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MADEin4 Metrology Advances for Digitized ECS (Semiconductor and Automotive) Industry 4.0

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M ADEin4

Metrology Advances for Digitized ECS

(Semiconductor and Automotive) Industry 4.0

Ilan Englard

Applied Materials Israel December 7 th 2020

European Collaborations Driving Smart Manufacturing Excellence Webinar

(2)

A GEND A

Automotive and Semiconductor domains analogy Objective and Industry 4.0 boosters

Summary and Outlook

MADEin4 Project Essentials

Automotive domain use cases

Data is the new oil: design, modeling, metrology and ML Context creation

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(3)

A GEND A

Objective and Industry 4.0 boosters MADEin4 Project Essentials

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(4)

MADEIn4 project essentials

• Number of consortium members: 47

• Countries involved: 10

• Start date: April 1, 2019

• Duration: 36 months

• Total effort: person.months: 10,503 (875 person.years)

• Total H2020 eligible costs: € 126,176,472.50

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(5)

MADEIn4 project essentials

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(6)

Develop and qualify new productivity boosters:

• Booster 1: High-productivity metrology and inspection tools for semiconductor and automotive industry

• Booster 2: Ready for “industry 4.0” Cyber Physical Systems (CPS):

• Higher data rates and smart acquisition and processing

• Smart use of data to improve the over-all productivity and predictability

Objective and Industry 4.0 boosters

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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A GEND A

Semiconductor and Automotive domains analogy

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(8)

Semiconductor and Automotive domains analogy

From: reactive manufacturing to predictive manufacturing

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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Semiconductor and Automotive domains analogy

Litho/

SEM

Etch/

SEM

CMP/

Reflectometry

Doors welding/

Optical inspection

Painting/

Optical inspection

Engines Assembly/

EOL hot test

… …

Number of measurements per wafer 10

3

Wafers per month 10

5

Number different products 10

2

< x < 10

3

Highly automated manufacturing

Number of inputs per unit process (features) 10

2

Manufacturing process longevity much less than 10

1

years

Number of measurements per car >>10

3

Cars per month 10

5

Number of different configurations 10

2

< x < 10

3

Highly automated manufacturing Number of inputs per unit

process (features) 10

2

Manufacturing process

continuously under improvement and changes

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Semiconductor Automotive

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Semiconductor and Automotive domains analogy

The Semiconductor and Automotive industries are sharing similar design and manufacturing flows and differ by the content of each of the design and manufacturing modules

This allows to develop innovative shared machine learning based methodologies which will enable the transformation of the manufacturing from reactive to predictive

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(11)

A GEND A

Data is the new oil: design, modeling, metrology and ML Context creation

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(12)

Data is the new oil

1

Data collected and pushed to the cloud

2

Sensors are added to industrial computers

3

Constant data collection for future possible usage

4

Data analysis by tailor- made algorithms

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(13)

CONTEXT (crossing) the chasm

Data analytics

and Artificial Intelligence

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Cyber-physical Systems (CPS) for Information Creation

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Artificial Intelligence (AI)

• AI: Artificial intelligence making decisions about a system

• ML: Machine learning modeling the behavior of a system

• NN: Neural networks are one implementation of machine learning

• DL: Deep learning is one implementation of Neural networks

AI

ML

Brain inspired

NN

DL

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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Digital twinning: Creating a virtual Representation of the Production Process

Traditional modeling: Physical models requiring little data but deep understanding of the process.

ML-based modeling: “black box” models requiring feature engineering coupled with sensor data.

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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CPS SENSORS: From Silicon wafer to IC Processes and Metrology

700 to 1500 operations for an average CMOS process

• 2-3 months manufacturing time

• 22-50 lithography layers

• 20 Diffusion Ops.

• 23-40 implants Ops.

• 13 DRY Etch Ops

• 78 WET Etch Ops

• 21 Thin-Film (metal) Ops.

• 7 CMP (Chemical-Mechanical-Polish) Ops.

• 240 Metrology Ops.

• 240 Yield Ops.

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: TOWER

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CPS Sensor Pre-Warn

Metrology use case - Metal deposition rate

Early indications of

problems?

Tool is down for service!

