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Paradigms Shifts in CMP Daniel Redfield Director - NMA
CMPUG - Semicon West 2018
External Use
Para
digm
Sh
ifts
in C
MP
2
State of CMP Market Inflections are driving CMP Growth and Complexity
Paradigm Shifts in CMPStrategies to address CMP Complexity
SMART CMPTailored CMP Pads
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NAND
3D NAND► More W & oxide CMP► CMOS Under Array► Stacked Cells► Multi-Material Polish
LOGIC
3D Transistor: FinFET Co contact & local interconnect 3D Multi Patterning
► SA Gate Contact
Large Die Form Factors► GPU
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CMP Enabled Inflections
ADVANCED PACKAGING
Wafer Level Packaging► Image Sensors► Redistribution Layer (RDL)► Through Silicon Via (TSV)
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5
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15
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25
30
1 2 3 4
Relat
ive N
o of
CMP
Pas
ses
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20
30
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60
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Relat
ive N
o of
CMP
Pas
ses
3D NANDGen 1
3D NANDGen 3
3D NANDGen 2
2.5D Si INTERPOSER
3D DRAM
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How will we use AI enabled SMART CMP?
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CMP Input Parameters.
Hardware MaterialsEndpoint Pressure/Velocity
Chemistry Delivery Temp. Control
Slurry Pad
Conditioner Retaining Ring
Polymer
GroovePore
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Open Loop CMP
Stability achieved through reduced Input Parameter variability.
Static Process Recipe
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Input Output
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Property Input Change
Dynamic Process Control
Static Process Recipe
Process Controlled CMP
Performance improved through Dynamic Process Control of Input Parameters.CMPUG Semicon West 2018 -External Use
Input Output
Output
Dynamic Process Control (Property + AI)
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Property Input Change
AI Augmented Process Controlled CMP
AI has the promise of optimizing output through the interpolation of multiple Input Parameters.CMPUG Semicon West 2018 -External Use
Input Output
Output
Warning with regards to AI Processing
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Output success is a function of algorithm training.
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CMP Process Optimization
CMP Polish process versatility continues to enable new integration schemes.
Die Layout
Polish Inputs
Layout Opt.Dummy Fill
Polish Process
Hardware Input Mod.
MaterialsInput Mod.
Performanceto Specification
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Each Process Input has a Structure/Property Model
Hardware MaterialsEndpoint Pressure/Velocity
Chemistry Delivery Temp. Control
Slurry Pad
Conditioner Retaining Ring
Polymer
GroovePore
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10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100
Die level5-20mm
Wafer300mm
Log Scale(m)
Feature Arrays25-3000 m
Device Features10 nm
Cell Periphery
CMP Pad Structure/Property Input Parameters
Critical Length ScalesCMPUG Semicon West 2018 -External Use15
Pad Porosity10-50 m
Polymer Chains50-100 nm
10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100
Die level5-20mm
Wafer300mm
Log Scale(m)
Pad Groove100-500 m
Feature Arrays25-3000 m
Device Features10 nm
Pad Rebound10-100 mm
Polish Element 0.5-3 mm
Cell Periphery
Polymer Domains500-5000 nm
CMP Pad Structure/Property Input Parameters
Structure/Property relationships can be deployed to design locally optimized CMP pads.CMPUG Semicon West 2018 -External Use16
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CMP Pad Input Parameter Optimization
Inputs validated against Outputs. Results are developed into Pad Structure/Property Algorithms.
Domain
GroovePore
Feature Pad Rebound
Polymer Chain
Polish Process
Structured DOE
Baseline Polish Recipe
Experimental Inputs
Fixed Inputs
Pad Property Inputs
Per
form
ance
Out
put
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CMP Pad Design Optimization
Rigorous Modeling efforts allow for targeted optimization of CMP Inputs and improvementto Die level Performance Outputs.
Domain
GroovePore
Feature Pad Rebound
Polymer Chain
Polish Process
Structured DOE
Baseline Polish Recipe Pad Property Inputs
Per
form
ance
Out
put
Experimental Inputs
Fixed Inputs
Pad Input Model
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Dynamic Process Control
SMART CMP
SMART CMP: Input Parameter Model optimized AI deployed using Dynamic Process Control.
AI Input Models
SMART CMP
Domain
GroovePore
Feature Pad Rebound
Polymer Chain
Polish Process
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