Cover
Series Page
Title Page
Copyright Page
Editor Biography
List of Contributors
Preface
1. Overview and Goals
2. Organization and Features
Acknowledgments
Foreword
1 Evolution of Automation and Development Strategy of Intelligent Manufacturing with Zero Defects
1. 1.1 Introduction
2. 1.2 Evolution of Automation
3. 1.3 Development Strategy of Intelligent Manufacturing with Zero Defects
4. 1.4 Conclusion
5. Appendix 1.A ‐ Abbreviation List
6. References
2 Data Acquisition and Preprocessing
1. 2.1 Introduction
2. 2.2 Data Acquisition
3. 2.3 Data Preprocessing
4. 2.4 Case Studies
5. 2.5 Conclusion
6. Appendix 2.A ‐ Abbreviation List
7. Appendix 2.B ‐ List of Symbols in Equations
8. References
3 Communication Standards
1. 3.1 Introduction
2. 3.2 Communication Standards of the Semiconductor Equipment
3. 3.3 Communication Standards of the Industrial Devices and Systems
4. 3.4 Conclusion
5. Appendix 3.A ‐ Abbreviation List
6. References
4 Cloud Computing, Internet of Things (IoT), Edge Computing, and Big Data Infrastructure
1. 4.1 Introduction
2. 4.2 Cloud Computing
3. 4.3 IoT and Edge Computing
4. 4.4 Big Data Infrastructure
5. 4.5 Conclusion
6. Appendix 4.A ‐ Abbreviation List
7. Appendix 4.B ‐ Abbreviation List
8. References
5 Docker and Kubernetes
1. 5.1 Introduction
2. 5.2 Fundamentals of Docker
3. 5.3 Fundamentals of Kubernetes
4. 5.4 Conclusion
5. Appendix 5.A ‐ Abbreviation List
6. References
6 Intelligent Factory Automation (iFA) System Platform
1. 6.1 Introduction
2. 6.2 Architecture Design of the Advanced Manufacturing Cloud of Things (AMCoT) Framework
3. 6.3 Brief Description of the Automatic Virtual Metrology (AVM) Server
4. 6.4 Brief Description of the Baseline Predictive Maintenance (BPM) Scheme in the Intelligent Prediction Maintenance (IPM) Server
5. 6.5 Brief Description of the Key‐variable Search Algorithm (KSA) Scheme in the Intelligent Yield Management (IYM) Server
6. 6.6 The iFA System Platform
7. 6.7 Conclusion
8. Appendix 6.A ‐ Abbreviation List
9. Appendix 6.B ‐ List of Symbols
10. References
7 Advanced Manufacturing Cloud of Things (AMCoT) Framework
1. 7.1 Introduction
2. 7.2 Key Components of AMCoT Framework
3. 7.3 Framework Design of Cyber‐Physical Agent (CPA)
4. 7.4 Rapid Construction Scheme of CPAs (RCS_CPA) Based on Docker and Kubernetes
5. 7.5 Big Data Analytics Application Platform
6. 7.6 Manufacturing Services Automated Construction Scheme (MSACS)
7. 7.7 Containerized MSACS (MSACS_C)
8. 7.8 Conclusion
9. Appendix 7.A ‐ Abbreviation List
10. Appendix 7.B ‐ Patents (AMCoT + CPA)
11. References
8 Automatic Virtual Metrology (AVM)
1. 8.1 Introduction
2. 8.2 Evolution of VM and Invention of AVM
3. 8.3 Integrating AVM Functions into the Manufacturing Execution System (MES)
4. 8.4 Applying AVM for Workpiece‐to‐Workpiece (W2W) Control
5. 8.5 AVM System Deployment
6. 8.6 Conclusion
7. Appendix 8.A – Abbreviation List
8. Appendix 8.B – List of Symbols in Equations
9. Appendix 8.C – Patents (AVM)
10. References
9 Intelligent Predictive Maintenance (IPM)
1. 9.1 Introduction
2. 9.2 BPM
3. 9.3 Time‐Series‐Prediction (TSP) Algorithm for Calculating RUL
4. 9.4 Factory‐Wide IPM Management Framework
5. 9.5 IPM System Implementation Architecture
6. 9.6 IPM System Deployment
7. 9.7 Conclusion
8. Appendix 9.A ‐ Abbreviation List
9. Appendix 9.B – List of Symbols in Equations
10. Appendix 9.C – Patents (IPM)
11. References
10 Intelligent Yield Management (IYM)
1. 10.1 Introduction
2. 10.2 KSA Scheme
3. 10.3 IYM System Deployment
4. 10.4 Conclusion
5. Appendix 10.A ‐ Abbreviation List
6. Appendix 10.B ‐ List of Symbols in Equations
7. Appendix 10.C ‐ Patents (IYM)
8. References
11 Application Cases of Intelligent Manufacturing
1. 11.1 Introduction
2. 11.2 Application Case I: Thin Film Transistor Liquid Crystal Display (TFT‐LCD) Industry
3. 11.3 Application Case II: Solar Cell Industry
4. 11.4 Application Case III: Semiconductor Industry
5. 11.5 Application Case IV: Automotive Industry
6. 11.6 Application Case V: Aerospace Industry
7. 11.7 Application Case VI: Chemical Industry
8. 11.8 Application Case VII: Bottle Industry
9. Appendix 11.A ‐ Abbreviation List
10. Appendix 11.B ‐ List of Symbols in Equations
11. References
Index
End User License Agreement

