fan-tien cheng...4 the importance of quality and the visions of industry 4.1 industry 4.0 values...

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2019年9月19日

鄭芳田Fan-Tien Cheng

國立成功大學智慧製造研究中心Intelligent Manufacturing Research Center (iMRC)

National Cheng Kung University

2

Outline

Introduction to Intelligent Manufacturing Research Center (iMRC)

Lead to Industry 4.1 by iMRC

Automatic Virtual Metrology (AVM)

Intelligent Factory Automation ( ) Alliance

How the Cloud-based Operates

Main Features & Benefits of

System Platform Demos

Promotion & Business Model of the System

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智慧製造研究中心簡介

Introduction to

Intelligent Manufacturing Research Center

(iMRC)

4

The Importance of Quality and

the Visions of Industry 4.1 Industry 4.0 values productivity, but overlooked the importance of quality

Industry 4.0 stresses highly on improving the productivity of production lines, but with less emphasis on quality. This

makes it impossible for the factories to achieve the goal of zero defects. The key reason is the lack of an affordable

and practical online real-time total inspection system.

Samsung Note 7 battery defects causing over 24 billion USD of loss

Take the flaws of the Samsung Note 7 cellphone battery production process for example, while demanding high

productivity from the production line, the quality of the products is relatively neglected. According to the

estimation of Bloomberg, Samsung lost 2 billion USD of revenue and the market value of its stock depreciated about

22 billion USD.

Zero Defects and the Visions of Industry 4.1

From the example mentioned above can we understand that it’s important not to overlook the quality of products

while pursuing productivity. By integrating the intelligent services such as Automatic Virtual Metrology (AVM),

Intelligent Predictive Maintenance (IPM), and Intelligent Yield Management (IYM) into the Advanced

Manufacturing Cloud of Things (AMCoT) platform, the goal of zero defects can be achieved. This is defined as

“Industry 4.1” by professor Fan-Tien Cheng. The mission of the Intelligent Manufacturing System based on

AMCoT developed by iMRC is to realize the visions of Industry 4.1.

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Intelligent Manufacturing System FrameworkSingle Machine

ApplicationsPro

du

ctio

n L

ine

Ap

plic

atio

ns

Service Subscription

3D PrintingSolar

Energy

AerospaceWheel Rim

TFT-LCDStretch Blow

MoldingCNC Machine

Tool

Semiconductor

...

AMCoT Platform

Manufacturing Service

Automated

Construction Scheme

(MSACS)

Big Data Cloud Platform

NoSQL DBAVR DB3D DBRDB HDFS

Co

re

Tec

hn

olo

gie

s

AVM IPM IYM AEO ROT DMT `AM

Automatic Virtual Metrology (AVM)

Intelligent Predictive Maintenance

(IPM)

Intelligent Yield Management (IYM)

Advanced Manufacturing Cloud of

Things (AMCoT)

Cyber-Physical Agent (CPA)

Manufacturing Service Automated

Construction Scheme

(MSACS)

Fan-Tien Cheng

e-Manufacturing

Research Center

Metal Powder Processing Technology

Optimization Processing Parameters for

Additive Manufacture

Optimization Metallographic/Microstructure

for Additive Manufacture

On-line Measurement Temperature and Area

of Additive Manufacture Melting Pool

On-line Measurement Cladding Height for

Additive Manufacture

Yu-Long Luo/ You-Ren Cheng

Metal 3D Additive Manufacturing Research Center

Big Data Cloud Platform

Calculation

Machine learning and

intelligent management

Nondestructive Testing and

Intelligent Internet of Things

AR/ VR Human-Machine

Interaction

Hong-Jang Hsiao/ Ming-Chi Tsai

Intelligent Big Data Integrated

Solutions Center

Process Data Sensing (PDS)

Data Driven Modeling (DMT)

Robust Optimization (ROT)

Advanced Evolution

Optimization (AEO)

Hao-Ching Yang/ Jyh-Horng Chou

Performance and Quality Robust Optimization Research

Center

CPAP CPAP CPAP CPAECPAECPAE

BA/ML AR/VR

Sy

ste

m

fram

ew

ork

DA

On Demand

6

Various Application Scenarios

7

Missions of iMRC

Our Intelligent Manufacturing Research Center is dedicated to

assist various Manufacturing Industries to realize the visions of

Industry 4.1.

