Post on 13-Oct-2020
S U M M I TP a r i s
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Industrie 4.0 impacts de l’ IoT, de l’IA et du ML
Xavier DelacourSolutions ArchitectAmazon Web Services
@XavierDelacour
I O T 1 0 2
Xavier MichalletHead of Data
Soitec
@xmichallet
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Opérations 50/mn
Vitesse de la chaine1m/mn
Consommation électrique
2Kw/H
Produit de séries x,y,
Une erreur aujourd’hui
Bras nécessitant une maintenance dans 1000
opérations
Le produit est terminé
Problème de qualité détécté
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L’IoT stimule la croissance du marché industriel
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
La 4ème revolution industrielle
Industrie 1.0
Production mécaniquealimentée par l’eau et la vapeur
Industrie 2.0
Production de masse basée sur la division des taches alimentée par de l’énergie eléctrique
Industrie 3.0
Electronique et Informatique pour pousser l’automatisationde la production
Industrie 4.0
Systèmes de production cyber physiques
18ème Siècle 20ème Siècle 1970s Aujourd’hui
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Les différentes étapes du voyage vers la fabrication numérique
ConnectivitéOpérationautonome
Capacité de prédiction
Que se passe-t-il si ?
et optimisation
Mise en place d’une culture data driven pour passer des operations en reaction à la prédiction
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Quoi et Pourquoi ca se passe ?
“réaction”
Que pourrait-Il arriver ? “simulation”
Qu’est ce qui va arriver ?“prévention”
Comment une réponseautomatique peut-elle –
être obtenue“auto-optimisation”
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Quels sont les facilitateurs techniques de l’industrie 4.0?
Robots autonomes
Simulation
Intégration verticale et horizontale
IoT industriel
CybersecuritéCloud
Fabricationadditive
RéalitéAugmentée
Analytique etBig Data
Le Cloud est une des 9 facilitateurs
Le Cloud est le facilitateur central àtoutes les disciplines et les industries
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3 zones d’application
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Que pouvez vous faire avec l’industrie 4.0
PLCMES
ERP
SCADA PLC
#1 Track and trace
Just in time delivery, condition
monitoring…
#3 Quality
management
Shape/Size/default detection
#4Predicative Failure
#5Predictive
maintenance, digital twin
#2 Monitoring
#2.1Consumable
monitoring (lubricants, filter…)
#2.2Condition
monitoring
#2.3Workers efficiency
and safety
#6Tour de contôle
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Exemple de clients industriels
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1 2 3
Maintenant… Comment faire ?
Exportez vos donnéesvers le cloud & créezun data lake
Tirez partie du ML et de l’IA pour
moderniser votreusine
Analysez et visualisezvos données
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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Envoyez vos données silotées dans votre usine vers un data lake
Pour analyser rapidement quelle valeur peut être générée.
Pour simuler des cas d’utilisation et leur un RoI comme la maintenance prédictive et le contrôle de la qualité afin de choisir lequel implémenter.
Donner un accès centralisé aux différents clients de vos données afin de leur permettre d’innover
Pourquoi ?
DL
Operational Reporting and
Dashboarding UsersAd-hoc/Self-service Users
Data Science, ML/AI Users
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AWS a développé des outils pour faciliter ce transfert
Storage | Archival Storage | Data
Catalog
ISV Connector
Sigfox Lora Connector / API
ʥA
MQTT ʥ
A
AWS a développé des outilspour vous aider à envoyervos données dans votredata lake sans impactervos processus de production:
Connecteurs avec des parntenaires éditeursde logiciel
Adapteur pour protocols industrielsdans Greengrass
Connecteurs avec des protocols de logisitique.
