marginal test for dram¡°원철.pdf · 2008. 11. 14. · tech (d/r) ↓ ~ input ↑ 1st regarding...

Marginal Test For DRAM

조원철조원철

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Contents

IntroductionBasic conceptStrategyStrategyLimit ?

Fundamental Concept

Concept DescriptionStatistical Grouping for Failure AnalysisOptimal Criteria Extraction from Test ResultOptimal Criteria Extraction from Test Result Test Time Reduction using Statistical Grouping

Experiment

1/25

Summary

Introduction

For the Future of DRAM industry,What is the most IMPORTANT thing for mass production ?

Unit process ?Technology Shrink ?gyDesign ?Test Engineering ?Test Engineering ?Defect control ?...

2/25

Introduction

Basic ConceptProfit Ratio

)(O t t Cost)()((%)

eaInputeaOutputYield = ASPYield

Cost⋅

−≈1

StrategyASP ↑……↓(Who knows? )ASP ↑……↓(Who knows? )Cost ↓ 3rdOutput Die ↑ 2ndpTech (D/R) ↓ ~ Input ↑ 1st

Regarding technology scaling, roughly assuming that …Saturation yield of new Tech.≒ Saturation yield of conventional Tech.

3/25

Increase of vital defect density? It will be overcome !!!

Introduction (Design Rule)

100%

Limit ?DRAM Season 2.

with Repair

DRAM Season 1.: No Repair Scheme

(No Redundancy )256Kb DRAMD/R : 2um

80%

with Repair

# of Original Good Die>> 0

D/R : 2um

60%

eld(

%)

DRAM Season 2.: With Repair Scheme

20%

40%Yie : With Repair Scheme

# of Original Good Die

1 10 100 1000 10000 1000000%

20%

DRAM Season 1.without Repair

g≒ 0

1 10 100 1000 10000 100000

D/R(nm)

Defects Say “ Catch me, if you can “

4/25

y , y“ Are you ready for Season 3 ? “

Introduction (Yield Up)

Limit ?100%

Assume that…

Same Tech.Same Fab

e.g. 1Gb DRAMD/R : 100nmN t Di 200

80%

Same Fab.Same Equip....

Net Die : 200

60%

ield

Yield of 1Gb DRAM

20%

40%Yi Yield of 1Gb DRAM

>> 2Gb DRAMe.g. 2Gb DRAMD/R : 100nm

0%

20% Net Die : 100

Arbitrary Defect Count

Statistics Say “ Catch me, if you can “

5/25

y , y“ Was he born with a ‘silver spoon in his mouth’ ? “

Introduction (Test Cost Reduction)

Mass production of DRAMis up to test/product technology.

Profit ratio

Ti i ld ASPYieldCost⋅

−≈1Time is gold

Strategy for Testing !!!

ASPYield ⋅

gy g

Statistical grouping

Test method for output

Test time Reduction

And so on

6/25

And so on……

Fundamental Concept

Overview

# of wafer

Yield

Test Time

Time

Phase A : Research Phase B : Development Phase C : Mass ProductionPhase A : Research Phase B : Development Phase C : Mass Production

7/25Gamma distribution Gamma distribution

Fundamental Concept

Let’s assume that

Target

YieldA

F ilB

FailDefect

F ilFail Fail Fail

Real

Quantities Quantities Quantities Q titiQuantities Quantities Quantities Quantities

Failure analysis routine for yield upF/A Wafer Selection : Fail Rate > Average No

Failure analysis result Feed Back Fix Yield up ? OK D f t C t l T t C f “DEFECT MODELING”

8/25

Defect Control Target : Come from “DEFECT MODELING”

Fundamental Concept

Wafer Selection for Failure Analysis

Fail Bad Group Bad Group

Bad Group

Good Group Good Group

Quantities

Good Group

C 1 C 2 C 3Quantities Case 1 Case 2 Case 3……

Case 1, 2, 3,……Which one do you want to select ?

It fully depends on F/A Engineer !!!

9/25

It fully depends on F/A Engineer !!!

