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

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

[email protected] 2015/10/17@KAIST

ISMI2015, Oct. 16-18, 2015

KAIST, Daejeon, South Korea

AMP

• R&D, 3D Printing, Big Data, Advanced Robots, Energy Saving

Industry 4.0

Cyber-Physical System, Smart Factory

• Information and Communication Technology, Cloud Computing, Internet of Things and Services

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, Eco-friendly Manufacturing

Manufacturing Strategies of Leading

Nations

3

Challenges and Opportunities for

US Manufacturing Renaissance

5

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.

In the next 40 years to the Year 2050

Four stages of

the “Industrial Revolution”

manufacturing facilities

 2nd: (start of 20thcentury)- electrically-powered mass production  3rd: (start of 1970s)- electronics and IT to achieve automation  4th : (today)- Cyber-Physical Systems

7

*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.

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 steelin the 1850s and culminated in early

factory electrification,mass production and theproduction line. (Wikipedia)

Means or Objectives: Industry 4.0 is

different from Lean Production!?

9 • 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。 10 source：BOSCH

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source：BOSCH

source：SIEMENS

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

Easy integration into the Cloud for Industry

Siemens sets standards in terms of connectivity to both Siemens and third-party devices within and outside the plant

Currently in a pilot phase and is gradually planned to be rolled out to further customers from November 2015

Simens: open cloud platform for

industrial customers

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 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

http://www.industry.siemens.com/services/global/en/portfolio/plant-data-services/cloud-for-industry/pages/default.aspx http://www.siemens.com/press/pool/de/pressemitteilungen/2015/digitalfactory/PR2015030152DFEN.pdf

Siemens Plant Analytics Services

Siemens acts as a data custodian

for customers

Value Network and Ecosystem

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.

15  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.

Industry 3.5：

Hybrid Strategy of

best practice of

Industry 3.0 and

to-be Industry 4.0 with

disruptive

innovations to

empower

manufacturing

intelligence.

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Modeling, Big Data, and Decision Analysis to

Empower Manufacturing Intelligence

21

22

巨量

變動性

多樣性

真實性

Data Volume & Data Veracity

23  Extreme and missing values are filled in red and blue, respectively.

Data Quality due to

Data Veracity

latency/multi-response metrology-WAT data

25

WAT

(Current, Voltage, Resistance)

Inline

(Thickness, Critical Dimension)

Data Variety: High-dimensional,

Multi-collinear, Imbalanced Big Data

second minute hour day week month Frequency

Aggregated Detail Equipment data WAT data CP data Production data Metrology data Defect data Granularity Backward diagnosis & trouble shooting

For-ward prediction & process control

Time series

Fab Cycle Prediction and Reduction

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Source Drain Critical dimension (CD) -> 20nm > 30 layers 31

Overlay errors

N+1 layer

N layer

Light source Reticle Lens Wafer X d x d Y d y d ) , (dxX dyY X Y 33

Dynamic backups to consider

both quality and productivity

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x1 y2

x2 y1

Proposed R2R control block diagram

for overlay error compensation

 Step1. Overlay process modeling for R2R control  Step2. DAPI controller design

Empirical

Study

Lot sequense EWMA DAPI

‐0.03 ‐0.02 ‐0.01 0 0.01 0.02 0.03 1 4 7 _{10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97} 10 0 10 3 10 6 T_x+X output

Lot sequence  _{EWMA DAPI}

无论输出值或投入 值，DAPI的变异均 较小！

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ECD control enhancement Problem

definition

Correlation analysis for identifying influence factors Data collection & integration Data cleaning & pre-inspection

Least square estimation Linear model for modeling etching bias

Dissimilarity measurement Dispatching rule simulation

Performance tracing & discussion Data preparation Implementation Is fitness good enough? Model construction Significant? Parameter estimation

Product & chamber effect extraction Is result acceptable? Dispatching rule development Yes Yes Yes No No No 

Problem definition

Data preparation

Model construction

Parameter estimation



Dispatching rule development &

offline validation



Implementation

Advanced Process Control (APC)

for CD control

Metrology

feedforward

feedback

Metrology

feedback

DCD

ECD

Photo

Etching

controller

controller

controller

APC Methodologies: • Run-to-run control

• Fault detection and classification (FDC) • Virtual metrology

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

real-time tool assignment and production control.

