0
Applying AVM and Advanced Manufacturing Cloud of Things
for Industry 4.1
e-Manufacturing Research Center National Cheng Kung University
Taiwan, ROC
Foresight Technology Company, Ltd. Taiwan, ROC
March 3, 2016
鄭芳田 Fan-Tien Cheng
1
VM Definition
Key Features of the AVM System
AVM Demo
Integrating GAVM into WMA
Requirements of CPA
Advanced Manufacturing Cloud of Things (AMCoT)
Applying AMCoT to WMA
Industry 4.0 & Industry 4.1
Selected References
Contents
2
VM Definition
3
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.
Virtual Metrology
it 1+it 2+it 3+it 4+it jt 1+jt 2+jt 3+jt 4+jt
UCL
LCL
Process TimeVir
tual
Met
rolo
gy D
ata
iy()
^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
T∆Transportation &
Measurement Time
T∆
Tti ∆+ Ttj ∆+it jt
UCL
LCL
Process Time
T∆
Rea
l Met
rolo
gy D
ata
iy()it
jt
Production Equipment
IBM
VM can convert sampling inspections with metrology delay into real-time and on-line total inspection.
4
Key Features of the AVM System
5
Conjecture Model
RI Module
GSI
RI
ProcessData
GSI Module
VMI
VMII
Only forTraining& Tuning
√√√√√√√√√√√
MetrologyData
√
Dual-PhaseAlgorithm
Data Preprocessing
DQI Z-Scorey
DQIx Z-Score
Data Preprocessing
AVM Server
Data Quality Evaluation
Prediction Kernel
Reliability Indexes
6
Advanced Dual-Phase VM Algorithm Metrology Data
Collection
Collection Completed
Metrology Data vs. Process Data
Correlation Check
Correlation Successful
Re-TrainingDQI, VM & RI/GSI
Models
TuningDQI, VM & RI/GSI
Models
No
No
Yes
Update VM, RI/GSI & DQI Models
Phase II
1st Cassette after Idling
YesNo
Manual Activation or Refreshing
Yes
No
Re-compute VMII with RI/GSI of Each Workpiece
in the Entire Cassette
Start Start
Update Models
DQIy CheckBad
Good
Yes
Compute VMI with RI/GSI
for the Workpiece
No
Yes
Phase I
Process Data Collection of a Certain
Workpiece
Collection Completed
DQIX CheckBad
Good Send Warning & Ask for Analysis
Disable RefreshingIf VM Accuracy Is Gained
Send Warning & Ask for Analysis
Correlative Process Data is Good
No
Yes
The DQIX and DQIy models are added into the algorithm to perform automatic data quality evaluation.
A model refreshing mechanism is added into the algorithm for automatic model-refreshing control.
When VM accuracy is gained, the refreshing procedure will stop automatically and the system will enter the normal operation state. Taiwan Patent No.: I349867;
US Patent No.: 8,095,484 B2; Korea Patent No.: 10-1098037; Japan Patent No.: 4914457; China Patent No.: 843932
7
Automatic Virtual Metrology (AVM) System
VMClient
SOAP
VMManager
MetrologyEquipment
SOAP
VM Server-1
MetrologyEquipment
...
...
Model Creation Server
CentralDatabase
ProcessEquipment
ProcessEquipment
VMClient
VM Server-n
8
(a) TFT Process Production Line
ProcessData
ProcessData
MetrologyCD
MetrologyDepthProcess
Data
MetrologyWidthProcess
DataProcess
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
GG1G2
Substrate (Glass)Substrate (Glass)
G1Gate Layer
Photoresist
Substrate (Glass) Substrate (Glass)
UV
Mask
Substrate (Glass) Substrate (Glass)
MetrologyTHK (G) Process
DataProcess
DataMetrology
CDMetrology
DepthProcessData
MetrologyWidthProcess
DataProcess
Data
VMI or VMII
MES
AVMCD
AVMTHK (G1)
AVMTHK (G2)
~
Material flowInformation flow
(G1)^
VMI )) VMII&
Film Deposition
(Stage I) Exposure Developing Etching Stripping
Positive Photoresist
Coating
AVMDepth
AVMWidth
Film Deposition (Stage II)
VMI )) VMII&VMI )) VMII&VMI )) VMII&VMI )) VMII&
Metrology THK (G1)
(b)
(a)
Substrate (Glass)
IN
GG1G2
Substrate (Glass)Substrate (Glass)
G1Gate Layer
Photoresist
Substrate (Glass) Substrate (Glass)
UV Mask
Substrate (Glass)
+-
Substrate (Glass)
MetrologyTHK (G)
~G2 = G - G1^
(b) Deployment of AVM Servers
Semiconductor Layer of the TFT Process Flow with Deployment of AVM Servers
9
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 into MES
Equipment i Equipment i+1
ProcessData
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
ProcessData
Material flowInformation flow
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
10
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.