Phase 1：Accomplishing the goal of having Zero Defects of

all the deliverables.

Phase 2：Accomplishing the goal of having Zero Defects of

all the products.

(Big Data Analytics & Continuous Improvement)

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Virtual Metrology (VM) is a method to conjecture manufacturing quality of a process tool based on

data sensed from the process tool and without physical metrology operation.

it 1it 2it 3it 4it jt 1jt 2jt 3jt 4jt

UCL

LCL

Process TimeVir

tua

l M

etr

olo

gy

Da

tai

y(

)^

OOC

PHASE 5

DEPT 6

DEPT 5

DEPT 4

DEPT 3

DEPT 2

DEPT 1

PHASE 4PHASE 3PHASE 2PHASE 1

DEPLOYMENT CHART

Metrology

Equipment

Virtual Metrology

System

Sampling Products

Products

Sensor Data ijx

Real -Time & On-Line

TTransportation &

Measurement Time

T

Tti Ttj it jt

UCL

LCL

Process Time

T

Rea

l M

etr

olo

gy

Da

taiy

()it

jt

Production

Equipment

IBM

VM can convert sampling inspections with metrology delay into

real-time and on-line total inspection.

Applying Virtual Metrology in Semiconductor and

TFT-LCD Industry

10

Introduction to AVM

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Data

PreprocessingProcess

Data

Data

Preprocessing

VM

Only for

Training

& Tuning

√√√√√√√√√√√

√Metrology

Data

Conjecture Model

Traditional VM Framework

Promptness and accuracy of traditional VM may not be achieved simultaneously. When promptnessis emphasized, accuracy is poor; and when accuracy is emphasized, promptness cannot be achieved.

Traditional VM values are provided without the reliance indexes (RIs) so users don’t know whetherVM values are reliable or not. This phenomenon is attributed to the so-calledapplicability/manufacturability problem of VM.

The traditional VM scheme is not able to perform on-line and real-time quality evaluation of process-and-metrology data collected. As such, abnormalities in process data or metrology data cannot beexcluded and will be added to the model tuning or re-training processes, resulting in deteriorated VMaccuracy.

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AVM Framework

Conjecture Model

RI Module

GSI

RI

Process

Data

GSI Module

VMI

VMII

Only for

Training

& Tuning

√√√√√√√√√√√

Metrology

Data√

Dual-Phase

Algorithm

Data Preprocessing

DQI Z-Scorey

DQIx Z-Score

Data Preprocessing

Promptness and accuracy can both be taken into consideration in the dual-phase algorithm. Phase

I emphasizes promptness to immediately calculate and output the Phase-I VM value (VMI); Phase II

improves accuracy to re-calculate (with the newly refreshed VM models) and output the Phase-II

VM values (VMII).

The AVM system generates the accompanying reliance index (RI) of each VMI and VMII. Users can

check the reliability of the VM prediction via its corresponding RI value.

The AVM system can ensure the quality of process data and actual metrology data on-line and real-

time, thus the quality of the outputted VM values can be further assured.

RI & GSI

Conjecture AlgorithmData Quality Evaluation

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Server-based AVM System

VM

Client

SOAP

VM

Manager

Metrology

Equipment

SOAP

VM

Server-1

Metrology

Equipment

...

...

Model

Creation

Server

Central

Database

Process

EquipmentProcess

Equipment

VM

Client

VM

Server-n

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The purpose is to integrate the functions of AVM with those of MES. The interfaces among AVM,

other MES components, and R2R (run-to-run) modules in the novel manufacturing system are

defined so that the total quality inspection system can be realized and the R2R capability can be

migrated from lot-to-lot control to wafer-to-wafer control.

