Device Scaling vs. Process Control Scaling: Advanced Sensorization Closes the Gap. A presentation by Mark Reath at Global Foundries and Alan Weber of Cimetrix Inc.

1. Joint Symposium of e-Manufacturing and Design Collaboration 2017
and International Symposium on Semiconductor Manufacturing 2017 1
Device Scaling vs. Process Control Scaling:
Advanced Sensorization Closes the Gap
Mark Reath et al
Alan Weber
GLOBALFOUNDRIES
Cimetrix
Mark.Reath@globalfoundries.com
alan.weber@cimetrix.com

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and International Symposium on Semiconductor Manufacturing 2017 2
Agenda
 Introduction and motivation
 Application summary
 Application details and results
 Implementation architecture
 Conclusions

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and International Symposium on Semiconductor Manufacturing 2017 3
Advanced technology node requirements
 Increased complexity:
– Equipment, processes and devices
 High throughput
– Decreased mean time to detect
 Advanced sensorization required:
– Process equipment
– Sub-fab support equipment

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and International Symposium on Semiconductor Manufacturing 2017 4
Data acquisition methods, scope and
collection frequency
Acquisition Method Deployment Scope Collection Frequency
SECS II / GEM 300 Broadly deployed across the
industry
1-3 Hz
Interface A Deployed at fewer than 10%
of fabs worldwide, but growing
10-20 Hz
SCADA Broadly deployed across the
industry for support equipment
0.1-1 Hz
Equipment log files Narrowly deployed on select
equipment types
100 Hz
Specialty sensors Narrowly deployed on select
equipment types and
processes
RGA : 1 Hz
Electroplating: 10 kHz
Arc Detection: 250 kHz

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and International Symposium on Semiconductor Manufacturing 2017 5
Required data collection rates for
equipment operating modes
Equipment operating
mode
Required data collection
rate, KHz
Recipe window of
interest, ms
Plasma strike 1-10 1000
Wafer temperature ramp 0.1-10 5000
Electroplating wafer entry 1-10 1000
ALD process cycle 0.2-2 100
Requirements exceed tool capability by over 3 orders of magnitude!

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and International Symposium on Semiconductor Manufacturing 2017 6
Advanced sensorization
Problems addressed
Application Problems Addressed
Cu electroplating entry current • Voids
Residual Gas Analysis (RGA) • ALD: precursor delivery.
• CVD: chamber clean endpoint.
• PVD: chamber leaks and contamination
Tool log file parsing • Substrate and chamber charging
• Plasma instability
• Dynamic seal degradation
Sub-fab support equipment
monitoring.
• Premature equipment failure
• Predictive maintenance based on vibration /
acoustic methods

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Copper Electroplating

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and International Symposium on Semiconductor Manufacturing 2017 8
Electroplated Copper Fill Evolution

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and International Symposium on Semiconductor Manufacturing 2017 9
Mechanisms of Copper Electroplating
High-speed data capture requirement

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and International Symposium on Semiconductor Manufacturing 2017 10
Two-step Copper Plating Process
PlatingCurrent(arbitraryunits)
Time (sec)
Peak Entry
Current
DC 1
DC 2

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and International Symposium on Semiconductor Manufacturing 2017 11
High-Speed Data Collection
Peak entry current sampled at 10 kHzPlatingCurrent,arbitraryunits
Time, s

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and International Symposium on Semiconductor Manufacturing 2017 12
Residual Gas Analysis:
CVD Clean Endpoint

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and International Symposium on Semiconductor Manufacturing 2017 13
Application details and results
CVD clean endpoint, wafer repeatability
Excellent wafer-to-wafer repeatability

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and International Symposium on Semiconductor Manufacturing 2017 14
Application details and results
CVD clean endpoint, tool comparison
Multiple wafers, identical tool hardware and clean recipe

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Advanced sensorization
Challenges
Applications Challenges
Cu electroplating, tool log file
parsing
• Data volume
• Cost
Residual Gas Analysis (RGA) • Data volume
• Spectral data format
• Dataset complexity
• Cost
Sub-fab support equipment
monitoring
• Data volume
• Spectral data format
• Dataset complexity
• Integration with wafer context data
• Cost

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and International Symposium on Semiconductor Manufacturing 2017 16
Implementation Architecture
and
EDA Standards Leverage

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and International Symposium on Semiconductor Manufacturing 2017 17
External sensor integration example
Typical approach (and challenges)
2
3
4
5
6 7
1
8
Factory
Systems
Process
Engineering
Database
Sensor
Interface
Process
Tool
S S S
GEM
APC, FDC
SOA
TCP/IP
Equipment
Integration
Server
Local
DB
Equipment
Controller
Sensor Integration Challenges
1. Finding a sensor that works
2. Sampling/process synchronization
3. Dealing with multiple timestamps
4. Scaling and units conversion
5. Applying factory naming convention
6. Associating context and sensor data
7. Ensuring statistical validity
8. Aligning results in process database
Local
DB
6

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Advanced sensor integration
Problem and solution summary
 Problem statement
– Reduce effort required to parse complex sensor data on
equipment local file systems and merge it with the EDA-sourced
FDC data
– Sensor types include OES, RGA, pyrometers, NDIR, Mass spec,
high-frequency RF, QCM, …
 Solution components
– Format conversion, data compression, new EDA metadata types
and interface modules
 EDA leverage
– Multi-client capability, model-based interface definitions,
powerful data collection plan (DCP) structure
 Key ROI factors
– Tool availability, test wafer usage, engineering effort

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and International Symposium on Semiconductor Manufacturing 2017 19
Model-based interface definition
Additional sensors appear in same structure
Full Equipment Model
(from process equipment)
Partial Equipment Model
(from sensor integration platform)
Minimal
Equipment
Structure
High-level
Equipment
structure
Process
ChamberProcess
Chambers
Embedded
Sensors
External
Sensors

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and International Symposium on Semiconductor Manufacturing 2017 20
Advanced sensor integration example
EDA solution architecture, multi-client capability
Process
Equipment
EDAGEM
Advanced Sensor
Integration Gateway
Custom Sensor Drivers
FICS / MES
EDA Client
EDA Server
EDA Client
Advanced Sensor
Metadata Model
DCIM* DCIM
Sensor-specific
Applications
Process-specific
applications
Factory-level
EDA Client Apps
(DOE, FDC, PHM, …)
HTTP HTTP
To factory-level systems
Context data
Synchronization
data
S2 S3
Process
Engineering
Database
2
3
4
5
6
7
1
8
S2
HTTP
Local
Sensor
Database

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Conclusions

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Additional work needed
With equipment suppliers’ support
 Robust standard interface implementations
 Increased data collection rates
 Increased visibility into equipment behavior
 Improved time management and
synchronization
 Adaptation to multiple data types
 Sub-fab data integration
 Reduced costs

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Acknowledgments
 INFICON co-authors
– Dillon Gregory and Joshua Larose
 GLOBALFOUNDRIES co-authors
– Boyd Finlay, Jack Downey, Chris Reeves, Jeff Wood,
Patrick Minton, Niels Rackwitz, Eric Warren, Brian
Conerny, Mohamed Elmrabet, Ray Bunkofske