Fundamental Concept

Probe Test Condition

Internal Voltage Window

U TestL Test

A Category Internal Voltage

ControlTarget

Window

g yFail Bit Count

gTarget

Internal Voltage

Upper MarginLower Margin U Test Condition

L Test Condition

Assume that all characteristics of transistors satisfy specification.

10/25

Fundamental Concept

Probe Test Result vs. Characteristics Result (F/A Result)

Time

μ,σ

ControlTarget

Tr. Vth Window

A CategoryFail Rate

Tr. VthTarget

U TestL Test

Tr Vth (Wafer Average)

Upper MarginLower Margin U Test Condition

L Test Condition

11/25

Tr. Vth (Wafer Average) ConditionCondition

Concept Description

Statistical Grouping

Lot A Yield Case1 Case2 …… Case14Wafer1 100 Normal Normal NormalWafer2 80 Abnormal Normal NormalWafer3 60 Abnormal Abnormal NormalWafer4 40 Abnormal Abnormal AbnormalWafer4 40 Abnormal Abnormal Abnormal

Number of Cases : 2 Groups for 4 wafers : 24 = 16-2 = 14

From Statistical Analysis (ANOVA, t-test……)

Statistics Case1 Case2 …… Case nR2 . . … .F-Ratio . . … .Prob >FProb >F . . … .

Choose case x having the maximum R2 value among all cases.

12/25

From this result, we can select reasonable wafers for F/A.

Concept Description

Criteria of Factor A Lot A 1 2 … 10

Statistical Grouping by Criteria (Factor)

Criteria

Response

10Wafer 1 Wafer 2…...Wafer N

StatisticsP-value

If Factor A : Min 1

P value . …R2 ... .

Max 10

In Multivariate Analysis, it is working

13/25

Concept Description

Window ExtractionAssume that Fail 1 ∝ Function of 2 Variable (A,B)

F il 2 F ti f 2 V i bl (A B)Fail 2 ∝ Function of 2 Variable (A,B)Same weight value,

Fail 1Fail ↑b

Fail 2Fail ↑

Fail 1,2

Y-axisVar. A

Y-axisVar. A

Y-axisVar. A

aAcceptableWindow

X-axis : Var.B X-axis : Var.B X-axis : Var.B

Window

14/25

Failure mechanism can be different between region a and b

Concept Description

Wafer CharacteristicsAssume that Characteristics Distributions are exactly Normal.

we know μ σwe know μ, σ

Test Items : High Test, Low Test, Base TestTest Time : 10min /Items

Low Char HighLow Char. High

From Statistical Grouping

Test Group A Group B Group CTest Group A Group B Group CB Test go go goH Test skip skip goL Test go skip skip

15/25

L Test go skip skipTest Time ↓ ↓ ↓

Experiment

Statistical GroupingStatus of Wafers

100

120

)

100

20

40

60

80

Mar

gin

Fail(

%)

60

80

%) 120

140160180

%)

0 10 20 30 400

40

Ran

ge(%

020406080

100120

Def

ect F

ail(

0

20

80

100

120

%)

0 20 40 60 80 1000

Yield Defect Fail MarginFail

0 20 40 60 80 1000

20

40

60

Yiel

d(%

16/25

0 20 40 60 80 100

Range(%)

