Fault Detection & Classification (FDC) and Fault ... · PDF fileISMI Symposium on Manufacturing Effectiveness 2011. 19th October -Advanced Process Control Session. Fault Detection

ISMI Symposium on Manufacturing Effectiveness 2011. 19th October - Advanced Process Control Session. Fault Detection & Classification (FDC) and Fault Prediction (FP) on Vacuum/Abatement Systems. Edwards Ltd. Michael Mooney, Shane Butler (NUI Maynooth)

ISMI Symposium on Manufacturing Effectiveness 201119th October – Advanced Process Control Session

Fault Detection & Classification (FDC) and Fault

Prediction (FP) on Vacuum/Abatement Systems

Authors: Michael Mooney, Shane Butler (NUI Maynooth, Ireland)

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Contents

Edwards Introduction

Edwards FabWorks-iMS & EADS

EADS Detectable/Classifiable Faults

EADS Development Process

EADS Fault Detection Example – TPU Liner Deposition

Fault Prediction – Particle Filters

Summary & Research Moving Forward

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Edwards - Vacuum & Abatement Systems Specialist

• Equipment installed

globally

• > 750,000 vacuum pumps

• > 250,000 pumps in

semiconductor

• > 8,500 Point Of Use (POU)

abatement devices

• Experienced global service

network

• 850 service support

engineers & specialists

• On-site & remote

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• FabWorks iMS – Intelligent Monitoring System

• Networks all Edwards equipment

• >160 FabWorks systems, monitoring >100K vacuum & abatement devices

• EADS – Edwards Advanced Diagnostic Services

• Fault Detection & Classification (FDC) and Fault Prediction (FP)

• Utilized by experienced global service & product specialists network

• Users notified of alert situations via FabWorks display, pager, email, SMS

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EADS Fault Detection & Classification (FDC) & Fault Prediction (FP) Capability

Warning – Predictive

Software Started

9 days before fault

Warning –

Recommend PM

4 days before fault

Alarm – Do Not

Commence Processing

8 hours before fault

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EADS Fault Detection & Classification (FDC) & Fault Prediction (FP) Capability

Warning: Predictive

Software Started

9 days before fault

Warning:

Recommend PM

4 days before fault

Alarm: Do Not

Commence Processing

8 hours before fault

Model Optimization

Optimization of logged

parameters and logging

rates

Technical Evaluation

Global Applications Knowledge

Reports

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FDC & FP Case Study – EADS Model on LPCVD Nitride

• A year prior to introduction of EADS multi-parameter deposition models (approx 60 pump scale)

• 5 catastrophic faults

• A year following introduction of multi-parameter EADS deposition models

• 1 catastrophic fault, 5 similar fault conditions predicted & proactively managed

• Benefits

• Significant wafer value savings derived, with improved security/yield of critical batch process

Warning – Recommend PM

15 days before swap-out

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• Examples:

• Turbo Pump Deposition & Turbo Pump Leak Detection on Ion Implant• Catastrophic pump faults prevented & 33% extended PM intervals (24 pump sample).

• ALD High-K Dry Pump Process Deposition • 4 swaps & 200% lifetime extensions on some systems (20 pump sample).

• Trap/Pump Exhaust Blockage Models on LPCVD Nitride• >90% of unplanned stoppage events now prevented (60 pump sample).

• Solar Amorphous - Dry Pump Deposition, F & Cl Corrosion, Exhaust Blockage • 5 pump faults prevented,13 prevented unplanned production stops & 25 pump life time extensions

(180 pump sample).

• Abatement Combustor Blockage & Abatement Liner Deposition• Savings equate to 125 hours per CVD tool per year (8 tool sample).

