Industry 4.1. Группа авторов

GSI exceeds its threshold at Sample 349 of Round 1.Figure 8.21 Simulation results of Cases 3‐5 for the first 200 samples. (a) R...Figure 8.22 Mean MAPE_P curves as functions of α₁ of 5‐cases APC methods...Figure 8.23 Automation levels of virtual metrology systems.

      9 Chapter 9Figure 9.1 SPC control chart of throttle‐valve angles in a PECVD tool.Figure 9.2 BPM scheme.Figure 9.3 State diagram of a device.Figure 9.4 Procedure of collecting the important samples needed for creating...Figure 9.5 Configurations of SPC control charts of DHI and BEI. (a) Converti...Figure 9.6 Flow chart of baseline FDC execution procedure.Figure 9.7 ECF RUL prediction model.Figure 9.8 Flowchart for calculating ECF RUL.Figure 9.9 Advanced BPM (ABPM) scheme.Figure 9.10 Prediction results of the ECF model. (a) Aging feature predictio...Figure 9.11 Flow chart of the TSP algorithm.Figure 9.12 Flow chart of the pre‐alarm module (PreAM).Figure 9.13 Management and equipment views of a solar‐cell manufacturing fac...Figure 9.14 Health index hierarchy.Figure 9.15 Intelligent predictive maintenance (IPM).Figure 9.16 Implementation architecture of the IPM_C (i.e. IPM_C‐IA) based on ...Figure 9.17 Workflow for constructing and deploying the IPM_C in a Kubernetes...Figure 9.18 Example IPM_C volume YAML file.Figure 9.19 Example IPM_C deployment YAML file.Figure 9.20 Example Dockerfile for creating the ABPM image.Figure 9.21 Example IPM_C service YAML file.

      10 Chapter 10Figure 10.1 Changing curves of yield and cost during the product life cycle....Figure 10.2 Traditional root‐cause search process of a yield loss.Figure 10.3 Intelligent yield management system.Figure 10.4 Procedure for finding the root causes of a yield loss by applyin...Figure 10.5 The KSA scheme.Figure 10.6 Flowchart of ALASSO with automated λ adjusting. λ: penalty; KV...Figure 10.7 Flow chart of the BSA module.Figure 10.8 Rule I in the BSA module.Figure 10.9 Rule II in the BSA module.Figure 10.10 Flow Chart of the Regression Tree.Figure 10.11 Description of Regression Tree Step 1 and Step 2.

