Big Data Analytics and Decision Analysis for Manufacturing Intelligence to Empower Industry 3.5

Transcription

1 ISMI2015, Oct , 2015 KAIST, Daejeon, South Korea Big Data Analytics and Decision Analysis for Manufacturing Intelligence to Empower Industry 3.5 Tsinghua Chair Professor Chen-Fu Chien, Ph.D. Department of IEEM, National Tsing Hua University, Hsinchu 30013, Taiwan Director, NTHU-TSMC Center for Manufacturing Excellence; STEP Consortium Co-chair: IEEE RAS Technical Committee on Semiconductor Manufacturing Automation Manufacturing Strategies of Leading Nations Industry 4.0 Europe 2020 Manufacturing Renaissance Cyber-Physical System, Smart Factory Information and Communication Technology, Cloud Computing, Internet of Things and Services AMP R&D, 3D Printing, Big Data, Advanced Robots, Energy Saving Robotics Revolution Industry Innovation 3.0 Made in China 2025 Humans and Robots Coexist Mutually-connected Robots, Autonomous Data Accumulatign and Utilizing ICT, Smart Factories Internet of Things, Smart Plant, 3D Printing, Parts and Materials Higher Value-Added Manufacturing Digital Manufacturing, Innovation-driven, Ecofriendly Manufacturing

2 3 The US Manufacturing Enhancement Act Challenges and Opportunities for US Manufacturing Renaissance A National Strategic Plan for Advanced Manufacturing "Buy American" Plan A Five Year Plan to boost U.S. exports White House AMP Initiatives 4

3 Industry 4.0: German Future Project Germany is preparing the 4th industrial revolution based on the Internet of Things, Internet of Services, Big Data Analytics to empower Cyber-physical Production Systems to enhance various industries. 5 Needs for Smart & Green Production In the next 40 years to the Year 2050

4 Four stages of the Industrial Revolution 1 st : water (end of 18 th century)- steam-powered mechanical manufacturing facilities 2 nd : (start of 20 th century)- electrically-powered mass production 3 rd : (start of 1970s)- electronics and IT to achieve automation 4 th : (today)- Cyber-Physical Systems *Source: Federal Ministry of Education and Research (2013), "Securing the future of German manufacturing industry recommendation the strategic initiative INDUSTRIE 4.0 final report of the industrie 4.0 working group," National Academy of Science and Engineering. 7 Industry 2.0!? The Second Industrial Revolution, also known as the Technological Revolution, [1] was a phase of the larger Industrial Revolution corresponding to the latter half of the 19th century, sometime between 1840 and 1860 until World War I. It is considered to have begun around the time of the introduction of Bessemer steel in the 1850s and culminated in early factory electrification, mass production and the production line. (Wikipedia) Taylorism: Scientific Management (Industrial Engineering) 8

5 Means or Objectives: Industry 4.0 is different from Lean Production!? 工 業 4.0 與 豐 田 生 產 方 式 都 是 後 拉 式 生 產 體 系, 需 要 的 時 候 才 按 照 所 需 的 量 生 產 所 需 的 產 品 工 業 4.0 則 是 單 個 生 產 體 系, 不 同 生 產 線 連 在 一 起, 靈 活 運 用 包 括 感 測 器 (Sensor ) 軟 體 ( Software ) 解 決 方 案 服 務 (Solution service), 隨 時 交 換 大 數 據, 可 按 照 客 戶 要 求, 隨 意 改 變 供 應 商 和 生 產 程 序, 實 現 符 合 成 本 效 益 的 訂 製 生 產 (Tailor Made), 稱 為 大 規 模 訂 製 (Mass Customization) 工 業 4.0 主 張 通 過 技 術 的 使 用, 來 實 現 整 個 製 造 業 而 不 僅 僅 只 是 工 廠 的 革 新 9 source:bosch 10

6 source:bosch 11 SIEMENS: open cloud platform for industrial customers A powerful, secure, and reliable cloud infrastructure enabling external experts to access the data, and merge them with other information Siemens acts as a data custodian for customers Siemens sets standards in terms of connectivity to both Siemens and thirdparty devices within and outside the plant Easy integration into the Cloud for Industry Currently in a pilot phase and is gradually planned to be rolled out to further customers from November 2015 source:siemens 12

7 Simens: open cloud platform for industrial customers Siemens Plant Analytics Services Siemens acts as a data custodian for customers Platform for data-based services such as energy data management Platform for analyzing big data from industrial applications Solution will be based on the SAP HANA Cloud Platform Platform as a Service offering for OEMs and application developers source:siemens 13 14

8 Value Network and Ecosystem Increasing adoption of cloud, internet, smart phones, wearable devices, multimode sensors enabled an unprecedented level of global mobile connectivity Cloud ecosystem is the complex system of interdependent components that work together to enable cloud services. Competition is now among business ecosystems, no longer between individual companies Value chain is restructured as network, supply chain position is changing, & firm boundary become blurred. 15 Industry 3.5: Hybrid Strategy of best practice of Industry 3.0 and tobe Industry 4.0 with disruptive innovations to empower manufacturing intelligence. 16

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10 Modeling, Big Data, and Decision Analysis to Empower Manufacturing Intelligence 19 20

11 21 巨 量 變 動 性 多 樣 性 真 實 性 圖 片 來 源 :( 22

12 Data Volume & Data Veracity Extreme and missing values are filled in red and blue, respectively. 23 Data Quality due to fixed recipe & multi-collinearity 24