Integrating AVM with

Manufacturing Execution System (MES)

Equipment i Equipment i+1

Process

Data

Material flowInformation flow

Metrology

)25 pcs

)

)1 pc

)

MES

R2R i R2R i+1

Alarm Manager

WIP Tracking

SPC Scheduler

Equipment Manager

Material Manager Reporting

)25 pcs

)

Equipment i Equipment i+1

Process

Data


Metrology

AVM

)25 pcs

)

)1 pc

)

MES

R2R i R2R i+1

Alarm Manager

WIP Tracking

SPC Scheduler

Equipment Manager

Material Manager Reporting

)25 pcs

)

VMI for FB

R2R Control

VMII for FF

R2R Control

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A key challenge preventing effective utilization of VM in R2R control is the inability to take the reliance level

in the VM feedback loop of R2R control into consideration. The reason is that adopting an unreliable VM

value may be worse than if no VM is utilized. The AVM system possesses the RI of VM to gauge the

reliability of VM results [2], [A]. Therefore, this novelty is to invent a novel scheme of R2R control that

utilizes AVM with RI/GSI in the feedback loop.

Applying AVM for W2W APC

Metrology

Tgt


Process

1ku

1

Product

zy

: No. of RunskZ: No. of Actural Measurements

Sampled product

k

R2R

0 1 ku

EWMA Filter

Metrology

Tgt


Process

AVMˆ

kykX

1ku

1

Product

Process

Data

For Training or Tuning

zy

: No. of RunskZ: No. of Actural Measurements

Sampled product

VMI or VMII

k

2 /RI GSI2 ( ,f RI

R2R

Upstream Metrology Data

0 1 ku

EWMA Filter

1)GSI

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AVM Deployment for the

Flat Panel Display Processes

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(a) TFT Process Production Line

Process

Data

Process

Data

Metrology

CDMetrology

DepthProcess

Data

Metrology

WidthProcess

Data

Process

Data

MESMaterial flowInformation flow

Film

Deposition

(Stage I) Exposure Developing Etching Stripping

Positive

Photoresist

Coating

Film

Deposition

(Stage II)

Metrology

THK (G1)

(a)

Substrate (Glass)

IN

GG1

G2

Substrate (Glass)Substrate (Glass)

G1Gate Layer

Photoresist

Substrate (Glass) Substrate (Glass)

UV

Mask


Metrology

THK (G) Process

Data

Process

Data

Metrology

CDMetrology

DepthProcess

Data

Metrology

WidthProcess

Data

Process

Data

VMI or VMII

MES

AVM

CD

AVM

THK (G1)

AVM

THK (G2)~


(G1)^

VMI )

)

VMII&

Film

Deposition

(Stage I)

Exposure Developing Etching Stripping

Positive

Photoresist

Coating

AVM

DepthAVM

Width

Film

Deposition

(Stage II)

VMI )

)

VMII&VMI )

)

VMII&VMI )

)

VMII&VMI )

)

VMII&

Metrology

THK (G1)

(b)

(a)

Substrate (Glass)

IN

GG1

G2

Substrate (Glass)Substrate (Glass)

G1Gate Layer

Photoresist


UV

Mask

Substrate (Glass)

+-

Substrate (Glass)

Metrology

THK (G)

~G2 = G - G1

^

(b) Deployment of AVM Servers

Semiconductor Layer of the TFT Process Flow

with Deployment of AVM Servers

33

Intelligent Factory Automation ( ) Alliance

Intelligent

Manufacturing

Intelligent Manufacturing Solution Strategic Alliance

The Best Choice of Industry 4.1

Suitable for All Kinds of Machine and Product Systems

iFA

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Server-based FrameworkiFA

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Cloud-based FrameworkiFA

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How Does the Cloud-based OperateiFA

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Main Features & Benefits of

Achieving ZERO DEFECTs of All Products for Industry 4.1

• Accomplishing Big-Data-Collection Infrastructure

[w/ CPA – Internet of Things and Edge computing]