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
• Processez• Enrichissez• Stockeztemporairement• Requêtez
• Aggregation de connectivité• Routez les messages• Déclenchez des actions
locales
Actions
• Stockage long terme• Enrichissement et
calcul complexe
ML : Décision, prédiction, alerte
Process & Compute
User interaction
• Connectez votre objet• Activez l’intelligence locale et
le ML at the edge
Extraire Router Préparer Processer Exploiter
Cycle de vie de la donnée
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Smart Gateway
execution ML message routing
rules engine…
EdgeDevice
Secure messagingBroker, Rules engine, Shadow, Security
Device managementRegistry, OTA updates…
Fleet protection Audit, Alert
Visual design of IoT applications
Process, enrich, temporary store
Clean and enrich data
Store & query data
Data set ready for analytics
Event detection & action
aggregate connectivity, trigger local action
Site monitoring visual tool
Operational Reporting and
Dashboarding Users
Ad-hoc/Self-service Users
Data Science, ML/AI Users
LT store, heavy compute & enrich
ML: Decision, prediction, alerting
ML platform
BI /analytics/ monitoring
Actions
Process & Compute
User interaction
Fonctionnalités AWS supportant ce processus
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AWS IoT Events
Le cercle vertueux de l’IoT
AWS IoT Analytics
AWS IoT SiteWise
AWS IoT Device Management
AWS IoT Things Graph
AWS IoT Core
AWS IoT Device Defender
AWS IoT Device SDK
Amazon FreeRTOS
AWS IoT Device Tester
AWS IoT Greengrass
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Xavier MichalletHead of DataSoitec
@xmichallet
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1st IOT Device @Soitec
Soitec – Who we are
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We design and deliver innovativesubstrates & solutions to enable our customers‘products shaping everyday life
DESIGNER & MANUFACTURER OF INNOVATIVE SEMICONDUCTOR MATERIALS
4Core Technologies
SMART CUT, SMART STACKING, EPITAXY
1100
1200-mm wafer fabs (Bernin I, China (via Simgui))
300-mm wafer fabs (Bernin II, Singapore*)CAPABILITY
Employees WorldwideGLOBAL PRESENCE
23“
”
High-growth MarketsSMARTPHONES, AUTOMOTIVE, CLOUD, IoT
Largest manufacturer of engineered substratesLEADER
*Customer samples: Q3FY19Corporate presentation Soitec Proprietary Copyright @ 2019 Soitec Jan. 2019
Soitec at a glance
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Soitecfoundation
1992 Bernin I Fab
100/200-mm
19992002Bernin II
Fab300-mm
Smart Cut ™Invention
1991Singapore
Fab 300-mm
20082014
Manufacturing Partner in China -
200-mm
2015-16Reinforce core semiconductor
business & Return to profitability
2017Back to profitable
ResultsLong-term supply
agreement signed with GF
〉 Revenue: €311m in 2017-2018
〉 Stock Exchange: Euronext since 1999
〉 Employees: 1100 worldwide, 20 nationalities
〉 Patent portfolio: >3000
〉 R&D spending: ~8% of annual revenue
〉 Worldwide leader in engineered substrates
〉 Capacity:
〉 Business model: wafers, licensing
200-mm – 1.1M wafers/y. (incl. the 50k wafers/y. capacity extension planned in Bernin I during FY’19)
300-mm - 1.6M wafers/y. (incl. the 150k wafers/y. capacity extension planned in Bernin II during FY’19)
2018First 300-mm wafers
being shipped to RF-SOI/ FD-SOI
customers from Singapore
Corporate presentation Soitec Proprietary Copyright @ 2019 Soitec Jan. 2019
Soitec in the value chain
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Smart Cut TM
technology
SOI processed wafer with multiple raw dice per wafer
SOI substrates(Silicon on Insulator)
Silicon die with millions of transistors
SOI transistor
S
Base material
Buried Oxide
DGSi
IC
PCB
Starting wafers
Engineered substrates
Corporate presentation Soitec Proprietary Copyright @ 2019 Soitec Jan. 2019
Our engineered substrates are at the heart of everyday life
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Higher reliability
Longer battery lifeOutstandingPerformance
$$$$
Optimized cost
Cloud infrastructure IoT
Automotive SmartphonesSoitec’s
engineered substrates
Corporate presentation Soitec Proprietary Copyright @ 2019 Soitec Jan. 2019
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Inside our Fabs
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Centralised, consolidated IT
Fully virtualized servers (VMware since 2006)
Cloud First Strategy
All new apps must be in the cloud (Saas or hosted in Public cloud)
Legacy apps migration to be study
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The process equipment is requiring a regulation based on the Atmospheric Pressure
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Project initiated
Get the pressure from the process equipment, perform the regulation inside our manufacturing system
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Pressure information provided by the process equipment is not stable and sensors are not matched
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Option 1
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Option 1
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Option 2
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Data Collection and data storage OK
Expose and Consume Data through API
- IT Security check- Clean Room certificat
Casing / 3D Printer
Acquisition Marathon test in office and in clean room
Experiment
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Partner IT/Manufacturing
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1 Tool Only1 Sensor
⇒ PRODUCT OK !!
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Risk Analysis (Failure Mode and Effects Analysis -FMEA)
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Realability & Availability❖ Deployment of 3 sensors❖ Average of 3 sensors is computed❖ Sensor matching check
Risk around the IoT device
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New device with a screen to enable manual data collection as a workaround.
Risk around connectivity
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Move To Production
❖ Monitoring & SLA Commitment❖ Spares❖ Training for IT & Maintenance teams
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Architecture
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Vizualisation
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Monitoring
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Results 1/3 : Cost Effective
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IoT Devices
20 Eur 40 Eur
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Results 2/3 : Flexible cost
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Results 3/3 : New IT
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Fast : 1 Month Alpha / 2 Month Beta / Total 6 months project with very low workload (24 MD)Scalable : By designSupport Effective : ServerlessOpen : API based
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What’s Next ?
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S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Maintenant que vous avez unebonne vision de ce que vous
pouvez faire, et que vous avezaccès à vos données, quel
problème voulez-vous résoudre ?
aws.amazon.com/IoT
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Merci !
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Xavier DelacourSolutions ArchitectAmazon Web Services
@XavierDelacour
Xavier MichalletHead of Data
Soitec
@xmichallet
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I TS U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.