List of Tables

Chapter 2
1. Table 2.1 Sensor comparison.
2. Table 2.2 Illustrative example of applying M codes to segment the “X44Y50” ...
3. Table 2.3 Definition of time‐domain SFs.
4. Table 2.4 Tool diagnosis example: results of using an RF model.
Chapter 3
1. Table 3.1 Handshake codes (bytes).
2. Table 3.2 Header composition.
3. Table 3.3 Timeout parameters (in seconds).
4. Table 3.4 Streams.
5. Table 3.5 Item header.
6. Table 3.6 Item format codes.
7. Table 3.7 SECS notations.
8. Table 3.8 HSMS timeouts.
9. Table 3.9 HSMS‐SS configuration.
10. Table 3.10 Comparison of HSMS and SECS‐I.
11. Table 3.11 Required parameter list of Coater.
Chapter 5
1. Table 5.1 Some distinctions between Docker containers and virtual machines.
Chapter 7
1. Table 7.1 Time analysis of deploying two PAM_C’s into a CPA_C.
2. Table 7.2 Query times using four types of data format in experiments 1 and ...
3. Table 7.3 Comparisons of execution times in processing different sizes of d...
4. Table 7.4 Annotated keywords‐SI tags lookup table.
5. Table 7.5 Comparisons of development times of Novice, Skilled Professional,...
Chapter 8
1. Table 8.1 Simulation‐parameter definitions and setting values.
2. Table 8.2 Cpk values of 5‐cases APC methods (α₁ = 0.35).
3. Table 8.3 MAPE_P values of 5‐cases APC methods (α₁ = 0.35, Unit: %)....
4. Table 8.4 Simulation parameters and setting values (Normal and Weibull dist...
5. Table 8.5 MAPE_P values of 5‐Cases APC methods for A. Normal with RI; B. Wei...
Chapter 9
1. Table 9.1 Scenario of HIH. (a) Before repair of throttle valve. (b) After r...
Chapter 11
1. Table 11.1 Total number of devices in the TFT process.
2. Table 11.2 Cpk and MAPE_P values of Cases 1 and 2.
3. Table 11.3 ACF and PACF.
4. Table 11.4 Significance of predictors.
5. Table 11.5 ACF and PACF.
6. Table 11.6 Significance of predictors.
7. Table 11.7 WIP tracking of Spin‐1 sizing percentage with the PDT mechanism.

