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Future Patterning Challenges: It’s not 2D, it’s 3D
Kevin HuangKLA Corporation
IWAPS 2019
KLA Non-Confidential | Unrestricted2
Agenda
▪ Technology trend
▪ Patterning control challenges for 3D devices
▪ Integrated data analytics to enable patterning control of 3D devices
▪ Metrology-guided defect discovery
▪ Cross-module optimization for multi-patterning
▪ Etch tilt control
▪ Summary
KLA Non-Confidential | Unrestricted3
Technology Trend2D Scaling Reaching Fundamental Limit Beyond 2021 (ITRS 2015, IRDS 2017)
1980 1990 2000 2010 2020
ArF Immersion Lithography with SAxP
EUV Lithography
500nm 250nm 130nm
65nm32nm
16nm
7nm
i-line (365nm) KrF (248nm) ArF (193nm)
5nm
1μm
g-line (436nm)
KLA Non-Confidential | Unrestricted4
2D 3D
5
10
15
20
25
30
2011 2015 2019 2023 2027 2031 2035
nm
Year
Lg for HP Logic Min HP for DRAM HP 2D Flash
Technology TrendDevice Architecture Evolving from 2D to 3D
FinFET Nanosheet
imag
e: im
ec
Vertical Memory
KLA Non-Confidential | Unrestricted5
Patterning Challenges for Logic and DRAM Devices
▪ Multiple patterning (SAxP and Len) will be extended to enable continued lateral scaling for leading-edge logic and DRAM devices
▪ 3D variabilities must be measured and controlled to ensure yield and performance
▪ Process variabilities occur in multiple steps, and must be controlled across modules
▪ Top, mid, bottom CD, SWA▪ Corner rounding, footing▪ Roughness▪ Recess, undercuts▪ Film thickness
γα β
Fin ~ Spacer2α ~ Spacer1β ~ Core1–2xSpacer2γ ~ Pitch-Core1-2xSp1-2xSp2
Within-module and cross-module control required to meet tight EPE budget
KLA Non-Confidential | Unrestricted6
Patterning Challenges for 3D Memory Devices
▪ Tier-tier misalignment results from a combination of litho overlay, etch tilt, films non-uniformity, topography, top-bottom CD errors
Align and focus over topography
In-Cell OVL
Litho overlay
Etch Tilt
Inter-connected process errors
Very thick w/ thickness non-uniformity
Topography variationCell
Kerf area
Kerf area
Kerf area
Thick hard mask
wafer warpage > 350µm; up to 700µm on 96 pairs
Variabilities in the vertical direction (topography, etch shape, films uniformity), and their interactions are limiting factors to 3D memory device performance and yield
KLA Non-Confidential | Unrestricted7
Open Architecture, Centralized Data and Analytics Platform
F a b - W i d e D a t a S o u r c e s
Overlay(ATL™, Archer™, SEM)
CD / Shape(SpectraShape™)
Films(SpectraFilm™, Aleris®)
Wafer Shape(PWG™)
Scanner(ASML / Nikon)
In Situ Process(SensArray®)
Centralized Data Warehouse
AnalyzeModelControl
5D Analyzer®
KLA Non-Confidential | Unrestricted8
Predictive Analytics Leveraging All Available Data Sources
Data integration across inspection, metrology, and process steps enables rapid detection and control of patterning excursions at the source
Review Station
AnalyzeMachine Learning
Predict
▪ Predictive Sampling▪ Yield Prediction
Predictive Analytics PlatformDefect and Yield
Metrology and Process
KLA Non-Confidential | Unrestricted9
Metrology-Guided Defect Review Sampling
OverlayLayer B-C
Dense OCD map (M1B)
BBP Inspection
Dense OCD map (M1C)
OVL_X OVL_Y OCD
Metrology Guided Sampling
5D Analyzer®5D Analyzer®5D Analyzer®
Sah, K. et. al. “Process Window Discovery, Expansion and Control of Design Hotspots Susceptible to Overlay Failures,” ASMC 2017
Modeled Metrology from 5D Analyzer®
KLA Non-Confidential | Unrestricted10