APC Utilizing AVM with RI/GSI
Metrology
Tgt
Material flowInformation flow
Process
1ku +
1α
Product
zy
: No. of RunskZ: No. of Actural Measurements
Sampled product
ηk
R2R
0 1 kuβ β+
EWMA Filter
Metrology
Tgt
Material flowInformation flow
Process
AVM ˆkykX
1ku +
1α
Product
ProcessData
For Training or Tuning
zy
: No. of RunskZ: No. of Actural Measurements
Sampled product
VMI or VMII
ηk
2α /RI GSI2 ( ,f RIα =
R2R
Upstream Metrology Data
0 1 kuβ β+
EWMA Filter
1)GSI α×
11
AVM Demo
12
Live Demo of the AVM System for CNC Precision Machining
Video showing the precision machining on cellphone backplanes.
(At the 2012 Taiwan International Machine Tool Exhibition)
GUI displaying real-time and online VM values of straightness 2.
(The CNC tool was located in a machine tool factory in Taiwan)
(The GUI was shown at the Exhibition Hall in Taipei, Taiwan)
The engineer proceeds to press the “Start Machining” button
The CNC machine is performing precision machining on the 25th cellphone backplane
The total duration of the precision machining is about 10 seconds
After the precision machining is completed, the GUI will display the predicted machining-precision value of the 25th cellphone backplane within 10 seconds
VM value of the 25th sample is displayed within 10 sec after
processing.
13
420408396384372360
348
Curr
ent
1
336
(b)
1
Dual Phase
Free Run
(a)
GSI
79
79PM3PM1 PM2
GSI = 9.55
50
GSI = 36.44GSI = 11.11
GSI
ESC Leakage Current
ESC Leakage CurrentLCL=130, UCL = 330
LCL=130, UCL = 330
VM (M
ean)
Curr
ent
Curr
ent
I I
RI
RIT
GSIT
RIT
GSIT
RI
420408396384372360
348336
VM (M
ean) The VM conjecture models will be tuned/re-
trained by the dual-phase scheme run-to-run.
The VM conjecture models will not be refreshed.
Smooth manufacturing process
Superior VM accuracy (Sampling rate can be reduced)
Demo of the RI, GSI, & Dual-Phase Schemes (1/2)
14
420408396384372360
348
Curre
nt
1
336
(b)
1
Dual Phase
Free Run
(a)
GSI
79
79PM3PM1 PM2
GSI = 9.55
50
GSI = 36.44GSI = 11.11
GSI
ESC Leakage Current
ESC Leakage CurrentLCL=130, UCL = 330
LCL=130, UCL = 330
VM (M
ean)
Cur
rent
Cur
rent
I I
RI RIT
GSIT
RIT
GSIT
RI
420408396384372360
348336
VM (M
ean)
GSI values are higher than GSIT
VM accuracy can be recovered and maintained
PM3PM1
RI values are less than RIT
VM accuracy becomes poor.
The VM conjecture models will be tuned/re-trained by the dual-phase scheme run-to-run.
The VM conjecture models will not be refreshed.
Demo of the RI, GSI, & Dual-Phase Schemes (2/2)
15
Sampling Rates 2/25 vs. 1/50
Sampling Rate 2/25
Sampling Rate 1/50
16
Integrating GAVM into WMA
17
The Wheel Machining Automation (WMA) cell layout
• two lathes (Lathe 1 and
Lathe 2) • one drilling machine
(Drill) • one off-machine
metrology (OMM) tool for sampling inspection of all precision items
• one robot is placed in the middle of the cell for material handling and two buffers are installed for input/output purposes.
