Upper Limit To How FastA Failure Mechanism Can Be Accelerated

Stress

Log ofTime to Failure

USECONDITIONS

Same FailureMechanism As Use

Condition

“TRUE” ACCELERATION

Max Stress forTrue Acceleration

INVALID OVERACCELERATION

Changed Failure Mechanism


Acceleration Factor For Room Temperature Use (25 ºC)

• Curve shows the acceleration factor for a part used at 25ºC as a function of

– Temperature it is stressed at for accelerated aging

– Activation energy

• Acceleration factor increases with

– Increasing temperature

– Increasing activation energy

50 100 150 2001

10

100

1 . 103

1 . 104

1 . 105

EA=1.0eV

EA=0.7eV

EA=0.3eV

EA=0.1eV

AF

For

25 ºC

Use

Stressing Temperature (°C)


Characterize Temperature Acceleration:III. Plot Log of Mean Times To Fail Vs. 1/t

T1T2T3

• For Arrhenius acceleration should look like a straight line

• Slope of line determines activation energy, EA

F(t)

1/T1 1/T21/T3

Log(t63)

1/Temperature

Time

Activation Energy, EA


Acceleration Factor Uncertainty Causes Uncertainty In Times At Use Conditions

0.01 0.1 1 10 100

Time

F(t)

0.001

Uncertainty InAcceleration

Factor

Uncertainty In Times to Failure Under Normal

Use Conditions

For example an activation energy of 0.7 +/- 0.05 eV

leads to a factor of 3 uncertainty in projecting failure times from 150C

burn in to a 30C use condition

EA

EA-

EA+






1. Introduction








7.1 Predicting Intrinsic Reliability for a Single Failure Mechanism, Part I

7.2 Part II

7.3 Predicting Constant Failure Rate

7.4 Predicting Reliability With Multiple Failure Mechanisms

7.5 Predicting Reliability With Redundancy

MODULES SECTIONS


37



Steps Needed to Predict Intrinsic FailuresDue to A Single Failure Mechanism

Identify Failure Mechanism

Develop Ability to Measure Time-to-Failure

Determine Statistical & Acceleration Models

Determine Acceleration Model Parameters

Measure Accelerated Times-to-FailureAnd Extrapolate to IC Use Conditions


6. Predicting Cumulative Number of FailuresFor Specified Lifetime

Time To Failure

F(t)6. Use The Specified

Lifetime to Find the Fraction That

Failed at the Specified

Confidence Level

Predicted IC

FailuresAt

LifetimeSpec

6

LifetimeSpec

6


Exercise:Which IC Is More Reliable?

• To answer the question have we have to make two common assumptions about these predictions;

– Failure rates were determined from tests with 0 failures

– Constant failure rate (exponential distribution) was assumed

3 Failures at 10 Years99% Confidence

1 Failures at 10 Years60% Confidence

?Company BCompany A


Case II: COMPETING Risk Modelof Multiple Failure Mechanisms

FailureMechanism

1

FailureMechanism

2

. . .

• Each component can fail by one of several failure mechanisms

• Failure mechanisms are independently of each other

• The first failure mechanism to reach its failure criteria causes the component to fail

• The mechanisms compete to see which can be the first to cause failure

COMPONENT


The Biggest Improvement Due to Redundancy Comes at Early Times

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

0.005

0 1000 2000 3000 4000Time (hrs.)

Failure Rate h(t)

1

42

At Early Times (With Few Cumulative Failures)The Failure Rate Improvement Due to Adding Redundant Components Is Relatively Large

Number of Redundant Components(1= No Redundancy)

At Later Times (With Many Cumulative Failures)

The Failure Rate Improvement Is Smaller













8.1 Acceptance Testing

8.2 Technology Qualification

8.3 Product Qualification

1. Introduction

MODULES SECTIONS


43



Due to Sampling Uncertainties There Are Always Risks In Using Acceptance Plans

AcceptanceCriteria

(e.g., ≤c fails out of n)

BUT also has a Risk, β, of ACCEPTING some actually BAD lots

(Consumer’s Risk)

BUT also has a Risk, α, of REJECTING

some actually GOOD lots

(Producer’s Risk)

Rejects SOME Lots That Actually Are Bad

Accepts SOME Lots ThatActually Really Are Good

ACCEPTREJECT


1. Specifications


DeliveredProduct


Failure

ReliabilityLevel

Stresses Lifetime

2. Design for Rel.

3.Building

In Rel

5.Rel

Screen

6.Testing In Rel.

4.Improve Quality

TechnologyDevelopment

Technology Development Is Where Building-In-Reliability & Quality Is Implemented


Failure(Hazard)

Rate

TimeProduct Release

LifetimeRequirement

Onset of wear-out

46

Technology Qualification Has Reliability Goals For All 3 Regions of Bathtub Curve

Failure Rate Meets Spec

1. Extrinsic (Option to use a

Screen)

2. Constant

Margin

3. Wear-out does not occur during

specified lifetime (“Margin”)


An Accelerated Test on an IC Can Over or Under Test Individual Failure Mechanisms

0

5

10

15

20

25

30

35

40

45

-0.2 0 0.2 0.4 0.6 0.8 1

Equivalent Years of

Operation For a Given

Failure Mechanism

Activation Energy (eV) of Failure Mechanism

Test conditions: Temperature & Time Equivalent to 10 Years Using EA=0.7 eV

Ho

t Carrier

Electro

-M

igratio

n

Oxid

e Breakd

ow

n


48

Knowledge Based Qualification: Mechanism by Mechanism Bottom Up Approach

OxideBreakdown

Stress TestStructure To Fail

Acceleration& Stat. Model

Electro-Migration



PackageCracking



Each Relevant Failure

Mechanism

Test Structures Until We Get a Distribution of

Failures

Use Physical & Statistical

Models

Application-Specific Use Conditions and

Lifetime

ReliabilityPrediction

ForProduct






1. Introduction








9.1 Renewal and Non-Renewal

9.2 Predicting Reliability of Repairable, Renewable Components

MODULES SECTIONS


49



In Repairable SystemsA Part Is Replaced When It Fails

Time

RemoveFailedPart

Replace With Good

Part


Repairable Systems Can HaveRenewal or Non-Renewal Processes

• A Renewal Process is a special type of Repairable System

– Replacement part is identical to the failing part with the same distribution of times-to-failure

– After replacement system is “good as new”

– Average rate of replacement does not change over time

• Otherwise the repair process is Non-Renewal

– Non-Renewal Processes are harder to analyze

• In this module we will only consider renewal processes

RepairableNon-Renewal

Renewal


At Any Given Time There Is a Distributionof Possible Cumulative Failures

0

2

4

6

8

10

12

0 2000 4000 6000 8000

Exponential Distribution of Times to FailureMean Time Between Failures = 500 hours

Cumulative Number of

Repairs

Operating Time (hours)

e.g., At 4000 hours there is a range of

possible cumulative

number of failures


Knowing Time To Failure Distribution Allows Answering Important Questions

As described earlier in this

tutorial determine:

Time to Failure distribution

type and distribution parameters

Median cumulative repair function, M(t) – the average number of repairs by time t

Distributions of times by which the kth (e.g. 5th) repair has occurred

Distribution of number of cumulative replacements that have occurred by a fixed time (e.g.,1000 hours)

AllowsPrediction

Of


Example: How Many Spares Are Needed To Ensure 90% Mission Success?

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10

ProbabilityOf HavingMore ThanK or MoreFailures

K

90% Success Means Want 10% or Less Chance of Running Out of Spares

10% Probability

With 8 replacements we have <10% chance of mission failure(>90% chance of mission success)

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