sophisticatedalgorithmsusedin thearea of defectpatternrecognitionon wafers · 2011-07-05 ·...

Sophisticated algorithms used in the areaof defect pattern recognition on wafers

SDS - Signature Detection System Thomas Kreutzmann

AMD Fab 36 LLC & Co.KG - Dresden, Germany

AEC/APC Europe, April 2008

2 3/20/2008

� Introduction / General Overview

– Task of Inline Defect Metrology at AMD Fab 36

– Why need an automated Signature Detection System?

� Goals of Project

� System Overview

– Data Flow

– Operation and Processing

– Clustering and Classification algorithms

– Features

Content

3 3/20/2008

What is “In-Line defect metrology” ? Line and equipment monitoring / In-line quality assurance

Process steps:- Film deposition- Lithography- Etch process

Process steps......

Process steps......

Process steps...

Time of processing: 4 to 12 weeks

In-Line defect metrology step:

Bare Si wafer start

Process end- electrical test

# of die per wafer - $$$

- Defect data / trends limit

- Images

- Wafer maps

Detect issues as early as possible

Prevent trailing lots from beingaffected by same issue

Run countermeasures based on “Trouble Shooting Guide”

4 3/20/2008

What is “In-Line defect metrology” ? Challenges for quality assurance in a fully automated Fab

No need - and almost no possibilities - for „real time“manual interventions by fab personnel under standardmanufacturing conditions

Fully automated manufacturing –also for metrology / defect metrology

Defect inspection steps

1) Automatic lot selection2) Automatic FOUP delivery3) Automatic recipe select4) Automatic wafer selection

Defect Data + ADCDefect Data + ADC

Defect review steps (SEM)

1) Automatic lot selection(indicated by OOC of scan)

2) Automatic FOUP delivery3) Automatic recipe select4) Automatic wafer selection

Defect review DataDefect review Data

5) Manual classificationclassification

5 3/20/2008

MQ Series

HTTPHTTP

CORBACORBA

DB2

APF/RTD

ActivityManager

CEI

Tool

SAPPHIRE

Ab Initio

LoaderARMOR

ASPECT

APC

CatalystFDC

ORACLE

ADAPT

MQ Series

SiViewMM

SMMMMM

Sch WD XMS

AMMO

AMHS-EI

RTD_API

SAP/PM TelAlert

MACS

AMHS

FabGUI

CORBA

MDS

PCMS

EMS

EPPM

eDR

HTTP

PPR

ePPCD

eLS

FabView

Yield Manager

MQ Series

HSMS

eSPEC

What is “In-Line defect metrology” ? Challenges for quality assurance in a fully automated Fab

Defect Data Volume:

~ 6 to 8 Mio defects per day~ 80.000 defect images per day

~ 7000 wafer scans per day

Cannot cover everythingbased on defect density or

ADC limits.

Inspect all wafermapsmanually for signatures?

Can raise error actions in „real time“ if signature

recognized?

Need an automatedsignature detection

system!

6 3/20/2008


– Task of Inline Defect Metrology at AMD Fab36



� System Overview

– Data Flow



– Features

Content

7 3/20/2008

Goals of Project

Automated detection and classification of signatures at wafer level…

Scratches

Stacked wafermap Particlesignatures

wafer edge “ring”

8 3/20/2008

Goals of Project

- Enable reliable automated detection of signatures

- Arcuated scratches

- Straight scratches

- Clouds

- Cluster

- Rings

- Ensure scalability and flexibility

- Multiple compute node system for fast feedback

- Maintainability through powerful setup GUI

- Seamless integration into MES environment

9 3/20/2008

Signature Examples

10 3/20/2008

Signature Examples

11 3/20/2008

Signature Examples

12 3/20/2008

Signature Examples

13 3/20/2008

Signature Examples

14 3/20/2008


– Task of Inline Defect Metrology at AMD Fab36



� System Overview

– Data Flow



– Features

Content

15 3/20/2008

FactoryControlSystems

Fab MES

SPC Software

Data Warehouse

...

System Integration / Data Flow

SDS Main Server

(Dispatcher)

...

additional compute nodes

YMS

Wafer Inspection Equipment

SDS EI

Tool EIs

DFS

1

2

3

4

5

5

5

16 3/20/2008

System Processing / Overview

Context based Filter &

Signature Select

Clustering

SDS Server Application

Zoning

Prioritization

Write

File

Search &

Parse

Classification

SDS GUI Application (Config. & Tuning)




Process steps......

Process steps......

Process steps...

Bare Si Wafer start

In-Line defect metrology step



C1 C2 P

17 3/20/2008

System Processing / Context Matching

C1 C2 P

check for Sig.2 and Sig. 3* / Process 1 / Layer 2

check for Sig.2Prod 2 / * / *

check for Sig.1Prod 1 / Process 1 / *

ActionContext




Process steps......

Process steps......

Process steps...




1. Context Matching

SDS Context System

• Which wafer / KLAR files to check?

• What types of signatures to check?

