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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.

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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.

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What is Tool Control?Supervisory Control

User Interface ServerUser ManagementJob ManagementStandard / Custom UIsData VisualizationData AnalysisSchedulerConfiguration ManagementAlarm ManagementRecipe ManagementStatus Message LoggingFactory Automation

Equipment Control

EFEMLoadportsLoadlocksTransfer ModuleProcess ModuleSub-systemsDevice Logic ModulesI/O ServicesI/O Level Simulation

Implemented Standards

E5 – SECSE30 – GEME37 – HSMSE39 – OSSE40 - PJME94 - CJME87 - CMSE90 – STS

E95 – UIE116 - EPTE84 – AMHS PIOE99 – Carrier IDE120 – CEME125 – EqSDE132 – CA&AE134 – DCM

Integra RS™

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What is Data Distribution?

• Process Performance

• Process Parameters

• Equipment Parameters

Internal Interfaces External Interfaces

Internal Tool Data

Database(TDI)

SECSGEM 300 Interface

EDA InterfaceASCII

Data File

Process Module(s)

I/O Controller(s)

EFEMVacuum & Internal Hardware Modules

On-Tool AnalysisGUI

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Old Way…..

Database(TDI)

SECSGEM 300 Interface

EDA Interface

ASCII Data File

SequencerGUI On-Tool Analysis

Process Module(s)

I/O Controller(s)

EFEMVacuum & Internal Hardware Modules

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Data-Driven Architecture

Database(TDI)

SECSGEM 300 Interface

EDA Interface

ASCII Data File

Process Module(s)

I/O Controller(s)

EFEMVacuum & Internal Hardware Modules

SequencerOn-Tool Analysis

Data Distribution Framework

GUI

Tool Services

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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)

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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…

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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

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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

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Time

3 M

inu

tes

25 W

afer

s6

Ho

urs

2000

Waf

ers

10 S

eco

nd

sS

ing

le

Pro

cess

Process Control: EP Signal Charting

Initial process control provided through EP signal matching Drill down to investigate out of specification cases

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Overlay signals or tool operating parametersTimes

Sig

nal

In

ten

sity

Par

amet

er V

alu

e

Process Control: EP Signal Charting

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Track Vital Statistics provide warning at low (or high) end of specification provide alarm for out of specification condition

Process Control: EP Signal StatisticsS

ign

al A

rea

Sig

nal

Hei

gh

t

terminal failure caused process module to error out and shut down

Wafer Number

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Time from power supply command to plasma detected Delays and multiple retries indicate defective system

Times

MW

Po

wer

an

d P

lasm

a S

ign

al

Daily Statistics Table

Daily Box Plot

Ign

itio

n T

ime

Single Wafer Ignition

FDC / PPM: Plasma Ignition Time and Retry Counter

out of control ignition times

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Use daily statistics to create time series plot Investigate out of specification and adverse trends

FDC / PPM: Plasma Ignition Time

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Preheat Pressure Control

Setting requires interaction between vacuum and gas supply systems

Daily Statistics Table

Pre

ssu

re, T

orr

Box Plot: showing distributionand outliers

Time Series: showing 2 processes in 2 chambers

Leaky vacuum valve identified

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Preheat Pressure Control

Setting requires interaction between vacuum and gas supply systems

PM1CH1recipe 1-5

PM1CH2recipe 1-5

PM2CH1recipe 1-5

PM2CH2recipe 1-5

Pre

ssu

re, T

orr

Bar Chart: showing chamberand recipe

PM1CH1 PM1CH2 PM2CH1 PM2CH2

Box Plot: showing variation of preheat pressure around set point

experimental flow control component

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Leak isolation valve and change in flow control affect preheat pressure

Preheat Pressure Control

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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.

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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).

Questions?