Integrating GAVM into Wheel Machining Automation for Total Inspection (1/4)
Metrology Tool
OutIn
Lathe 1
Drill
Lathe 2
AVM Server
GED
Sampling Inspection (e.g. 1 out of 20) Total Inspection
ReaderLaser
Marker
18
Conjecture Model
RI Module
GSI
RI
GSI Module
VMI
VMII
Only forTraining& Tuning
AdvancedDual-Phase
VM Algorithm
Data Preprocessing
DQI Z-Scorey
DQIx Z-Score
Data Preprocessing
AVM ServerGED
Comm
unicationAgent
ApplicationInterface
PluggableApplication
Module
(PAM)
EquipmentDriver
DataCollectionManager
DCPlan
DCReport
InterfaceB
ox STDB
Process Data
Metrology Data
Machine T
oolM
etrology Tool
Sensor Data
Machining Parameter
Metrology D
ata
GAVM System for Machine Tools.
Integrating GAVM into Wheel Machining Automation for Total Inspection (2/4)
19
• Traditional Wheel Machining Inspection: wheel quality can only be checked by one in-line metrology (ILM) equipment for total inspection of primary precision items, and one off-machine metrology (OMM) tool for sampling inspection of secondary precision items.
• GED-plus-AVM (GAVM) System: The integration system of generic embedded device (GED) and AVM play
an inspector role of achieving nearly ZD of automated total inspection for the machine-tool industry.
Sufficient process data for the GAVM system are not only collected from sensors, but also can be: segmented to extract the essential parts from the original numerical-
control (NC) file for synchronizing various machining quality/metrology data
filtered for improving signal-to-noise (S/N) ratios transformed into essential features for building VM models, and the
corresponding solutions of those three challenges were implemented in GED
Integrating GAVM into Wheel Machining Automation for Total Inspection (3/4)
20
Integrate GAVM into WMA: a Miniature of Industry 4.0 • GED is the prototype of an IoT agent • AVM is an example of CPS To Do: Enhance the functions of GED, and then adopt Advanced Manufacturing Cloud of Things (AMCoT) for wheel production industries based on the miniature GAVM system.
Integrating GAVM into Wheel Machining Automation for Total Inspection (4/4)
21
Requirements of CPA
22
GED: focus on data collection and implementation of pluggable applications
• To reach the goal of Industry 4.0, the functions of 3C, including computation, communication, and control, should be implemented into GED so as to become a CPA for achieving “man-machine collaboration”
Differences between CPA and GED: 1) control of interaction with physical objects and cyber systems 2) the communication among the cyber systems (e.g. AMCoT), the physical
objects (e.g. machine tools), and human operators
Requirements of CPA (1/2)
23
Conjecture Model
RI Module
GSI
RI
GSI Module
VMI
VMII
Only forTraining& Tuning
AdvancedDual-Phase
VM Algorithm
Data Preprocessing
DQI Z-Scorey
DQIx Z-Score
Data Preprocessing
AVM ServerGED
Comm
unicationAgent
ApplicationInterface
PluggableApplication
Module
(PAM)
EquipmentDriver
DataCollectionManager
DCPlan
DCReport
InterfaceB
ox STDB
Process Data
Metrology Data
Machine T
oolM
etrology Tool
Sensor Data
Machining Parameter
Metrology D
ata
GAVM system for machine tools.
Requirements of CPA (2/2)
Turns GED to CPA
24
Cyber-Physical Agent (CPA)
CPA consists of 1) CPA Control Kernel 2) Communication Service 3) Data Collection Manager 4) Data-Collection Plan (DCPlan) 5) Data-Collection Report
(DCReport) 6) Equipment Driver 7) Application Interface 8) Database
CPA Architecture
CPA Control Kernel
Data Collection Manager
Equipment DriverApplication Interface
Pluggable Application
Module
Pluggable Application
Module
...
Communication SerivceREST SOAP
DCR
DCPDatabaseController
PageMaker
Command Handler...
Database
GPIO Driver
ZigBee/WSNDriver
(USB/COM)
Ipv4 Driver
(Wi-Fi/Ethernet)
IPv6/WSNDriver
(6LoWPN)
Taiwan Patent : I225606 US Patent:7,162,394
Japan Patent:特許4303640號
25
Advanced Manufacturing Cloud of Things (AMCoT)
26
Advanced Manufacturing Cloud of Things AMCoT provides a cloud-based platform for connecting and sharing all
information of things for bridging the technology supports among vender, customers, and manufacturing tools.
AMCoT
Customer 2
Customer 1
Customer 3
Customer 4
Vender
One-to-Many Relationship among a Vender and its Customers via AMCoT
Application Example -- AVM Model-refreshing: Each customer does not need to build its own AVM models but just downloads all the preliminary AVM models (provided by the vender) from AMCoT.