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EADS project development requires:

• Identify probable FDC/FP models from Knowledge Base

• Data collection - optimisation of logged parameters and logging rates

• Data processing – data characterisation & transformation

• Data mining/modelling to configure models to customer process

• To distinguish normal operating characteristics from those associated with abnormal operation and fault based behaviour

• Training of the model: use of analytics, learning algorithms

• Testing of model using ‘unseen’ customer data

• System implementation of models

• Verification of success

• Reduced faults & downtime

• Fault/Technical analysis

EADS Iterative Development Process – Problem to Solution

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EADS Model Development – Data Processing

• Data processing forms a key step in characterizing and

transforming the data in preparation for modeling, incorporating:

• Advanced moving window methods

• Smoothing, multiple time based differentials (gradient & variance)

• Event characterization (area & frequency)

• Gaussian Mixture Models (GMMs)

PUMP

FAULT

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EADS Model Development – Machine Learning

• Machine Learning techniques have included:

• Artificial Neural Networks, Rule Induction/Decision Trees

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EADS Model Development

Warning – Predictive Monitoring Started

1.5 months before fault

Warning - Recommend PM

15 days before fault

Alarm – Do not Start a New

Process

5 days before fault

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Edwards Thermal Processing Unit (TPU)

A. Head Unit

B. Combustion Chamber

C. Quench Unit

D. Cyclone Scrubber

E. Packed Tower Scrubber

POU Abatement Device

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Equipment Layout & Fault Example

Fault example –

deposit build up on

Ceramic Liner,

aversely affecting

abatement efficiency.

1. Natural Gas & Oxygen

2. PFC Containing Process

Effluent

3. Radiation Exchange

4. Ceramic Liner

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Gaussian Mixture Models

A probabilistic tool for density estimation

using a superposition of individual densities

where,

subject to,

Data Processing – Gaussian Mixture Models (GMMs)

• Gaussian Mixture Models (GMMs) have been applied as an

effective technique to detect TPU Liner Deposition faults

Component1

Component2

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Abatement TPU Signal Tracking

• Distributions within two windows, before & after deposition

starts

T(oC)

Mode1

Mode2

Mode1Mode2

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Abatement TPU GMMs by Operating Mode

Mode1 Mode2

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The content of this presentation is confidential and should not be distributed to a third party without prior authorization from Edwards. © Edwards Limited 2010

Abatement TPU GMM Signal Tracking

Mode1 – Component1 Tracking




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EADS Fault Detection Example – TPU Inlet Blockage

Alarm – Do Not Start A

New Process

3 days before unplanned

stoppage

Warning –

Recommend PM


stoppage

Warning –

Predictive Monitoring Started


stoppage

T(oC)

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Contents


FabWorks-iMS & EADS






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Fault Prediction – Particle Filters – An Introduction

• By predicting evolution of the TPU fault indicator measurement

into the future, we can estimate the Remaining Useful Life (RUL)

• To address prognostic uncertainty it makes sense to represent the

TPU degradation level as a random variable, with an associated

probability density

• Suggests use of recursive Bayesian estimation techniques for prognostics

• Particle Filters implement a recursive Bayesian filter via Monte

Carlo sampling

• Approximates the state pdf as a set of particles and weights

• Capable of handling non-linear models and/or non Gaussian noise

processes

• Application to fault diagnosis and prognostic problems growing in recent

years. Examples include fatigue crack growth estimation [Orchard, 2007]

and lithium-ion battery degradation [Saha et al, 2009]

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Fault Prediction – Model Based Prognostics

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Fault Prediction – Particle Filters – Projecting Current State

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Fault Prediction – Particle Filters – Estimate Remaining Useful Life (RUL)

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Contents


FabWorks-iMS & EADS






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• Edwards Advanced Diagnostics Services (EADS) successfully employs

FDC/FP techniques optimized for the behavior of vacuum & abatement

equipment in semiconductor applications

• Fault Detection & Classification (FDC) and Fault Prediction (FP)

• Multi-parameter rule-based system

• Gaussian Mixture Models – highly suitable with emerging ‘green’ modes

• Particle filters – for Remaining Useful Life estimates (RUL) & adaptable for different

processes

• FDC/FP techniques used reduce failures, downtime, improve maintenance

scheduling & pooling

• Research moving forward includes:

• Multi-model particle filters

• Predictions based on Process Count information

• Automated Learning

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End

Thanks for listening

Any questions?

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Fault Detection & Classification (FDC) and Fault ... · PDF fileISMI Symposium on Manufacturing Effectiveness 2011. 19th October -Advanced Process Control Session. Fault Detection