List of Illustrations

Chapter 1
1. Figure 1.1 ISMT e‐Manufacturing hierarchy.
2. Figure 1.2 Four key components for the advanced e‐Manufacturing model.
3. Figure 1.3 MES operation procedures.
4. Figure 1.4 Functional architecture of the ISMT CIM framework.
5. Figure 1.5 The HMES framework.
6. Figure 1.6 ESCM architecture and key processes.
7. Figure 1.7 Functional‐block diagram of the holonic supply‐chain system.
8. Figure 1.8 The ISMT EES framework.
9. Figure 1.9 The proposed EES framework.
10. Figure 1.10 Comparison of SC and EC.
11. Figure 1.11 Engineering‐chain‐management system framework.
12. Figure 1.12 Changing curves of yield and cost during the product life cycle....
13. Figure 1.13 Five‐stage strategy for increasing yield in RD/ramp‐up and MP ph...
14. Figure 1.14 Production line of the bumping process.
Chapter 2
1. Figure 2.1 Fundamental steps for developing an intelligent application.
2. Figure 2.2 An external data acquisition system for acquiring process and met...
3. Figure 2.3 Relative motion between a cutting tool and a workpiece.
4. Figure 2.4 Installation of a Dynamometer.
5. Figure 2.5 Installation of a CT.
6. Figure 2.6 Installation of accelerometer by stud mounting.
7. Figure 2.7 Vibration data collection of Z‐axis.
8. Figure 2.8 Installation of thermal couple.
9. Figure 2.9 Distance between a thermal couple and spindle.
10. Figure 2.10 Installation of an AE sensor.
11. Figure 2.11 Sensor fusion system comprising an accelerometer and a thermal c...
12. Figure 2.12 Sensor fusion system using five types of sensors.
13. Figure 2.13 An external data acquisition system triggered by electronic rela...
14. Figure 2.14 An DC signal: (a) in time‐domain; (b) in frequency‐domain; (c) i...
15. Figure 2.15 A random signal: (a) in time‐domain; (b) in frequency‐domain; (c...
16. Figure 2.16 Three‐level decomposition tree of the DWT.
17. Figure 2.17 View of the time and frequency domains.
18. Figure 2.18 A vibration signal: (a) in time‐domain; and (b) in FFT spectrum....
19. Figure 2.19 Unchanged resolution of STFT time‐frequency plane.
20. Figure 2.20 Dynamic window of WPT time‐frequency plane.
21. Figure 2.21 WPT decomposition binary tree.
22. Figure 2.22 Architecture of the AEN.
23. Figure 2.23 Using a smart tool holder to detect tool state.
24. Figure 2.24 Detrending of the thermal effect in strain‐gauge data: (a) befor...
25. Figure 2.25 De‐noising signals to highlight differences between dry‐run and ...
26. Figure 2.26 Collected vibration signals (including idling and machining peri...
27. Figure 2.27 Comparison of the original and decoded features under four idlin...
28. Figure 2.28 Automated segmentation of machining signals using an AEN: (a) di...
29. Figure 2.29 Comparison of time‐domain signals (upper portion), WPT features ...
30. Figure 2.30 WPT distribution results for different cutting depths in the X a...
31. Figure 2.31 Comparison of four SFs extracted by using an AEN for samples of ...
32. Figure 2.32 A forging load (pressure)‐stroke curve.
33. Figure 2.33 Failure diagnosis in a forming process.
34. Figure 2.34 Sample validation using the single dimension feature of the midd...
35. Figure 2.35 AEN‐DNN architecture for failure diagnosis.
Chapter 3
1. Figure 3.1 SECS block.
2. Figure 3.2 Block transfer.
3. Figure 3.3 Multi‐block message (677 data bytes).
4. Figure 3.4 Illustration of system bytes.
5. Figure 3.5 Block transfer protocol in a multi‐block message.
6. Figure 3.6 T1, T2, and T3 timeouts.
7. Figure 3.7 Example of T2 timeout and retry limit.
8. Figure 3.8 Equipment is Master and Host is Slave.
9. Figure 3.9 Primary and secondary messages.
10. Figure 3.10 SECS‐II message detail of host sending S1F3 to equipment.
11. Figure 3.11 SECS‐II message detail of Equipment sending S1F4 to Host.
12. Figure 3.12 Message detail style.
13. Figure 3.13 Message structure receiving S1F3.
14. Figure 3.14 Message structure sending S1F4.
15. Figure 3.15 Scopes of GEM, SECS‐II, and other communications alternatives.
16. Figure 3.16 Subsidiary standards of HSMS.
17. Figure 3.17 SECS‐I RS‐232 connections versus HSMS TCP/IP Ethernet connection...
18. Figure 3.18 HSMS‐SS message format.
19. Figure 3.19 Connect.
20. Figure 3.20 Data.
21. Figure 3.21 Disconnect.
22. Figure 3.22 Linktest.
23. Figure 3.23 HSMS can share network with other TCP/IP protocols.
24. Figure 3.24 SECS‐I and HSMS‐SS protocol stacks.
25. Figure 3.25 Interface A Standards on equipment.
26. Figure 3.26 Interface A Integrated Scenario.
