Merck KGaA · XETR:MRK
Merck KGaA — Merck KGaA, Darmstadt, Germany — Technical Presentation (February 2022)
29 slides from Merck KGaA's technical presentation, Feb 15, 2022
Slide 1: Scaling Digital Solutions: Core to Semiconductor Innovation
SCALING DIGITAL SOLUTIONS: CORE TO SEMICONDUCTOR INNOVATION Kutup Kurt, Head of Operations of Digital Solutions Semiconductor Materials Webinar Series Feb 15, 2022
Slide 2: The Electronics business operates as EMD Electronics in the U.S. and Canada
The Electronics business of Merck KGaA, Darmstadt, Germany operates as EMD Electronics in the U.S. and Canada. 2 Semiconductor Materials Webinar Series - Scaling Digital Solutions | 2022 Feb 15
Slide 3: We are unique
Merck KGaA, Darmstadt, Germany holds the rights to the name and trademark “MERCK” internationally, except for the United States and Canada We are unique Since our founding over 354 years ago, we’ve become truly global with more than 58,000 employees in 66 countries In the U.S. and Canada we operate
Slide 4: Agenda
1 Industry Challenges and Needs Agenda 2 Prerequisites for Execution Plan 3 Learning by Doing: Use Case Examples 3.1 Quality 3.2 R&D 3.3 Supply Chain 4 Setting the Foundation for Scaling 4 Semiconductor Materials Webinar Series - Scaling Digital Solutions | 2022 Feb 15
Slide 5: Quality Control Parameters are Growing Exponentially
In Ecosystem Quality Control Parameters are Growing Exponentially Data complexity “Finding the Needle in the Haystack” If we keep the same quality approach, cost will increase 100,000 Cost of Controlling Parameters 10,000 60,000 # of Parameters CoA 200 2030- Add Interactions 1,000 + Process between
Slide 6: Ecosystem Expectations... are Our Goals
Ecosystem Expectations... Quality R&D Supply Chain 0 24 Impacts Time to Hour ramp Responsiveness Supply Interruptions Learning cycle acceleration for Responses to all Excursions material selection information and data Complaints Fast transition from R&D, to inquiries within 24 hours OO
Slide 7: Scaling Digital Solutions: Where to Start?
Scaling Digital Solutions Where to Start? ASSET LIFECYCLE Production Technology / system / Knowledge Transfer Product / process / Knowledge transfer Pilot Source Production Customer Engineering Laboratory & Construction Process Research development Product development 7 Semiconductor Materials Webin
Slide 8: Prerequisites for a Data-driven Business in Production
Scaling Digital Solutions Prerequisites for a Data-driven Business in Production Process Data mapping mapping Digital assessment Process Raw data material data Roadmap & prioritization Business case Quality Implementation & data revealing of the benefit Platform for value-adding data to accomplish d
Slide 9: Strategy, Not Technology, Drives Digital Transformation
Scaling Digital Solutions Strategy, Not Technology, Drives Digital Transformation [1] Preparation Assessment Roadmap Assessment & & & Business Case Implementation Summary Pre-work Site Visit Prioritization Impact on ecosystem must be considered for “Roadmap and Prioritization” [1] The 2015 Digital B
Slide 10: First Prerequisite: Digital Assessment acc. Digital Plant Maturity Model
Scaling Digital Solutions First Prerequisite: Digital Assessment acc. Digital Plant Maturity Model [1] maturity Level 5 Adaptive Plant Overall Plant Maturity Level scale Level 4 Predictive Plant Level 3 Connected Plant Level 2 Digital Silos 1.4 Level 1 Pre-digital Plant [1] The development of a Digi
Slide 11: Dimensions of Digital Plant Maturity Model
Scaling Digital Solutions Dimensions of Digital Plant Maturity Model [1] Business Capabilities Overview Enabling Capabilities Overview Manufacturing Execution and Process Automation People and Culture 2.2 0.8 Production Lab Execution IT Security Business Planning 2.0 1.5 and Quality and 1.6 0.4 Insi
Slide 12: Second & Third Prerequisites: Process and Data Mapping
Scaling Digital Solutions Second & Third Prerequisites: Process and Data Mapping now Process flow Reaction Separation Intermediate Formulation Final Product Device Man. Process Process Product Process information flow now Identify the pain point in the process and justify the solution (corrective vs
Slide 13: Data Sharing
Core to Semiconductor Innovation Data Sharing
Slide 14: Call to Action: Data Sharing & Collaboration
Call to Action: Data Sharing & Collaboration Creating a Data Ecosystem to Enable End-to-End Advanced Predictive Manufacturing from Material Suppliers to Device Manufacturers Device manufacturers (n) Sub-suppliers (n-2) Suppliers (n-1) Enabling Capabilities: ✓ Predictive Models ✓ Pareto of Critical P
Slide 15: Data-driven Innovation from R&D to Material Quality