• Accomplishing Cloud-Computing Infrastructure

[w/ AMCoT – Cloud Manufacturing]

• Establishing internet security and monitoring system

[w/ IoT Security Operation System – Information Security]

• Managing the utilization of the factory and monitoring the Overall Equipment

Effectiveness (OEE)

[w/ EMS – Equipment management ]

• Accomplishing Workpieces Total Quality Inspection

[w/ AVM – Total Quality Inspection for Achieving Zero Defects]

• Improving Production-Tools’ Availability

[w/ IPM – RUL Prediction Preventing Unscheduled Down]

• Real-time monitoring the tool status and provide the adequate timing for tool

replacing

[w/TLM – RUL Prediction of the tool status ]

• Constructing Root-Causes Searching Infrastructure of Yield Loss

[w/ IYM – Root-Causes Identification of Yield Loss for Continuous Improvement]

iFA

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Bottle Industry

PET Stretch-Blow Molding Machine(2018.08.15 2018 TaipeiPlas CHUM POWER)

Semiconductor Industry

Bumping Process(2018.08.01 ASE Group)

Automobile Industry

Wheel Machining(2018.05.09 2018 iMTDuo FEMCO)

iFA

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Semiconductor Industry

Bumping Process

(2018.08.01 ASE Group)

iFA

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Bumping Process

Exposure Cu Plating

Positive Photoresist

Coating

Sputtering Deposition

Stripping Developing

ProcessData

ProcessData

ProcessData

Etching

ProcessData

ProcessData THKCu

THKTi

CD

CD

THK

Delivery after Total Inspection

ReflowFlux

Clean

MetrologyExterior

Defects Check

MetrologyBall Height/

DiameterMetrologyBall Sheer

ProcessData

UV

UBM


SubstrateCutout

Dipositive Protection Layer

(PB01)In

Come Wafer

RDL

SubstrateCutout

Exposure Cu Plating


Coating



ProcessData

ProcessData

ProcessData

Etching

ProcessData

ProcessData THKCu

THKTi

CD

CD

THK

UV

Dipositive

Protection Layer

(PB02)

SubstrateCutout

Ball mount

ProcessData

RDL: Re-distribution Layer

UBM: Under Bump Metallurgy

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AMCoT Framework &Intelligent Manufacturing Deployment Procedure

CPA CPA CPA CPA CPA CPA CPA CPACPACPA CPACPA

Material flow

Information flow

Take UMP as example

Exposure Cu

Plating

Positive

Photoresist

Coating

Sputtering

Deposition Stripping Developing Etching

THKCu THKTi CD CD THK

Delivery after

Total InspectionReflow

Flux

Clean

Metrology

Exterior Defects

Check

Metrology

Ball

Height/DiameterMetrology

Ball Sheer

Ball

mount

UV Mask

Advanced Manufacturing Cloud of Things

Equipment

Prognosis

Predictive

Maintenance

Simulation

IYM

...Model

Creation

Virtual

Metrology

AVM

...

R2R

Yield

EnhancementYield

Management

IPM

Control

AlgorithmStrategy...

Service Broker

...

Cloud of Things Services ( SOAP / REST )

Big Data Analytics Apps

HDFSRDB

Manufacturing Services Automated Construction

Scheme (MSACS)

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【Cloud-based AVM】Real-time prediction of the product

processing quality

• Collect the process & metrology data from the tools via CPA

• Upload to cloud-based AVM service to predict the product quality,

and then send notification to GUI

• Users can check the product processing quality in real time

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AVM System Display Framework

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Cloud-based AVM Demo for CD of Photo Process (ASE)

52

【Cloud based IPM Service】Predict the device health status and

service life

• Collect the process data from the target device via CPA

• Upload to cloud-based IPM service to build the remaining useful life (RUL)

prediction model, and then download the RUL model to the CPA for

monitoring the device health status and predicting the RUL

• Users can check the device health status and remaining useful life (RUL) in

real time

53

SOAP

……

C&H

Creation

Server

(CCS)