Predictive Sampling Improves SEM Effectiveness
14
54
3 1 3
175
129
32
8 4
49
15
DOI 1 DOI 2 DOI 3 DOI 4 DOI 5 SNV
91% reduction in SEM non-visual (SNV)
Lower SNV, Higher DOI caprateIncrease probability of catching defects
causing production excursions
Higher SEM EffectivenessAllow decrease in SEM sample plan while maintaining actionable pareto
POR SamplingPredictive Sampling
KLA Non-Confidential | Unrestricted11
Cross Module CDU Optimization for Multi-Patterning
Litho-Etch CDU Model
CD/ProfileTemp/Bias PowerFocus/Dose/CDFocus/Dose per field
Litho SpectraShape™ Etch SpectraShape™
5D Analyzer®
KLA Non-Confidential | Unrestricted12
Cross-Module CDU Optimization for Multi-PatterningA
DI C
D (
nm
)
Incoming CDU
SOG
–A
DI B
ias
(nm
)
SOG etch effect
SOC
–SO
G B
ias
(nm
)
SOC etch effect
AEI
CD
(n
m)
Final CDU
Actual CD (nm)
Pre
dic
ted
CD
(n
m)
Predictive model
+ΔT
-ΔTCENTER EDGE
ESC Temperature difference
SOG
CD
off
set
(nm
)
Bias Power offset
SOC
CD
off
set
(nm
)
▪ Improve CDU by Temperature at SOG
▪ Improve CDU by Bias Power at SOC
▪ Predictive model for Final CD post SOC
Quantify control opportunities and build correction model
KLA Non-Confidential | Unrestricted13
Cross-Module CDU Optimization for Multi-Patterning
Etch-aware litho feedback control improves AEI CDU by 50% at the cost of reduced DoFCorrection at the source is required to maximize process windows at each step
AEI
CD
U 3
σ(n
m)
50%
OCD
Exposure Latitude vs. DOF
Exp
osu
re la
titu
de
%
Compensated
5%
Depth of Focus
3%
Standard
75nm25nm
Etch-Aware Litho Feedback Control
KLA Non-Confidential | Unrestricted14
High Aspect Ratio Etch Tilt Control
Etch Tilt Model
Tilt X, Tilt YTilt X, Tilt Y
SpectraShape™ Etch SpectraShape™
5D Analyzer®
Etch Settings
Tool Specific Etch Tilt Model ▪ Focus ring height▪ Tunable edge sheath▪ Bias voltage
KLA Non-Confidential | Unrestricted15
Measure Etch Tilt with High Sensitivity
R2 = 0.85
Top CD: ±10nmThickness: ±10nm Factor Min Max
Hole Ellipticity 0.95 1.05
Orientation 0 90
Mask Taper (nm) 0 12
Underlayer Tilt (nm) -3 3
Bow in Oxide (nm) 6 10
Clear and measurable response to tilt using SpectraShape system.High sensitivity despite wide variation in etch figures of merit
KLA Non-Confidential | Unrestricted16
5D Analyzer Enabled Etch Tilt Minimization
5D Analyzer modeled optimal etch conditions to minimize tilt signature across the wafer
Nominal Tilt Optimized Tilt Nominal Optimized
Tilt Distribution(Inner: R<100mm; Outer: R>100mm)
Inw
ard
O
utw
ard
Inner Outer Inner Outer
KLA Non-Confidential | Unrestricted17
Summary
▪ As semiconductor devices become increasingly three dimensional in nature, patterning control must address variabilities in the vertical direction
▪ An open-architecture, centralized, fab-wide data warehouse and analytics platform enables rapid discovery and control of pattering variations at the source
▪ Provide predictive analytics by combining metrology, inspection, and process context data to accelerate yield learning
▪ Cross-module optimization and control maximize process windows at each step
▪ Integration of SpectraShape measurements with 5D Analyzer models enables reduction of etch tilt effects across the wafer
KLA Non-Confidential | Unrestricted18
Acknowledgments
▪ Gino Marcuccilli
▪ Barry Saville
▪ Antonio Mani
▪ Ankur Agarwal
▪ George Hoo
▪ Poh-Boon Yong
▪ Paul MacDonald
▪ Zhengquan Tan
▪ Markus Mengel
▪ Ramkumar Karur-Shanmugam
▪ Scott Corboy
▪ Ady Levy
Thank you!