      11 Chapter 11Figure 11.1 Process flow of TFT‐LCD manufacturing.Figure 11.2 Semiconductor layer of the TFT process flow with deployment of A...Figure 11.3 Thin‐film structure in CVD process.Figure 11.4 Combination of TFT photo step.Figure 11.5 CF manufacturing process flow with deployment of AVM servers. (a...Figure 11.6 PS layer flow of the CF manufacturing process with deployment of...Figure 11.7 LCD manufacturing process flow with deployment of AVM servers. (...Figure 11.8 Dual‐stage indirect VM architecture.Figure 11.9 Single‐stage example: Stage‐I VM results at Position 2. LCL, low...Figure 11.10 Dual‐stage example: Stage‐II VM results at Position 2. LCL, low...Figure 11.11 Combination example of cooperative‐tools: VM_I result at Positio...Figure 11.12 Illustration of an erroneous measurement.Figure 11.13 Inline example of cooperative‐tools at Position 1: (a) NN_I and ...Figure 11.14 TFT manufacturing process.Figure 11.15 PEP flow of the semiconductor layer.Figure 11.16 Accumulated Type 2 loss results.Figure 11.17 Procedure for finding the root causes of a yield loss by applyi...Figure 11.18 RI_K result of X_R search.Figure 11.19 KSA search results of Type 2 loss on Lot 49. (a) Top 1 Device: ...Figure 11.20 RI_K result of X_P search.Figure 11.21 Root cause analysis of control voltage on Chamber A of Equipmen...Figure 11.22 T2T control scenario of the PECVD manufacturing process.Figure 11.23 T2T control scheme.Figure 11.24 T2T controller.Figure 11.25 Scenario of Applying the AVM system to the PECVD process.Figure 11.26 VM accuracy verification.Figure 11.27 T2T with AM. (a) All samples. (b) APC samples.Figure 11.28 T2T with VM. (a) All samples. (b) APC samples.Figure 11.29 T2T+VM without RI&GSI. (a) All samples. (b) APC samples.Figure 11.30 T2T+VM with RI&GSI. (a) All samples. (b) APC samples.Figure 11.31 RI and GSI are lower than RI_T and GSI_T, respectively at Sample ...Figure 11.32 Cycle‐time improvement by applying T2T+AVM.Figure 11.33 Illustration of the necessity of adopting the C&H modeling samp...Figure 11.34 Results of the FDC portion of the BPM scheme.Figure 11.35 BPM‐related data and indexes of an entire PM period.Figure 11.36 ECF RUL predictive results.Figure 11.37 Throttle valve RUL predictive results of the TSP algorithm. (a)...Figure 11.38 IPM dashboard. (a) Management view. (b) Equipment view.Figure 11.39 Equipment status dashboard in MES.Figure 11.40 Sequence diagram showing the interfaces between the MES and IPM...Figure 11.41 Illustrations of the functions of RI, GSI, and dual‐phase schem...Figure 11.42 Production line of the bumping process.Figure 11.43 Common equipment model of the Sputter equipment.Figure 11.44 Turbo Pump RUL predictive results of the TSP algorithm. (a) Agi...Figure 11.45 Illustration of UBM bumping process variables (5 device variabl...Figure 11.46 Analysis results with/without IESA. (a) Without IESA analysis. ...Figure 11.47 IESA Regression Tree analysis results.Figure 11.48 Illustration of adding new interaction‐effect variables (SD 01 ...Figure 11.49 Conversion of the OS‐to‐SS Q‐time variable into binary form....Figure 11.50 Trend chart of the accumulated yield loss vs. OS‐to‐SS Q‐time....Figure 11.51 Integrating GAVM into WMA.Figure 11.52 GAVM architecture for machine tools.Figure 11.53 A unique QR‐code‐identification engraved on the mounting‐surfac...Figure 11.54 One‐to‐many relationship among a vender and its customers via A...Figure 11.55 Architecture of the existing GAVM system.Figure 11.56 Detailed drawing of integrating WMA’s vender and customers into...Figure 11.57 Global cyber‐physical interactions (AVM models refreshing). LCL...Figure 11.58 Operating scenarios of modeling and running samples of the TVA ...Figure 11.59 Flowchart of the TVA scheme.Figure 11.60 VM results with and without the TVA scheme.Figure 11.61 Comparison of on‐machine probing (OMP), CMM, and VM.Figure 11.62 Using a probe to touch the outside of an EC end‐face.Figure 11.63 Position trends and their curve‐fitting results of 10 ECs.Figure 11.64 Actual deformed position (D) and ideal position (I) of an EC....Figure 11.65 Approximate machining position (A) on a deformed EC.Figure 11.66 Flowchart of generating the fittest ellipse and AC via GA.Figure 11.67 Flowchart of integrating the on‐line probing, the DF scheme, an...Figure 11.68 AVM results for diameter prediction.Figure 11.69 Position prediction. (a) VM results of four cases: (1) without ...Figure 11.70 Comparison of off‐line measurement and virtual metrology.Figure 11.71 CPAVM scheme.Figure 11.72 Illustration of the production‐data‐traceback (PDT) mechanism....Figure 11.73 Information flow of the PDT mechanism.Figure 11.74 AMCoT for carbon‐fiber manufacturing.Figure 11.75 AVM results of sizing percentage.Figure 11.76 Carbon‐fiber manufacturing on‐line display results.Figure 11.77 Two‐stage PET stretch‐blow molding machine.Figure 11.78 Implementation of AVM for blow molding machines.Figure 11.79 Architecture of IM‐based R2R control.Figure 11.80 Architecture of AVM‐based R2R control.Figure 11.81 AVM‐based R2R control implementation in multiple machines.Figure 11.82 Flow chart of AVM‐based R2R control scheme.Figure 11.83 Experimental results of AVM‐based R2R control for Case 1‐out‐of...Figure 11.84 C_PM values of Case 1‐out‐of‐1 lot.

      Guide

      1  Cover Page

      2  Series Page

      3  Title Page

      4  Copyright Page

      5  Editor Biography

      6  List of Contributors

      7  Preface

      8  Acknowledgments

      9  Foreword

      10  Table of Contents

      11  Begin Reading

      12  Index