Source: TOWER

(19)

Predictive Yield: Feature Engineering

From Feature to Lot level

Different processes require their own level of abstraction to capture specific process behavior

Feature level:

Retargeting Assist features OPC

Mask frame level:

Mask frame assembly

Wafer level:

Metrology Fab Process Tool

Lot level:

Multiple wafers showing similar processing/metrics

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(20)

Predictive Yield

Full process sequence characterization

0.51 R-squared score for predicted vs observed

1.96 MAE (Mean absolute Error) score Input:

Process & Layout Input:

Process

0.47 R-squared score for predicted vs observed

2.09 MAE (Mean absolute Error) score

0.62 R-squared score for predicted vs observed

1.74 MAE (Mean absolute Error) score Input:

Process & Layout &

Previous Measurements (all)

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(21)

Predictive Yield

Digital Twin - Process characterization modeling & optimization

Different designs respond

differently to process conditions They are manually optimized in a time consuming and expensive fashion

Design 1 Design 2 Design 3 Design 4 Design 5

Different Designs

Di ff er en t P roc es s Cond iti ons

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(22)

Predictive Yield

Digital Twin - Design-aware process optimization

Design 1

Design 2

Design 3

Design 4

Design 5 Design 6

Process for new designs can be virtually optimized

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(23)

Predictive Yield

Process Impact Analysis

Shapley analysis show the most impactful process steps are not necessarily the last one in the process sequence and last one to be measured

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(24)

Predictive Yield

Digital Twin Feature Based Yield Prediction

Feature importance assessment to guide correction or define optimal operation

Feature importance

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: MENTOR

(25)

A GEND A

Automotive domain use cases

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(26)

Use Case 1: Cars body line digitization with Pre-Warn scenarios

FCA, NANOMOTION, TOWER, TU DELFT, POLITO

Problem statement: door welding defects rarity

Solution: development of an inline doors inspection Automated Defects Classification (ADC)

Weldspots with LED illumination

3D Process simulation Partners test field

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(27)

Use Case 1: Cars body line digitization with Pre-Warn scenarios

Welding doors inspection and ADC flow

Nanomotion: Camera stabilized gimbal

TOWER: sensor and camera

TUD: Image conditioning

POLITO: Auto Defect classification (ADC) FCA Design of inno va ti ve s ol uti on and in teg ra ti on

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automatic Defects Classification in the Semiconductor domain

• ADC is a central server used for Recipe Creation, Runtime Classification &

Monitoring in semiconductor manufacturing

• ADC is developed in the Semiconductor domain to:

• Help semiconductor manufacturers to increase and maintain IC chip yields

• Monitor whether the process is under control

• Provide high availability (uptime 99.99%)

Where is my DOI* ?

*DOI - Defect Of Interest

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/10/2020

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• As design rule shrinks, defects hide in inspection noise → the amount of manually classified defects is increasing

• Classification can be done faster while taking into account much more attributes than human eyes can

• High consistency can be reached with an Automatic system

Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automatic Defects Classification in the Semiconductor domain

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(30)

Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automotive domain doors inspection and ADC flow overall architecture and processing pipeline

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: FCA

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Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automatic Defects Classification in the Automotive domain

• Preliminary neural networks (NN) tested both on synthetic and real images

• An independent classification (a distinct network) for each soldering point were used

• Two approaches of NN were compared:

• One-class classifiers (unsupervised)

• Uses only ``good’’ samples, Classes are OK/KO

• Traditional multiclass CNN (supervised)

• Classes can be multiple

• Two different architectures of processing options were tested:

- Serial - Parallel

“good” “bad”

Choice will depend on the cost (performance) of the overall pipeline of operations (camera,

cropping, image processing) Speed vs. size of the network

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

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• SUPE

SERIAL P ARA LLEL

SUPERVISED UNSUPERVISED

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automatic Defects Classification in the Automotive domain

(33)

• Exploring ‘SEMI – Automotive’ ADC potential cross fertilization:

• Is there potential value in adapting concepts from SEMI to automotive and vice versa?

