Speech given at the Innovation Forum Automation 2018 - Device Scaling vs. Process Control Scaling by Alan Weber (Cimetrix), Mark Reath, André Holfeld

1. Device Scaling vs. Process Control Scaling: Advanced
Sensorization Closes the Gap
Mark Reath, André Holfeld, et al
Alan Weber

2. Agenda
GLOBALFOUNDRIES 2
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

3. Agenda
GLOBALFOUNDRIES 3
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

4. 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

5. 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

6. 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!

7. 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

8. Agenda
GLOBALFOUNDRIES 8
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

9. Electroplated Copper Fill Evolution

10. Mechanisms of Copper Electroplating
High-speed data capture requirement

11. Two-step Copper Plating Process
PlatingCurrent(arbitraryunits)
Time (sec)
Peak Entry
Current
DC 1
DC 2

12. High-Speed Data Collection
Peak entry current sampled at 10 kHz
0.5sec  5k samples & time  ≈100kB
150sec  1500k samples & time  ≈30MB  >5..10GB/day
PlatingCurrent,arbitraryunits
Time, s

13. Agenda
GLOBALFOUNDRIES 13
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

14. Application details and results
CVD clean endpoint, wafer repeatability
Excellent wafer-to-wafer repeatability

15. Application details and results
CVD clean endpoint, tool comparison
Multiple wafers, identical tool hardware and clean recipe

16. 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

17. Agenda
GLOBALFOUNDRIES 17
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

18. Implementation Architecture
and
EDA Standards Leverage

19. 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

20. 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

21. 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

22. 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

23. Agenda
GLOBALFOUNDRIES 23
Introduction and background
Case1: Copper Electroplating
Case2: RGA - CVD Clean Endpoint
1
2
3
Implementation architecture4
Conclusion5

24. 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

25. Agenda
GLOBALFOUNDRIES 25
Questions?

26. • 감사합니다
• 唔該
• Merci
• Danke
• 多謝
• ありがとうございます
• Gracias
Thank you
26

27. Acknowledgments
• Mark Reath for analyzing and preparing data
• 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

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