27. Figure 3.27 E132 Authentication model.
28. Figure 3.28 CEM example – diagram of a conveyor system.
29. Figure 3.29 CEM example – photo of a conveyor system.
30. Figure 3.30 CEM example‐conveyor CEM description diagram.
31. Figure 3.31 E125 Equipment metadata.
32. Figure 3.32 Example of CEM and EqSD – conveyor system.
33. Figure 3.33 E164 in CEM and EqSD.
34. Figure 3.34 Overview of data collection.
35. Figure 3.35 Data collection manager and consumer interfaces.
36. Figure 3.36 Definition of data collection report.
37. Figure 3.37 Definition and example of event report.
38. Figure 3.38 Definition and example of exception report.
39. Figure 3.39 Definition of trace report.
40. Figure 3.40 Example of trace data collection.
41. Figure 3.41 Example of DCR buffering.
42. Figure 3.42 Automation pyramid.
43. Figure 3.43 Illustrative use case of OPC clients and servers.
44. Figure 3.44 Foundations of OPC‐UA
45. Figure 3.45 OPU‐UA architecture.
46. Figure 3.46 Architecture overview of an OPC‐UA client and an OPC‐UA server....
47. Figure 3.47 OPC‐UA security model.
48. Figure 3.48 Sequence diagram of operating procedure between a pair of OPC‐UA...
49. Figure 3.49 Intelligent Manufacturing architecture in the FCCL industry.
50. Figure 3.50 Monitoring and control system architecture of Coater by applying...
51. Figure 3.51 Use cases of data manipulation.
52. Figure 3.52 Sequence diagram of Data Collection.
53. Figure 3.53 Sequence diagram of recipe download.
Chapter 4
1. Figure 4.1 Architecture of virtual machines on top of hypervisor and physica...
2. Figure 4.2 Comparison of cloud computing service models.
3. Figure 4.3 Architecture of the three cloud deployment models.
4. Figure 4.4 Traditional IT utilization of enterprises and factories using on‐...
5. Figure 4.5 Manufacturing company or factory utilizing cloud computing to ext...
6. Figure 4.6 Simplified generic system architecture of cloud manufacturing (CM...
7. Figure 4.7 Architecture and operational flow of the cloud‐based AVM system....
8. Figure 4.8 Generic three‐layer cloud‐based IoT architecture.
9. Figure 4.9 MQTT Publish/Subscribe architecture for the communication of IoT....
10. Figure 4.10 AMQP Publish/Subscribe architecture for the communication of IoT...
11. Figure 4.11 Cloud‐based IoT architecture with an edge computing layer.
12. Figure 4.12 Application of IoT and edge computing in wheel machining.
13. Figure 4.13 Software stack for a HDS server.
14. Figure 4.14 Programming in DRS.
Chapter 5
1. Figure 5.1 Comparison of virtual machines and Docker containers.
2. Figure 5.2 Illustration of Docker containers running on virtual machines.
3. Figure 5.3 Constituent components of Docker Engine.
4. Figure 5.4 A high‐level view of Docker architecture with some of its workflo...
5. Figure 5.5 Architecture of a Linux Docker host.
6. Figure 5.6 Architecture of a Windows Docker host.
7. Figure 5.7 Architecture of Windows Server Containers.
8. Figure 5.8 Architecture of Hyper‐V Containers.
9. Figure 5.9 Anatomy of a Docker container image.
10. Figure 5.10 An example of Dockerfile.
11. Figure 5.11 The process of building an image.
12. Figure 5.12 The history of the pythonapp:latest image.
13. Figure 5.13 The sizes of two Linux container images.
14. Figure 5.14 A shorthand Dockerfile for building a Linux container image.
15. Figure 5.15 The stacked layers of the Linux container image built by the Doc...
16. Figure 5.16 A shorthand Dockerfile for building a Windows container image.
17. Figure 5.17 The stacked layers of the Windows container image built by the D...
18. Figure 5.18 Illustration of many containers sharing the same image layers.
19. Figure 5.19 Architecture of the container network model (CNM) for Linux cont...
20. Figure 5.20 Architecture of bridge networking.
21. Figure 5.21 Architecture of host networking.
22. Figure 5.22 Architecture of none networking.
23. Figure 5.23 Architecture of overlay networking.
24. Figure 5.24 Architecture of CNM for Windows containers.
25. Figure 5.25 Workflow of building, shipping, and deploying a containerized ap...
26. Figure 5.26 An example Dockerfile for building a Linux web application image...
27. Figure 5.27 Building steps of the Linux web application image.
28. Figure 5.28 Execution results of the docker tag and docker push commands.
29. Figure 5.29 Screenshot showing that the “imrc/example‐linux” image has been ...
30. Figure 5.30 Execution result of the docker pull command.