Data-driven Innovation from R&D to Material Quality Paradigm Shift from Conventional CoA to Predictive Quality Control Process & raw material lot-to-lot variation impact material performance but they are not visible on the CoA. Assay Success criteria Standard Metals Oxygen- 1 Reduced variations 2 Co
Slide 16: Use Case Examples
Learning by Doing Use Case Examples
Slide 17: Recap: Scaling Digital Solutions in the Light of Data Sharing & Collaboration
Recap: Scaling Digital Solutions in the Light of Data Sharing & Collaboration Identify & Execute Lighthouse Projects to Define Scaling Roadmap Production Technology / system / Knowledge Transfer Product / process / Knowledge transfer Process Data mapping mapping Pilot Digital Raw assessment material
Slide 18: Quality Use Case 1: Spin-on Dielectrics Application
Quality Use Case 1: Spin-on Dielectrics Application Root-cause Analysis & Improving Customer’s Performance Objective Project Work • Employ data analytics to quickly resolve a quality excursion • A multifunctional Data Exchange Advanced Analytics team would use between customer & we • 2 learning cycl
Slide 19: Quality Use Case 2: Predictive Quality Control for Device Manufacturers
Quality Use Case 2: Predictive Quality Control for Device Manufacturers step 1 Develop multivariate models to predict Customer’s performance Our data Customer Multi-variate Predicted -X data - Y modeling customer data step 2 Identify the variables that most impact Develop advanced process monitoring
Slide 20: Proactive Materials Quality Control with Hybrid Process Control Systems
Proactive Materials Quality Control with Hybrid Process Control Systems Ensuring Materials Quality in Real-time for Semiconductor Customers Quality goes Quality kept in out of spec spec Critical Quality Attribute Temperature Temperature Perturbation perturbation Disturbance Control Input 1 Steady Co
Slide 21: R&D Use Case 1: Learning Cycle Acceleration in Planarization
R&D Use Case 1: Learning Cycle Acceleration in Planarization • Large Design Space • 59 days on CMP tool with data collected on a total of 509 slurries over two-year period • 47 different additives and 19 different types of abrasive particles tested in various combinations • With a large test-matrix
Slide 22: R&D Use Case 2: Data Analytics for Problem Solving in Thin Films
R&D Use Case 2: Data Analytics for Problem Solving in Thin Films Objective Project Work • Data Analytics was employed to quickly resolve product ramp challenges Data exchange Advanced analytics Scientific justification Dimensionality reduction strategies by product experts to • A multifunctional tea
Slide 23: Supply Chain Use Case: Raw Material Review Board (RMRB)
Supply Chain Use Case Raw Material Review Board (RMRB) 1. Quality of raw material inputs has significant impact on quality of finished goods. 2. Finished goods CoA parameters can be predicted prior to manufacturing based on material targets and internal analysis of raw material QC parameters. 3. Thi
Slide 24: Supply Chain Use Case: Raw Material Review Board
Supply Chain Use Case Raw Material Review Board 24 Semiconductor Materials Webinar Series - Scaling Digital Solutions | 2022 Feb 15
Slide 25: Workflow for End-to-End Data Management in Semiconductor Ecosystem
Workflow for End-to-End Data Management in Semiconductor Ecosystem Example from Quality for Preventive Control Strategy step 1 step 2 step 3 SAP Develop predictive models to qualify a new batch for customer’s fab performance Assess all candidate batches Compile supplier´ s & customer's data step 4 V
Slide 26: Setting the Foundation for Scaling
Setting the Foundation for Scaling Idea to Execution
Slide 27: Our Vision and Our Lighthouse
Our lighthouse Provide Our Drive data-sharing best-in-class projects semiconductor solutions by Vision Plan setting while leveraging data in quality, supply chain, and R&D the foundation to shape the industry and in data automation, analytics and be the preferred partner. capabilities Supply We give
Slide 28: Thank you
Thank you Acknowledgements • Business Fields of Semiconductor Materials • Operations Teams of Digital Solutions Kutup Kurt Email: kutup.kurt@emdgroup.com Office: +49 6151 72 58802 Mobile: +49 1511 45 47583
Slide 29: Ideas for future webinars
If you have ideas for future webinars or if you are interested in specific topics, please scan the QR code below and email us. The vibrant M, AZ, Intermolecular, Versum and ACT are trademarks of Merck KGaA, Darmstadt, Germany or its affiliates. All other trademarks are the property of their respecti
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