27
Applying AMCoT to WMA
28
…
AMCoTSTDB/CDB
AVM System
Tool Life Management
Service
Collision Detection Service
Metrology Planning Service
IntelligentPredictive
Maintenance
CPA CPA1Tool RUL
Vender Customer 1CPA2Cell 1 Cell 2
Customer 2
Customer 3
Customer 4
Applying AMCoT to WMA
Integrating WMA’s Vender and Customers into AMCoT
29
UCL
LCL
Vender
Sample No.
(mm
)
SC2
Cell 1
SC3SC1
DQIy Error
The first wheel in Cell 1
GSI
Mai
n M
otor
C
urre
nt: R
MS
(A)
30
20
010
GSI
Global Cyber-Physical Interactions (AVM Models Refreshing). LCL: lower control limit (72.93 mm); UCL: upper control limit (73.15 mm).
Illustrative Example−Experiment
30
Industry 4.0 & Industry 4.1
31
Industry 4.0
Industry 4.0 = IoT + CPS + Cloud Manufacturing (CM) + Big Data Analytics.
Industry 4.0 only keeps the faith of achieving nearly Zero Defects (ZD) state.
Zero Defects (ZD) • ZD has been one of the quality-improvement objectives for
accomplishing manufacturing quality [A]. • Through prevention methods, ZD aims to boost production and
minimize waste. • ZD is based on the concept that the amount of mistakes a worker
makes doesn't matter since inspectors will catch them before they reach the customer [A].
[A] Halpin, James F. Zero Defects: A New Dimension in Quality Assurance, New York: McGraw-Hill, 1966.
32
Industry 4.1
Industry 4.0 + AVM = Industry 4.1
→ To achieve the goal of Zero Defects (ZD) • Stage I:Achieving the goal of ZD for all the Deliverables • Stage II:Achieving the goal of nearly ZD for all the Products by Continuous Improvements
33
Process Flow of TFT-LCD Manufacturing
The TFT-LCD manufacturing flow consists of four processes: TFT, CF (color filter), LCD, and LCM (liquid crystal module).
TFT substrate
CF substrate
34
TFT-LCD Front-End Process TFT Process
CF Process LCD Process
Chromaticity
THKTHKTHKTHK
Chromaticity
PS LayerG LayerR LayerBM Layer ITO LayerB Layer OC Layer FinalInspection
Mura
Totalpitch
Chromaticity
ProcessData
ProcessData
ProcessData
ProcessData
ProcessData
ProcessData
ProcessData
BM line width
OD
Mura
BMTHK
Totalpitch
MarkPOS
THK
Chromaticity
MuraMarkPOS
Pixel Dimension
THK
Substrate (Glass)
GSubstrate (Glass)
BM BM BM BM
Substrate (Glass)
R Substrate (Glass)
BSubstrate (Glass)
PS
Substrate (Glass)
ITO
Unifornity SheetResistance
PS Height
PS CD
SheetResistance
TEGParticle
PatternInspection
Pin Hole
Particle
THK
Width
SheetResistance
Pin Hole
Particle
THK
Width
SheetResistance
Pin Hole
Particle
THK
Width
SheetResistance
Gate Metal (Stage I)
AS (Stage II)
Particle
GETHK GE
Width
Sheet Resistance Pin Hole
Particle
THK
Width
SheetResistance
SD (Stage III)
BP (Stage IV)
ITO (Stage V)
FinalInspection
ProcessData
ProcessData
ProcessData
ProcessData
ProcessData
Substrate (Glass)
Gate Metal Source/Drain Metal
Substrate (Glass) Substrate (Glass)
SiNx
Substrate (Glass)
ITOSiN A-Si N+ A-Si
Substrate (Glass)
THK
PretilyAngle
THKProcessData
ProcessData
Scribe Location
ProcessData
Polarizer Attach
ProcessData
Assemble
Polarizer Location
Scribe
Polyimide Print ODF
Substrate (CF)
Substrate (TFT)
Seal Dispense
ProcessData
Substrate (TFT) Substrate (TFT)
Substrate (CF)
Substrate (TFT)
FinalInspection
Cell Gap
Luminance
ContrastRate
RubbingSystem
ProcessData
THKProcessData
Polyimide Print
Seal Dispense
ProcessData
RubbingSystem
ProcessData
ProcessData
Substrate (TFT)
Substrate (CF) Substrate (CF)
HOT PressSubstrate (TFT)
Substrate (CF)
Substrate (CF)
Substrate (TFT)
PretilyAngle
THK
ProcessData
35
Selected References
36
Selected References Journal Papers F.-T. Cheng, C.-A. Kao, C.-F. Chen, and W.-H. Tsai, “Tutorial on Applying the VM Technology for TFT-LCD
Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, vol. 28, no. 1, pp. 55-69, February 2015. F.-T. Cheng, C.-F. Chen, Y.-S. Hsieh, H.-H. Huang, and C.-C. Wu “Intelligent Sampling Decision Scheme Based on the
AVM System,” International Journal of Production Research, vol. 53, no. 7, pp. 2073–2088, 2015. H.-C. Yang, H. Tieng, and F.-T. Cheng, “Automatic Virtual Metrology for Wheel Machining Automation,” International
Journal of Production Research. DOI: 10.1080/00207543.2015.1109724, published online: Nov 2015. F.-T. Cheng, H. Tieng, H.-C. Yang, M.-H. Hung, Y.-C. Lin, C.-F. Wei, and Z.-Y. Shieh, “Industry 4.1 for Wheel Machining
Automation,” IEEE Robotics and Automation Letters, vol. 1, no.1, pp. 332-339, January 2016.