31. Figure 5.31 Execution result of the docker run command.
32. Figure 5.32 Screenshot displaying the home page of the running containerized...
33. Figure 5.33 An example Dockerfile for building a Windows web application ima...
34. Figure 5.34 Building steps of the Windows web application image.
35. Figure 5.35 Execution result of the docker push command.
36. Figure 5.36 Screenshot showing that the “imrc/example‐windows” image has bee...
37. Figure 5.37 Docker pull command's execution result on a Windows Docker host....
38. Figure 5.38 Execution result of the docker run command.
39. Figure 5.39 Screenshot displaying the home page of the running containerized...
40. Figure 5.40 Architecture of Kubernetes.
41. Figure 5.41 Creation Process of a Pod.
42. Figure 5.42 System architecture with three Control Plane Nodes.
43. Figure 5.43 System architecture with the stacked etcd topology.
44. Figure 5.44 System architecture with the external etcd topology.
45. Figure 5.45 System architecture without ingress.
46. Figure 5.46 System architecture with ingress.
47. Figure 5.47 Two phases of scheduler.
48. Figure 5.48 Ready status of the control plane node.
49. Figure 5.49 Dashboard while the control plane node is ready.
50. Figure 5.50 Generating the join token of the control plane.
51. Figure 5.51 Worker node joining the cluster by the join token.
52. Figure 5.52 Status of the cluster shown by kubectl.
53. Figure 5.53 Status of the worker1 shown in dashboard.
54. Figure 5.54 Example of a YAML file
55. Figure 5.55 Deploying the httpd service by applying example.yaml.
56. Figure 5.56 Status of Pods shown in dashboard.
57. Figure 5.57 Screenshot displaying a workable httpd service.
Chapter 6
1. Figure 6.1 Architecture design of the AMCoT framework.
2. Figure 6.2 Functional block diagram of the AVM server.
3. Figure 6.3 Functional block diagram of the BPM scheme in the IPM server.
4. Figure 6.4 Functional block diagram of the KSA scheme in the IYM server.
5. Figure 6.5 Cloud‐based iFA system platform.
6. Figure 6.6 Server‐based iFA system platform.
Chapter 7
1. Figure 7.1 Architecture design of the AMCoT framework.
2. Figure 7.2 An example of intelligent manufacturing platform based on the AMC...
3. Figure 7.3 Framework of CPA.
4. Figure 7.4 Framework of CPA_C.
5. Figure 7.5 System architecture of RCS_CPA.
6. Figure 7.6 Horizontal auto‐scaling mechanism of PAM_C’s in RCS_CPA.
7. Figure 7.7 Load balance mechanism of PAM_C’s in RCS_CPA.
8. Figure 7.8 Failover mechanism among the Pods of a PAM_C in RCS_CPA.
9. Figure 7.9 Screenshot showing that a Kubernetes cluster has been created, an...
10. Figure 7.10 Screenshot showing that the BPM_C has four Pods for load balance....
11. Figure 7.11 Screenshot showing that the four Pods of the BPM_C are distribute...
12. Figure 7.12 Health‐gauging web GUI of the BPM_C, which contains four Pods wor...
13. Figure 7.13 Framework of the big‐data‐analytics application platform.
14. Figure 7.14 Comparison of query times using four types of data table in expe...
15. Figure 7.15 Comparison of query times using four types of data format in exp...
16. Figure 7.16 Architecture of the proposed BEDPS.
17. Figure 7.17 Three‐phase workflow of MSACS.
18. Figure 7.18 System architecture of MSACS.
19. Figure 7.19 Hierarchical information of a Jar SSLP in a Java decompiler.
20. Figure 7.20 Hierarchical information of a C# DLL SSLP in a C# decompiler....
21. Figure 7.21 Generic KI extraction algorithm of SSLPs.
22. Figure 7.22 Illustration of the Lib. Info. Template in JSON.
23. Figure 7.23 Illustration of the SI Info. Template in JSON.
24. Figure 7.24 C# WSP template.
25. Figure 7.25 Example of “APIController.cs” for C# WSP template.
26. Figure 7.26 Flowchart of the automated source code generation.
27. Figure 7.27 Result of automated code generation for the WSP template in Figu...
28. Figure 7.28 Automated service construction mechanism designed in Server Cons...
29. Figure 7.29 Web GUI of MSACS.
30. Figure 7.30 GUI showing the method information in the AVMService.dll.
31. Figure 7.31 GUI showing the web API information of the AVM service.
32. Figure 7.32 GUI for conducting virtual metrology using the created AVM CMfg ...
33. Figure 7.33 Four‐phase workflow of MSACS_C.
Chapter 8
1. Figure 8.1 Comparison between actual metrology and virtual metrology.
2. Figure 8.2 Current physical metrology operating scenarios.
3. Figure 8.3 Tool and process monitoring without and with VM.