IPM

Manager

IPM

Client

……

Central

DB

IPM

Server nCA

Process

Equipment n

IPM

Server 1

CPA

Device 1 Device 2 Device n

……

Process

Data

Each Device

Related

Process Data

CA

CPA

BPM

CPA

BPM

Process

Equipment 1

BPM

CPA

Device 1 Device 2 Device n

……

Process

Data

Each Device

Related

Process Data

CPA

BPM

CPA

BPMBPM

SOAPPrivate Cloud

Private Cloud

Cloud-based IPM Service (1/2)

Intelligent Predictive Maintenance (IPM) System：

Systematic management flow via 3-layered framework

Cyber-Physical Agent (CPA) is pluggable, users can select different predictive maintenance

algorithm according to needs, and establish Predictive Maintenance Algorithms Library for

implementing different types of target devices.

55

IPM System Display Framework

60

【Cloud-based IYM System】Find out the key devices and

parameters which affects the yield

Phase I: Search for the key devices which affects the yield

Phase II: Search for the key parameters which affects the yield

• Finding out the root cause which affect the yield via the 2 Phase

process effectively

• Reduce the trouble shooting time and improve the yield

63

KSA Input Data

Take Bumping Process as an Example

Exposure Cu Plating


Coating



ProcessData

ProcessData

ProcessData

Etching

ProcessData

ProcessData THKCu

THKTi

CD

CD

THK

Delivery after Total Inspection

ReflowFlux

Clean

MetrologyExterior

Defects Check

MetrologyBall Height/

DiameterMetrologyBall Sheer

ProcessData

UV

UBM


SubstrateCutout

Dipositive Protection Layer

(PB01)In

Come Wafer

RDL

SubstrateCutout

Exposure Cu Plating


Coating



ProcessData

ProcessData

ProcessData

Etching

ProcessData

ProcessData THKCu

THKTi

CD

CD

THK

UV

Dipositive

Protection Layer

(PB02)

SubstrateCutout

Ball mount

ProcessData

Final Inspetion (Y)Inline Metrology (y)

Production Route

(XR)

Process Data (XP)

WaferID:

AB0001

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IYM System Operation

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Automobile Industry

Wheel Machining

(2018.05.09 2018 iMTDuo FEMCO)

iFA

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2018 iMTDuo System Demo --Wheel Machining (FEMCO)

iFA

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Prof. Fan-Tien Cheng Presenting FEMCO’s AMCoT System to President Tsai, Ying-Wen at 2018 iMTduo (2018.5.9) (1/2)

82

System Platform DemosiFA

Bottle Industry

PET Stretch-Blow Molding

(2018.08.15 2018 TaipeiPlas CHUM POWER)

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iFA2018 TaipeiPlas System Demo --PET Stretch-Blow Molding

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Vice President Chen, Chien-Jen Visited Chum Power AMCoT at 2018 TaipeiPlas (2018.8.15)

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TAIPEI PLAS 2018 – Smart Machinery Award

2018.08.22 經濟日報

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Promotion & Business Model of

The SystemiFA

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Server-based System Cloud-based SystemiFA iFA

89

Business Model

90

Intelligent Manufacturing Cloud

Promotion and Operation

Mission Task Item Company in Charge

Marketing &

Promotion

Categorization of Manufacturing

Industries for Promotion

Sales Support

System

Planning &

Implementation

IoT platform, Internet, Data

Security, and CPA

System Integration, Monitoring

Facilities, MMI, etc.

System

Warranty &

Maintenance

Tier-1 Maintenance

(Call or On-Site Service)

Tier-2 Maintenance

Tier-3 Maintenance

iFA

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Invitation

Welcome to the joint booth of iMRC & FS-Tech at K3071

to learn more about , AVM, IPM, IYM, and FDC!

Stage

We are here

Entrance

fan-tien cheng...4 the importance of quality and the visions of industry 4.1 industry 4.0 values...

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