• Context configuration based on product,

product group, process, layer

� Enhances system classification

performance

18 3/20/2008

System Processing / Context Matching

C1 C2 P1. Context Matching

SDS Context Configuration

• By a simple to use GUI

• Wild cards for powerful context matching

• Multiple signatures per context with

prioritization

• Different variants of signature models for

different contexts

• …

19 3/20/2008

System Processing / Clustering

2. Clustering

- Partitioning of data into subsets

- Density based DbScan algorithm by Martin Ester et al. fits in most cases

- Clustering parameters definable for every version of a Signature Model

C1 C2 P

20 3/20/2008

System Processing / Clustering

Density-Based Clustering- For each object of a cluster the neighborhood of

a given radius (ε) has to contain at least a minimum number of points (MinPts).

- An object o is direct density-reachable from an object q wrt. ε and MinPts in a set of objects D if: o ϵ N

ε(q) and Card(N

ε(q)) ≥ MinPts

- An object p is density-reachable from an object q wrt. ε and MinPts in a set of objects D, denoted as p >D q, if there is a chain of objects p1, …, pn ,p1 = q, pn = p such that pi ϵ D and pi+1is directly density-reachable from pi

- An object p is density-connected to an object q wrt. ε and MinPts in a set of objects D if there is an object o ϵ D such that both p and q are density-reachable from o.

- A cluster C wrt. ε and MinPts in D is a nonempty subset of D satisfying the following conditions:

1. Maximality ∀∀∀∀p,q ϵ D: if p ϵ C and q > D p then q ϵ C

2. Connectivity ∀∀∀∀p,q ϵ D: p is density-connected to q

Core point

Circle of a given radius ε

Noise

p

q

o

21 3/20/2008

System Processing / Classification

3. Classification

- Refinement of clustering based on model of geometric shape:

- Line segment � straight scratch

- Arc � polish scratches

- Ring � rings

- Polygon � spots, Clouds

- Two algorithms were implemented:

ACC = Adaptive Compensation Calculation

(Systema) applied to sharp contours

(like scratches and spots)

EM (Expectation Maximization) applied to

soft contours (like clouds and rings)

C2C1 P

22 3/20/2008


EM = Expectation Maximization

- finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables

- E-step computes an expectation of the likelihood by including the latent variables as if they were observed

- M-step which computes the maximum likelihood estimates of the parameters by maximizing the expected likelihood found on the E-step

- signatures to be found on wafer maps were modelled using geometric shapes

23 3/20/2008


EM - Initialization

24 3/20/2008


EM - Iteration

Start

Result

25 3/20/2008


EM - Iteration

Result

26 3/20/2008


EM - Result

27 3/20/2008

System Processing / Zoning

4. Zoning

- Possibility to attach a typical region on wafer for each signature type

- A signature will only be detected if it is mainly located inside a zone

- Useful to exclude areas with blurred defect information

- Speeds up recognition

PC1 C2

28 3/20/2008

System Processing / Zoning

4. Zone - ExamplesPC1 C2

Search here for Signature Type A only

Search here Signature Type C only

Search here for Signature Type B only

…

29 3/20/2008

System Processing / Prioritization

C1 C25. Prioritization

- Possibility to prioritize Signature Models at certain contexts

- Defects in super-posed signatures will be assigned to prioritized Signature Model (see next slide)

P

30 3/20/2008

System Processing / Prioritization

5. Prioritization – Example

- Priority of scratch model ishigher than priority of ring model at a given context

- Each defect will be assigned to one defect class only

- Thus, defects belonging to scratch will be classified as such, even though they residein the proximity of the ring

31 3/20/2008

System Integration intoFactory Control System

- Use classification data in Yield Management System

- Feed SDS results via modified Equipment Interface intoMES/SPC System

- Send instant notification via Defect Feedback System (DFS)

32 3/20/2008

System Integration intoFactory Control System

Yield Management System

„P“ indicates classdata of pattern

recognition source

33 3/20/2008

System Integration intoDefect Feedback System (DFS)

- Flexible definition of rules and recipients for notification- Instant wafer history

34 3/20/2008

Thanks...

... to all who contributed to SDS development!

Special thanks to:

Jens Klobes, Robert FenskeLead Developers Systema

Dr. Remo Kirsch, Dirk Jung, Dr. Manfred Heinz, Nico NoackKey Customers/AppAdmins AMD Fab36/CFM

35 3/20/2008

Backup

36 3/20/2008

SDS Technical Features

Server Application

� Java based

� Service Oriented Architecture

� Application Server (Apache Tomcat)

� Oracle Database (for persistence data, statistics)

� Scalable by additional compute nodes

� OS independent (Solaris, Windows, Red Hat Linux)

GUI

� Fast and easy configuration, pattern detection & performance monitoring, training & tuning

� RCP (Eclipse) based

� User roles and privileges

� LDAP security interface & management

37 3/20/2008

Trademark Attribution

AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. Other names used in this presentation are for identification purposes only and may be trademarks of their respective owners.

©2006 Advanced Micro Devices, Inc. All rights reserved.

sophisticatedalgorithmsusedin thearea of defectpatternrecognitionon wafers · 2011-07-05 ·...

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