4. Figure 8.4 Illustration of a false alarm and a missed detection.
5. Figure 8.5 Automatic Virtual Metrology (AVM) system.
6. Figure 8.6 AVM server.
7. Figure 8.7 Advanced dual‐phase VM algorithm.
8. Figure 8.8 Plugging AVM into the MES framework.
9. Figure 8.9 Relationships among AVM, MES components, and R2R controllers.
10. Figure 8.10 Operating scenarios among AVM, MES components, and R2R controlle...
11. Figure 8.11 Collaboration diagram for integrating AVM into MES.
12. Figure 8.12 Model of EWMA R2R control.
13. Figure 8.13 W2W control scheme utilizing VM [15, 16, 32].
14. Figure 8.14 AVM server with PreY input.
15. Figure 8.15 Schematic Diagram of Defining RI for Normal Distribution.
16. Figure 8.16 Schematic diagram of defining RI_W for Weibull distribution.
17. Figure 8.17 W2W control scheme utilizing AVM with RI and GSI. (a) Complete W...
18. Figure 8.18 Simulation results of 5‐cases APC methods of Round 1.
19. Figure 8.19 RI and GSI exceed their thresholds at Sample 50 of Round 1.
20. Figure 8.20 GSI exceeds its threshold at Sample 349 of Round 1.
21. Figure 8.21 Simulation results of Cases 3‐5 for the first 200 samples. (a) R...
22. Figure 8.22 Mean MAPE_P curves as functions of α₁ of 5‐cases APC methods...
23. Figure 8.23 Automation levels of virtual metrology systems.
Chapter 9
1. Figure 9.1 SPC control chart of throttle‐valve angles in a PECVD tool.
2. Figure 9.2 BPM scheme.
3. Figure 9.3 State diagram of a device.
4. Figure 9.4 Procedure of collecting the important samples needed for creating...
5. Figure 9.5 Configurations of SPC control charts of DHI and BEI. (a) Converti...
6. Figure 9.6 Flow chart of baseline FDC execution procedure.
7. Figure 9.7 ECF RUL prediction model.
8. Figure 9.8 Flowchart for calculating ECF RUL.
9. Figure 9.9 Advanced BPM (ABPM) scheme.
10. Figure 9.10 Prediction results of the ECF model. (a) Aging feature predictio...
11. Figure 9.11 Flow chart of the TSP algorithm.
12. Figure 9.12 Flow chart of the pre‐alarm module (PreAM).
13. Figure 9.13 Management and equipment views of a solar‐cell manufacturing fac...
14. Figure 9.14 Health index hierarchy.
15. Figure 9.15 Intelligent predictive maintenance (IPM).
16. Figure 9.16 Implementation architecture of the IPM_C (i.e. IPM_C‐IA) based on ...
17. Figure 9.17 Workflow for constructing and deploying the IPM_C in a Kubernetes...
18. Figure 9.18 Example IPM_C volume YAML file.
19. Figure 9.19 Example IPM_C deployment YAML file.
20. Figure 9.20 Example Dockerfile for creating the ABPM image.
21. Figure 9.21 Example IPM_C service YAML file.
Chapter 10
1. Figure 10.1 Changing curves of yield and cost during the product life cycle....
2. Figure 10.2 Traditional root‐cause search process of a yield loss.
3. Figure 10.3 Intelligent yield management system.
4. Figure 10.4 Procedure for finding the root causes of a yield loss by applyin...
5. Figure 10.5 The KSA scheme.
6. Figure 10.6 Flowchart of ALASSO with automated λ adjusting. λ: penalty; KV...
7. Figure 10.7 Flow chart of the BSA module.
8. Figure 10.8 Rule I in the BSA module.
9. Figure 10.9 Rule II in the BSA module.
10. Figure 10.10 Flow Chart of the Regression Tree.
11. Figure 10.11 Description of Regression Tree Step 1 and Step 2.
Chapter 11
1. Figure 11.1 Process flow of TFT‐LCD manufacturing.
2. Figure 11.2 Semiconductor layer of the TFT process flow with deployment of A...
3. Figure 11.3 Thin‐film structure in CVD process.
4. Figure 11.4 Combination of TFT photo step.
5. Figure 11.5 CF manufacturing process flow with deployment of AVM servers. (a...
6. Figure 11.6 PS layer flow of the CF manufacturing process with deployment of...
7. Figure 11.7 LCD manufacturing process flow with deployment of AVM servers. (...
8. Figure 11.8 Dual‐stage indirect VM architecture.
9. Figure 11.9 Single‐stage example: Stage‐I VM results at Position 2. LCL, low...
10. Figure 11.10 Dual‐stage example: Stage‐II VM results at Position 2. LCL, low...
11. Figure 11.11 Combination example of cooperative‐tools: VM_I result at Positio...
12. Figure 11.12 Illustration of an erroneous measurement.
13. Figure 11.13 Inline example of cooperative‐tools at Position 1: (a) NN_I and ...
14. Figure 11.14 TFT manufacturing process.
15. Figure 11.15 PEP flow of the semiconductor layer.
16. Figure 11.16 Accumulated Type 2 loss results.
17. Figure 11.17 Procedure for finding the root causes of a yield loss by applyi...
18. Figure 11.18 RI_K result of X_R search.