• Objectives

• Introduction to SEMI and Automotive ADC concepts, challenges and solutions

• Potential cross fertilization ideas

• Action:

• Map similarities and differences between SEMI ADC and Automotive ADC: needs, constraints, approaches

Use Case 1: Cars body line digitization with Pre-Warn scenarios

Automatic Defects Classification analogy between Automotive and Semiconductor - Outlook

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(34)

Use case 2: End-of-Line Engines Testbenches

AVL, FCA

Problem statement:

End of Line engines tests efficiency:

▪ Conducting End Of Line engines hot testbench is expensive (time, effort, …)

▪ Hot EoL tests are conducted for 5-10 % of all engines

▪ Typically engines are selected randomly, this poses the risk that erroneous engines are overlooked

Solution:

cycle time reduction:

▪ Reduction of false negatives (healthy engines that are qualified as erroneous) through data-driven selection (correlation of after sale, cold and hot test data)

▪ Reduction of false positives (erroneous engines that are qualified as healthy) through predictive warning driven from the cold test bench

Source: AVL List GmbH

12/10/2020 European Collaborations Driving Smart Manufacturing

Excellence Webinar 2020

(35)

Use Case 2: EoL Evaluation of the impact on production tolerances on engine KPIs

Key Performance Indicators Factor Combination

𝜎 Usage

Environment

Aging Tolerances

Consumables

On-Board Diagnostics

Engine Model Virtual ECU*

Emissions CO

2

DoE

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

Source: AVL List GmbH

*ECU: Electronic Control Unit

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Use Case 2: Method EoL Cold/Hot Optimization

(historic data)

ICE*

no.

Ch1 time series …. Ch [30+] time

series

E1 1.2, 1.4, 1.7, .. 1.2, 1.4, 1.7, ..

E2 0.87, 1.2, 1.7, … 0.87, 1.2, 1.7, …

E3 0.27, 0.2, 0.7, … 0.27, 0.2, 0.7, …

..

En

ICE no.

(subset of EoL cold test)

Label (test oracle)

E1 healthy

E2 erroneous

E3 healthy

..

Em erroneous

Model learning: e.g., classification model 𝑌 𝑖 = ℎ 𝑥 𝑖 1 , 𝑥 𝑖 2 , … , 𝑥 𝑖 𝑚 + 𝐸 𝑖

EoL cold tests EoL hot tests

Source: AVL List GmbH

Source: AVL List GmbH

Labels (fault modes)

Quality- Indicators

/ KPIs

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

*Internal Combustion Engine

12/9/2020

(37)

Use Case 2: Method EoL Cold/Hot Optimization

(live data)

EoL cold tests EoL hot tests

ICE no.

Ch1 …. Ch [30+]

E1 1.2, 1.4, 1.7, .. 1.2, 1.4, 1.7, ..

E2 0.87, 1.2, 1.7, … 0.87, 1.2, 1.7, …

E3 0.27, 0.2, 0.7, … 0.27, 0.2, 0.7, …

..

En

ICE no.

False positive/

False negative

E1 healthy

E2 erroneous engines qualified as healthy

E18 healthy

..

Em Healthy engine qualified as erroneous

𝑌

𝑖

= ℎ 𝑥

𝑖1

, 𝑥

𝑖2

, … , 𝑥

𝑖𝑚

+ 𝐸

𝑖

→ prediction

Source: AVL List GmbH

Source: AVL List GmbH

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(38)

Use Case 2: Automotive / Semiconductor Predictive Analysis Analogy

Feature importance

Semiconductors wafers features process prediction Automotive engines features process prediction

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

Digital Twin Digital Twin

12/9/2020

Source: AVL List GmbH Source: MENTOR

(39)

Summary and Outlook

• Data is the new oil and data analytics with artificial intelligence uses context to produce information

• Analogies between the Semiconductors and Automotive domains could enable cross fertilization between the two

• MADEin4 is expected to make a breakthrough for yield prediction and pre-warn scenarios accuracy and cycle time in both the Semiconductors and Automotive domains by:

• Enhanced methodologies, algorithms and hardware such the case of automatic defects classification (ADC) in the production line

• Improved digital twin modeling accuracy with design, process and metrology data from various sources

European Collaborations Driving Smart Manufacturing Excellence Webinar 2020

12/9/2020

(40)

M ADEin4

Thank You For Your Attention

This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement

No 826589. The JU receives support from the European Union’s Horizon 2020 research and innovation

programme and Netherlands, Belgium, Germany, France, Italy, Austria, Hungary, Romania, Sweden and Israel

References

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