13 Data Veracity latency/multi-response metrology-wat data WAT (Current, Voltage, Resistance) Inline (Thickness, Critical Dimension) 25 26

14 Aggregated Data Variety: High-dimensional, Multi-collinear, Imbalanced Big Data For-ward prediction & process control CP data WAT data Granularity Production data Defect data Metrology data Backward diagnosis & trouble shooting Detail Equipment data second minute hour day week month Frequency Time series 27 28

15 Fab Cycle Prediction and Reduction 29 30

16 Drain > 30 layers Source Critical dimension (CD) -> 20nm 31 Overlay errors N+1 layer N layer x 2 y 1 y 2 x 1 d d x X y Y x1 x2 2 y1 y2 2 32

17 Y ( d x X, d y Y ) Light source d Y Reticle d y d x d X X Lens Wafer 33 Dynamic backups to consider both quality and productivity 34

18 y 2 x 1 x 2 y 1 35 Proposed R2R control block diagram for overlay error compensation Step1. Overlay process modeling for R2R control Step2. DAPI controller design Step3. Performance monitoring and evaluation 36

19 T x+x input 0.05 Empirical Study Lot sequense EWMA DAPI 无 论 输 出 值 或 投 入 值,DAPI 的 变 异 均 较 小! T x+x output Lot sequence EWMA DAPI 37 38

20 Problem definition ECD control enhancement Problem definition Data preparation Data collection & integration Data cleaning & pre-inspection Data preparation Correlation analysis for identifying influence factors Model construction Parameter estimation Model construction Significant? Yes Linear model for modeling etching bias Least square estimation No Dispatching rule development & offline validation Parameter estimation Is fitness good enough? Yes Product & chamber effect extraction No Implementation Dispatching rule development Dissimilarity measurement Dispatching rule simulation Is result acceptable? No Yes Implementation Performance tracing & discussion 39 Advanced Process Control (APC) for CD control DCD ECD Photo Metrology Etching Metrology controller controller controller feedback feedforward feedback APC Methodologies: Run-to-run control Fault detection and classification (FDC) Virtual metrology 40

21 Manufacturing intelligence framework for DCD-ECD variation reduction Estimate the chamber effects via mining historical data. Define similarity measurement for etching chambers and tools, respectively, to match with DCD results of wafers. Determine tool priority for each process lot to support realtime tool assignment and production control. 41 Etch bias = ECD DCD Modeling Etch Bias and Estimating Product/Chamber Effects Linear regression model is used to model Etch bias and estimate the product and chamber effects E x Prod ip P i p 1 x and Prod ip P p1 x chamber ic C c1 Prod p x x are binary var iable (0 or 1). chamber ic Prod ip 1 C c1 chamber c x chamber ic, i 1,..., n. i some of considered parameters : mean bias (idealy, ˆ ECD target - DCD target) Prod p chamber c chamber c : Product : chamber : standard effect for effect error of Product chamber p for chamber c effect for chamber c 42

22 Chamber effect target Chamber dissimilarity measurement chamber effect target is that the chamber effect c meeting this target via the best process chamber for lot i. T chamber i ECD_Target DCD ˆ Dissimilarity for each etch chamber, i 1,..., n. The dissimilarity not only considers the squared distance between estimated chamber effect and chamber effect target but also the standard error of estimated chamber effect. The chamber had the smallest dissimilarity is the best one for lot i. i P p1 ˆ Prod p x Prod ip d c i ˆ chamber chamber 2 c Ti, i 1,... n; c 1,..., C. ˆ chamber c 43 Validation and Implementation The C pk improvement was 20% in average after implementation in an empirical study for a few months for a field test in Taiwan. The scaling score is used to monitor the operational effectiveness of the dispatching rules to trace the control performance. Frequency 25% 20% 15% 10% 5% 0% Average C pk : 2.83 Average C pk : % 13.03% 11.65% 11.59% 10.26% 7.85% 7.79% 7.37% 3.73% 4.03% Tool score interval Before implementation After implementation Product Number Standard Number Standard RMSE C of lot deviation pk RMSE of lot deviation C pk C pk improvement A % B % C % D % E % F % G % H % 44

23 Dynamic Decisions for siteimbalances IC final testing 45 Fundamental Objective Hierarchy and Means Objective Network 46

24 Trade-off among alternatives 47 From Influence Diagram to Decision Tree 48

25 Decisions in the Decision Flow Lot-End Shut Down? Yes No Shut Down For Repair Close Site(s)? No Yes Continue Repair? Close Low Yield Site(s) No Yes Complete Repair Continue Test 49 Validation of Empirical Study Site ID Estimated Load Estimated Pass Real Yield Site ID Estimated Load Estimated Pass Real Yield On-site operator decision: Close 14 sites including site 1, 3, 4, 5, 7, 9, 11, 12, 13, 15, 21, 23, 29, and 32. Passed Units: 322 units/ Testing Time: 28 time units Proposed optimal decision: Close 2 sites of the site 1 and 7. Passed Units: units/ Testing Time: 13 time units p.s. ART (allowable repair time) = 8.45 minutes and thus should not shut down for repair 50

26 Apple + IBM to empower business analytics, optimization & decision 51 52

27 53 Thank you very much for your kind attentions!!! 54