19. Figure 11.19 KSA search results of Type 2 loss on Lot 49. (a) Top 1 Device: ...
20. Figure 11.20 RI_K result of X_P search.
21. Figure 11.21 Root cause analysis of control voltage on Chamber A of Equipmen...
22. Figure 11.22 T2T control scenario of the PECVD manufacturing process.
23. Figure 11.23 T2T control scheme.
24. Figure 11.24 T2T controller.
25. Figure 11.25 Scenario of Applying the AVM system to the PECVD process.
26. Figure 11.26 VM accuracy verification.
27. Figure 11.27 T2T with AM. (a) All samples. (b) APC samples.
28. Figure 11.28 T2T with VM. (a) All samples. (b) APC samples.
29. Figure 11.29 T2T+VM without RI&GSI. (a) All samples. (b) APC samples.
30. Figure 11.30 T2T+VM with RI&GSI. (a) All samples. (b) APC samples.
31. Figure 11.31 RI and GSI are lower than RI_T and GSI_T, respectively at Sample ...
32. Figure 11.32 Cycle‐time improvement by applying T2T+AVM.
33. Figure 11.33 Illustration of the necessity of adopting the C&H modeling samp...
34. Figure 11.34 Results of the FDC portion of the BPM scheme.
35. Figure 11.35 BPM‐related data and indexes of an entire PM period.
36. Figure 11.36 ECF RUL predictive results.
37. Figure 11.37 Throttle valve RUL predictive results of the TSP algorithm. (a)...
38. Figure 11.38 IPM dashboard. (a) Management view. (b) Equipment view.
39. Figure 11.39 Equipment status dashboard in MES.
40. Figure 11.40 Sequence diagram showing the interfaces between the MES and IPM...
41. Figure 11.41 Illustrations of the functions of RI, GSI, and dual‐phase schem...
42. Figure 11.42 Production line of the bumping process.
43. Figure 11.43 Common equipment model of the Sputter equipment.
44. Figure 11.44 Turbo Pump RUL predictive results of the TSP algorithm. (a) Agi...
45. Figure 11.45 Illustration of UBM bumping process variables (5 device variabl...
46. Figure 11.46 Analysis results with/without IESA. (a) Without IESA analysis. ...
47. Figure 11.47 IESA Regression Tree analysis results.
48. Figure 11.48 Illustration of adding new interaction‐effect variables (SD 01 ...
49. Figure 11.49 Conversion of the OS‐to‐SS Q‐time variable into binary form....
50. Figure 11.50 Trend chart of the accumulated yield loss vs. OS‐to‐SS Q‐time....
51. Figure 11.51 Integrating GAVM into WMA.
52. Figure 11.52 GAVM architecture for machine tools.
53. Figure 11.53 A unique QR‐code‐identification engraved on the mounting‐surfac...
54. Figure 11.54 One‐to‐many relationship among a vender and its customers via A...
55. Figure 11.55 Architecture of the existing GAVM system.
56. Figure 11.56 Detailed drawing of integrating WMA’s vender and customers into...
57. Figure 11.57 Global cyber‐physical interactions (AVM models refreshing). LCL...
58. Figure 11.58 Operating scenarios of modeling and running samples of the TVA ...
59. Figure 11.59 Flowchart of the TVA scheme.
60. Figure 11.60 VM results with and without the TVA scheme.
61. Figure 11.61 Comparison of on‐machine probing (OMP), CMM, and VM.
62. Figure 11.62 Using a probe to touch the outside of an EC end‐face.
63. Figure 11.63 Position trends and their curve‐fitting results of 10 ECs.
64. Figure 11.64 Actual deformed position (D) and ideal position (I) of an EC....
65. Figure 11.65 Approximate machining position (A) on a deformed EC.
66. Figure 11.66 Flowchart of generating the fittest ellipse and AC via GA.
67. Figure 11.67 Flowchart of integrating the on‐line probing, the DF scheme, an...
68. Figure 11.68 AVM results for diameter prediction.
69. Figure 11.69 Position prediction. (a) VM results of four cases: (1) without ...
70. Figure 11.70 Comparison of off‐line measurement and virtual metrology.
71. Figure 11.71 CPAVM scheme.
72. Figure 11.72 Illustration of the production‐data‐traceback (PDT) mechanism....
73. Figure 11.73 Information flow of the PDT mechanism.
74. Figure 11.74 AMCoT for carbon‐fiber manufacturing.
75. Figure 11.75 AVM results of sizing percentage.
76. Figure 11.76 Carbon‐fiber manufacturing on‐line display results.
77. Figure 11.77 Two‐stage PET stretch‐blow molding machine.
78. Figure 11.78 Implementation of AVM for blow molding machines.
79. Figure 11.79 Architecture of IM‐based R2R control.
80. Figure 11.80 Architecture of AVM‐based R2R control.
81. Figure 11.81 AVM‐based R2R control implementation in multiple machines.
82. Figure 11.82 Flow chart of AVM‐based R2R control scheme.
83. Figure 11.83 Experimental results of AVM‐based R2R control for Case 1‐out‐of...
84. Figure 11.84 C_PM values of Case 1‐out‐of‐1 lot.