Experiment

Statistical GroupingR2 of Old Grouping

All Avg. 79%Count 221

D/F > 9 , M/F < 12

Avg. 66%Std Dev 17%F/A

Defect Fail 9%

Std. Dev. 17%Count 61

F/A

D/F < 9 , M/F > 12

Avg. 72%Std. Dev. 8%

Count 35

D/F < 9 , M/F < 12


Count 125Grouping ResultWafer Count for F/A

F/A

Margin Fail 12%

Count 35Count 125 Wafer Count for F/ADefect Fail : 61Margin Fail : 35

R2 : 0 44

Target Model

17/25

Margin Fail 12% R2 : 0.44

Experiment

Statistical Grouping ( Fish born Diagram )R2 of New Grouping


D/F > 37,

Defect Fail 37% Avg. 27%Std. Dev. 10%

Count 7D/F >37, M/F > 19D/F < 37 , M/F < 19,

D/F > 8

F/A

G i l


Count 33


Count 45

F/A

Defect Fail 8%

F/A

Grouping ResultF/A Wafer Count

Defect Fail : 7M i F il 33

D/F < 37 , M/F < 19,D/F < 8

Margin Fail : 33Defect + Margin : 45

R2 : 0.861Margin Fail 19%Target Model

Avg. 85%Std. Dev. 3.4%

Count 136

18/25

Margin Fail 19% Target Model

Experiment

Statistical GroupingProbe Test Result vs. Electric Parameter of Measurement (EPM)


Para 7

Para127 > 21Para 7 < 7.4

Para 127 > 21Para 7 > 7.4

Target Model


Count 130


Count 51

F/A

Para 127

Para127 < 21Para 51 > 15

Para 127 < 21Para 51 < 15

Grouping ResultWafer Count for F/A


Count 25


Count 15

Wafer Count for F/A40 + 51 = 91

R2 : 0.853

F/AF/A

19/25Para 51

Experiment

Statistical GroupingWafer Selection for Margin Failure Analysis


Defect Fail 37% D/F > 37,


Count 7D/F < 37

D/F > 9

F/A

D/F < 37D/F > 9

Para 85 > 0.05


Count 33f il 9%

Para 85 < 0.05


Count 17

F/A

Defect Fail 9%

D/F < 37D/F < 9

Para 65 > -0.27

D/F < 37D/F < 9

Para 65 < -0.27

Grouping ResultWafer Count for F/A

Para 65 > 0.27


Count 117

Para 65 < 0.27


Count 47

Defect Fail : 7Margin Fail vs. Para65 : 47 Defect + Margin

F il P 85 17

F/A

20/25

Fail vs. Para85 : 17R2 : 0.856

Experiment

Test Window EvaluationW/F SELECTION from statistical grouping in terms of marginal fail, and EPM

characteristicscharacteristics

Para 65 : Low W/F Para 65 : High W/F

F1F2F1

F4

F3

Var

iabl

e Y

F4

Var

iabl

e Y F2

F5

F3

F5

Variable XVariable X

Variable x, y : Control Factor of Test ConditionF1 n : Test Items Line : Acceptable Fail Bit Count for A Repair

21/25

F1,…n : Test Items, Line : Acceptable Fail Bit Count for A RepairResult from Using the RSM (Response Surface Method)

Experiment

Test Window EvaluationMerged Result

F1F3

Y

F4

F3

Varia

ble

Y

F5 Suggested Condition

V

F2

Old Test Condition

22/25

Variable X

Experiment


Test Items Selection

F1F2F1

F4

F3


iabl

e Y

F4iabl

e Y F2

F3

Var

i

F3

Var F5

Variable X

F5

Variable X

F3 Test can be skipped ! F4 Test can be skipped !

23/25

Experiment

Test Window EvaluationMargin Fail Rate : Old vs. New

15

20

25

te(%

)

90

9899.5

-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4

nts

5

10

Mar

gin

Fail

Ra

2

10

305070

mul

ativ

e C

oun

Old New

0

0.01

0.52

242628

Cum

21

24

12141618202224

unts

12

15

18

n Fa

il R

ate(

%)

2468

1012

Cou

3

6

9

New

Mar

gin

24/25

-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 40

Bin

3 6 9 12 15 18 21 24

Old Margin Fail Rate(%)

Summary

Strategy for Survival of Future DRAM industryNot Tech (D/R),

But Increase of Output Die Cost Reduction by Statistical ModelingBut Increase of Output Die, Cost Reduction by Statistical ModelingUntil Season 3,

Shrinking down of the Design Rule can not be worked at yield up.

Requirements for the Yield UpStatistical Grouping Statistical Analysis

Requirements for the Test Time ReductionStatistical Grouping Statistical Test Condition

25/25

Statistics says “ I still know what you did last SEASON! “

marginal test for dram¡°원철.pdf · 2008. 11. 14. · tech (d/r) ↓ ~ input ↑ 1st regarding...

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