Data-Driven Tool Architectures The Gateway to Quality Equipment Data Authors: Glen Gilchrist , Senior Systems Engineer, Axcelis Technologies Larry Bourget , Director Product Management, Axcelis Technologies Kourosh Vahdani , Vice President Global Services, Cimetrix, Inc.

The Challenge A higher quality and quantity of data is necessary to achieve optimized equipment and process control performance, resulting in higher wafer yield and equipment reliability. Current tool architectures do not allow for high frequency data collection from lowest levels of the tool.

A higher quality and quantity of data is necessary to achieve optimized equipment and process control performance, resulting in higher wafer yield and equipment reliability.

Current tool architectures do not allow for high frequency data collection from lowest levels of the tool.

What is Tool Control? Supervisory Control User Interface Server User Management Job Management Standard / Custom UIs Data Visualization Data Analysis Scheduler Configuration Management Alarm Management Recipe Management Status Message Logging Factory Automation Equipment Control EFEM Loadports Loadlocks Transfer Module Process Module Sub-systems Device Logic Modules I/O Services I/O Level Simulation Implemented Standards E5 – SECS E30 – GEM E37 – HSMS E39 – OSS E40 - PJM E94 - CJM E87 - CMS E90 – STS E95 – UI E116 - EPT E84 – AMHS PIO E99 – Carrier ID E120 – CEM E125 – EqSD E132 – CA&A E134 – DCM Integra RS™

What is Data Distribution? Process Performance Process Parameters Equipment Parameters

Process Performance

Process Parameters

Equipment Parameters

Old Way…..

Data-Driven Architecture

Data-Driven Architecture Enables Advanced Features High-speed, high quality diagnostic and processing data are fed to factory interfaces, an on-tool database, and the GUI to optimize productivity. This data speed and quality is required for: Equipment Data Acquisition (EDA)/ Interface A Advanced Process Control (APC) Fault Detection & Classification (FDC) Run-to-Run Control (R2R) Predictive & Preventative Maintenance (PPM) Enhanced Equipment Quality Assurance (EEQA) Enhanced Equipment Quality Management (EEQM)

High-speed, high quality diagnostic and processing data are fed to factory interfaces, an on-tool database, and the GUI to optimize productivity.

This data speed and quality is required for:

Equipment Data Acquisition (EDA)/ Interface A

Advanced Process Control (APC)

Fault Detection & Classification (FDC)

Run-to-Run Control (R2R)

Predictive & Preventative Maintenance (PPM)

Enhanced Equipment Quality Assurance (EEQA)

Enhanced Equipment Quality Management (EEQM)

Integra Using CIMControlFramework™ Data-Driven architecture provides high-speed access to higher quality and quantity data Simple interfaces ensure extensibility for future enhancements Uses the latest Microsoft™ .NET technology Uses WCF and SOA for scalability and distribution Use Cases…

Data-Driven architecture provides high-speed access to higher quality and quantity data

Simple interfaces ensure extensibility for future enhancements

Uses the latest Microsoft™ .NET technology

Uses WCF and SOA for scalability and distribution

Use Cases…

Analysis of Integra Development Data for PPM High quality data can be analyzed from a local database using a commercial package Data can be published at a high throughput via Interface A Data is also available via traditional SECS interface

High quality data can be analyzed from a local database using a commercial package

Data can be published at a high throughput via Interface A

Data is also available via traditional

SECS interface

Data Analysis Capabilities Process Control FDC / PPM and Plasma Characteristics Vacuum System Characteristics (“health”) MW Power and Source Gas Box and Manifold Chamber, Chuck and Pin Lifter Transfer Module and Load Locks Wafer Handling and Robots Use Cases EP signal charting and statistics Plasma ignition time, plasma ignition retry counter Preheat pressure control

Process Control

FDC / PPM and Plasma Characteristics

Vacuum System Characteristics (“health”)

MW Power and Source

Gas Box and Manifold

Chamber, Chuck and Pin Lifter

Transfer Module and Load Locks

Wafer Handling and Robots

EP signal charting and statistics

Plasma ignition time, plasma ignition retry counter

Preheat pressure control

Process Control: EP Signal Charting Initial process control provided through EP signal matching Drill down to investigate out of specification cases Time 3 Minutes 25 Wafers 6 Hours 2000 Wafers 10 Seconds Single Process

Initial process control provided through EP signal matching

Drill down to investigate out of specification cases

Overlay signals or tool operating parameters Process Control: EP Signal Charting Times Signal Intensity Parameter Value

Overlay signals or tool operating parameters

Track Vital Statistics provide warning at low (or high) end of specification provide alarm for out of specification condition Process Control: EP Signal Statistics Signal Area Signal Height terminal failure caused process module to error out and shut down Wafer Number

Track Vital Statistics

provide warning at low (or high) end of specification

provide alarm for out of specification condition

Time from power supply command to plasma detected Delays and multiple retries indicate defective system FDC / PPM: Plasma Ignition Time and Retry Counter Times MW Power and Plasma Signal Daily Statistics Table Daily Box Plot Ignition Time Single Wafer Ignition out of control ignition times

Time from power supply command to plasma detected

Delays and multiple retries indicate defective system

Use daily statistics to create time series plot Investigate out of specification and adverse trends FDC / PPM: Plasma Ignition Time

Use daily statistics to create time series plot

Investigate out of specification and adverse trends

Preheat Pressure Control Setting requires interaction between vacuum and gas supply systems Time Series: showing 2 processes in 2 chambers Leaky vacuum valve identified Daily Statistics Table Pressure, Torr Box Plot: showing distribution and outliers

Setting requires interaction between vacuum and gas supply systems

Preheat Pressure Control Setting requires interaction between vacuum and gas supply systems PM1CH1 recipe 1-5 PM1CH2 recipe 1-5 PM2CH1 recipe 1-5 PM2CH2 recipe 1-5 Pressure, Torr Bar Chart: showing chamber and recipe PM1CH1 PM1CH2 PM2CH1 PM2CH2 Box Plot: showing variation of preheat pressure around set point experimental flow control component

Setting requires interaction between vacuum and gas supply systems

Leak isolation valve and change in flow control affect preheat pressure Preheat Pressure Control

Leak isolation valve and change in flow control affect preheat pressure

Conclusions With Integra’s Data-Driven architecture, based on CIMControlFramework, the capability to meet the stringent tool performance and reliability requirements of the future is available today. As presented in the Use Cases, Process Control, FDC/PPM and Equipment “health” monitoring is possible due to the availability of quality data.

With Integra’s Data-Driven architecture, based on CIMControlFramework, the capability to meet the stringent tool performance and reliability requirements of the future is available today.

As presented in the Use Cases, Process Control, FDC/PPM and Equipment “health” monitoring is possible due to the availability of quality data.

Acknowledgement This work was part of a successful joint development project between Axcelis Technologies and Cimetrix. Significant contributions were made by both teams, led by Dan Mattrazzo (Axcelis, Project Manager) and Bill Grey (Cimetrix , Director of R&D).

This work was part of a successful joint development project between Axcelis Technologies and Cimetrix.

Significant contributions were made by both teams, led by Dan Mattrazzo (Axcelis, Project Manager) and Bill Grey (Cimetrix , Director of R&D).

Questions?