Patents Fan-Tien Cheng, Hsien-Cheng Huang, and Chi-An Kao, “Dual-Phase Virtual Metrology Method,” U.S. Patent no.:
7,603,328 B2, Taiwan R.O.C. Patent no.: I338916, Japan Patent no.: 4584295, China Patent no.: 823284, and Korea Patent no.: 10-0915339.
{行政院2011年傑出科技貢獻獎} {2011經濟部國家發明創作獎銀牌}
Fan-Tien Cheng, Hsien-Cheng Huang, Yi-Ting Huang, and Jia-Mau Jian, “System and Method for Automatic Virtual Metrology,” U.S. Patent no.: 8,095,484 B2, Taiwan R.O.C. Patent no.: I349867, Japan Patent no.: 4914457, China Patent no.: 843932, and Korea Patent no.: 10-1098037. {2012經濟部國家發明創作獎 發明獎金牌} {2013 IEEE Inaba Technical Award for Innovation Leading to Production}
Haw-Ching Yang, Hao Tieng, Min-Hsiung Hung, and Fan-Tien Cheng, “Method for Predicting Machine Quality of Machine Tool,” Taiwan R.O.C. Patent no.: I481978; with U.S. and China Patents Pending under applications 14/069,382 and 201310593639.0, respectively.
Fan-Tien Cheng, Chun-Fan Chen, Yao-Sheng Hsieh, Hsuan-Heng Huang, and Chu-Chieh Wu, “Metrology Sampling Method and Computer Program Product Thereof,” Taiwan R.O.C. Patent no.: I521360; U.S. and China Patents Pending under applications 14/666,324 and 201410196529.5, respectively.
37
Companies or Organizations that have technology transferred and/or deployed AVM related Patents or Technologies
Semiconductor Industry: TSMC (台積電), UMC (聯電), ASE (日月光)
TFT-LCD Industry: Innolux (群創), AUO (友達), CPT (華映)
Photovoltaic Industry: Motech (茂迪)
Machine Tool Industry: FEMCO (遠東機械) /FATEK (發得科技)
Aerospace Industry: AIDC (漢翔)
Organizations: ITRI (工研院) (Machine Tool Technology Center工具機科技中心 & Big Data Technology Center巨資中心) , MIRDC (金工中心)
38
Thank you for your listening !
Q&A
39
Foresight Technology
Tool & Info Automation • PC/PLC
Control • SECS/GEM
Capability
Data Collection & Storage • MES • Tool Process
Log & Parser
Data Visualization & Control • Tool Real-Time
Monitor • Data Viewer
Data Featuring • RTM FDC:
Retrieve Real-Time Data to Physical Index
• eRunCard: Retrieve Process/ Mfg Information
Data Analysis • YMS/EDA:
Engineering Data Analysis
• ePK: Key Parameter Analysis
Big Data Application • Control: Run-
to-Run Control • Prediction:
AVM & IPM • Tool Matching • Tool Health
機台與資訊 自動化
資料搜集 與儲存
資料視覺化與監控
資料特徵萃取
資料分析
Big Data應用
IoT Big Data
• Website: http://www.fs-technology.com/ • Phone: (06)236-6981 • Mobile: +886-977-158-326 • e-Mail: [email protected]
Industry 4.1