marginal test for dram¡°원철.pdf · 2008. 11. 14. · tech (d/r) ↓ ~ input ↑ 1st regarding...
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Marginal Test For DRAM
조원철조원철
wonchoel.cho@hynix.com
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
Avg. 86%Std. Dev. 3%
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
All Avg. 79%Count 221
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
Avg. 63%Std. Dev. 7%
Count 33
Avg. 76%Std. Dev. 4%
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)
All Avg. 79%Count 221
Para 7
Para127 > 21Para 7 < 7.4
Para 127 > 21Para 7 > 7.4
Target Model
Avg. 86%Std. Dev. 3%
Count 130
Avg. 77%Std. Dev. 3.5%
Count 51
F/A
Para 127
Para127 < 21Para 51 > 15
Para 127 < 21Para 51 < 15
Grouping ResultWafer Count for F/A
Avg. 65%Std. Dev. 3.7%
Count 25
Avg. 41%Std. Dev. 16%
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
All Avg. 79%Count 221
Defect Fail 37% D/F > 37,
Avg. 27%Std. Dev. 10%
Count 7D/F < 37
D/F > 9
F/A
D/F < 37D/F > 9
Para 85 > 0.05
Avg. 76%Std. Dev. 4%
Count 33f il 9%
Para 85 < 0.05
Avg. 59%Std. Dev. 7%
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
Avg. 86%Std. Dev. 2.5%
Count 117
Para 65 < 0.27
Avg. 74%Std. Dev. 7%
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
Para 65 : Low W/F Para 65 : High W/F
Test Items Selection
F1F2F1
F4
F3
Para 65 : Low W/F Para 65 : High W/F
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! “
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