Preface

In the era of global competition, improving productivity and increasing yield of the manufacturing industries through information and communication as well as cloud computing technologies, big data analytics, and cyber‐physical systems (CPS) are the common goals for the manufacturers worldwide. For instance, Germany proposed Industry 4.0 in hopes to construct Smart Factory so as to enhance its global competitiveness and continue to take the lead in manufacturing. The Advanced Manufacturing Partnership issued by the United States aimed to regain the leadership in international manufacturing competitiveness for attracting the manufacturing industries back to the United States. Chinese government brought out the plan called “Made in China 2025” in 2015, where the guidelines and strategies of becoming one of the countries with strong manufacturing power by 2025 were clearly stated. Facing the United States‐China trade war that began in 2018, even though Chinese government stopped to mention Made in China 2025 in public to not intensify the conflict, they claimed that their goals of enhancing Intelligent Manufacturing have not been changed. Adjustments on the strategies in accordance with the international trend would be made, which is considered as the new version of Made in China 2025.

Overview and Goals

The current Industry 4.0 related technologies emphasize on productivity improvement but not on quality enhancement; in other words, they can only keep the faith of achieving nearly Zero‐Defects (ZD) state without realizing this goal. The key reason for this inability is the lack of an affordable online and real‐time Total Inspection technology. By adopting the Automatic Virtual Metrology (AVM) that has been certified with the invention patents from six countries (Taiwan ROC, USA, Japan, Germany, China, and Korea) developed by the research team of Fan‐Tien Cheng, the Editor and main author of this book, ZD can be achieved as AVM can provide the Total Inspection data of all products online and in real time. A defective product will be discarded once it is detected by AVM; in this way, all of the deliverables will be ZD. Further, the Key‐variable Search Algorithm (KSA) of the Intelligent Yield Management (IYM) system developed by our research team can be utilized to find out the root causes of the defects for continuous improvement on those defective products. As such, ZD of all products can be achieved. Therefore, once AVM and IYM are integrated into the successfully developed Industry 4.0 platform constituting of Internet of things (IoT), CPS, big data analytics, and cloud computing, the state of ZD can be realized, which is defined as Industry 4.1 by Fan‐Tien Cheng. The concepts of Industry 4.1 were disclosed in IEEE Robotics and Automation Letters in January 2016.

To realize and promote Intelligent Manufacturing, National Cheng Kung University (NCKU) established the Intelligent Manufacturing Research Center (iMRC) with Professor Fan‐Tien Cheng being its Director. Based on the platform of Advanced Manufacturing Cloud of Things (AMCoT) that won IEEE Conference on Automation Science and Engineering (CASE) 2017 Best Application Paper Award, iMRC integrates cross‐disciplinary research resources, utilizes various Intelligent‐Manufacturing related technologies, and implements Intelligent Manufacturing services [including AVM, IYM, Intelligent Predictive Maintenance (IPM), …, etc.] to develop the so‐called Intelligent Factory Automation (iFA) System Platform. Through implementing the iFA System Platform to the manufacturing tools and production lines of high‐tech (e.g. semiconductor, TFT‐LCD, and solar cell) and traditional industries (e.g. machine tool, aerospace, blow molding machine, and carbon fiber), ZD of all products as well as highly efficient and flexible intelligence capabilities (single‐machine intelligence, production‐line intelligence, and global‐fab intelligence) can be accomplished for improving the competitiveness and profits of all Intelligent‐Manufacturing related industries.

Organization and Features

To promote Intelligent Manufacturing and carry out the vision of Industry 4.1, our research team decided to include the survey and introduction of Intelligent Manufacturing, as well as the intact concept and the core technologies of Industry 4.1, along with the successful cases of iFA implementation for Industry 4.1 in different industries all in this book. This book contains 11 chapters in total. Chapters 1–5 describe the evolution of automation and the development strategy of Intelligent Manufacturing and introduce the mandatory components and fundamental technologies for constructing Intelligent Manufacturing. Chapter 6 introduces the overall concept of iFA integrated by the AMCoT framework with pluggable modules of AVM, IPM, and IYM intelligent services. Two versions of the iFA System Platform are provided for different business models. Chapter 7 illustrates the AMCoT framework for constructing the advanced cloud manufacturing platform. Chapters 8–10 address the principles and implementation of AVM, IPM, and IYM, respectively. Finally, the actual Intelligent‐Manufacturing implementation and application cases adopting all the techniques mentioned above in seven industries, including flat panel display, solar cell, semiconductor, automobile, aerospace, carbon fiber, and blow molding, are presented in Chapter 11. Moreover, all the major patents related to AMCoT, AVM, IPM, and IYM are listed in Appendices 7.C, 8.C, 9.C, and 10.C, respectively.

A photograph of a signature.

Fan‐Tien Cheng

Life Fellow, IEEE

Chair Professor, NCKU

Director of Intelligent Manufacturing Research Center (iMRC), NCKU

July 2021

Jón Atli Benediktsson	Xiaoou Li	Jeffrey Reed
Anjan Bose	Lian Yong	Diomidis Spinellis
David Alan Grier	Andreas Molisch	Sarah Spurgeon
Elya B. Joffe	Saeid Nahavandi	Ahmet Murat Tekalp

Intelligent Manufacturing with Zero Defects

Overview and Goals

Organization and Features