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Modeling Etch Bias and Estimating

Product/Chamber Effects



Etch bias = ECD – DCD



Linear regression model is used to model Etch bias and

estimate the product and chamber effects



some of considered parameters

42 1 1). or (0 iable binary var are and . ,..., 1 , 1 chamber 1 Prod chamber Prod 1 chamber chamber 1 Prod Prod       









c

c

p

chamber

for

effect

chamber

of

error

standard

:

chamber

for

effect

chamber

:

Product

for

effect

Product

:

target)

DCD

target

ECD

ˆ

(idealy,

bias

mean

:













Chamber dissimilarity

measurement



Chamber effect target

• chamber effect target is that the chamber effect c meeting this

target via the best process chamber for lot i.



Dissimilarity for each etch chamber

• 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.

43

.

,...,

1

,

ˆ

ˆ

ECD_Target

n

i

x

DCD

T





















.

,...,

1

;

,...

1

,

ˆ

ˆ

C

c

n

i

T

d













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.

Product

Before implementation After implementation

Cpkimprovement Number of lot RMSE Standard deviation Cpk Number of lot RMSE Standard deviation Cpk A 163 0.0075 0.0073 2.34 140 0.0063 0.0061 2.84 21.39% B 100 0.0103 0.0068 2.56 108 0.0101 0.0062 2.82 10.12% C 98 0.0073 0.0073 2.25 136 0.0066 0.0066 2.51 11.28% D 239 0.0084 0.0084 1.61 493 0.0058 0.0058 2.40 48.87% E 105 0.0108 0.0096 1.90 156 0.0080 0.0079 2.49 30.95% F 215 0.0099 0.0083 2.19 274 0.0069 0.0072 2.72 24.30% G 183 0.0091 0.0085 2.22 219 0.0083 0.0085 2.35 5.56% H 224 0.0090 0.0086 2.23 370 0.0076 0.0074 2.80 25.69% 13.03% 3.73% 4.03% 7.85% 7.79% 7.37% 10.26% 11.65% 11.59% 22.69% 0% 5% 10% 15% 20% 25% 10 20 30 40 50 60 70 80 90 100 Fr e q ue nc y Tool score interval Average Cpk: 2.42 Average Cpk: 2.83

Dynamic Decisions for

site-imbalances IC final testing

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Fundamental Objective Hierarchy

and Means Objective Network

Trade-off among alternatives

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From Influence Diagram to

Decision Tree

Lot-End

Shut Down For Repair Close Low Yield Site(s) Shut Down? Close Site(s)? Yes No Continue Repair? Yes No No Complete Repair Yes Continue Test 49

Decisions in the Decision Flow

Site ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Estimated Load 0 13 13 13 13 13 0 13 13 13 13 13 13 13 13 13 Estimated Pass Real Yield 0.00 12.54 9.70 10.66 11.16 10.11 0.00 12.04 11.13 12.04 11.20 11.13 9.80 10.59 10.88 10.11 Site ID 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Estimated Load 13 13 13 13 13 13 13 13 13 13 13 13 13 12 12 12 Estimated Pass Real Yield 9.58 10.26 10.26 9.58 11.12 10.95 10.26 9.79 10.95 10.95 13.00 10.83 10.18 8.67 9.33 9.76

 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: 318.54 units/ Testing Time: 13 time units

• p.s. ART (allowable repair time) = 8.45 minutes and thus should not shut down for repair

Validation of Empirical Study

Apple + IBM to empower business

analytics, optimization & decision

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Thank you very much

for your kind attentions!!!