Wednesday, November 14, 2018
Session

Smart Manufacturing

Chair Michael Arnold, Managing Director, PEER Group GmbH
Michael Arnold

Michael Arnold
Managing Director
PEER Group GmbH

Michael Arnold

Biography
Dr. Michael Arnold, Managing Director, has been responsible for PEER Group´s European operations since 2003. He established a strong services position in the global semiconductor manufacturing market. Michael is the account manager for several of PEER Group’s top customers in Europe. He has served as a member of the SEMICON Europe technical program committee since 2009 and currently chairs the European chapter of the SEMI Smart Manufacturing Technology Community. In 2017, Silicon Saxony appointed Michael as a Board Member. Prior to joining PEER Group, Michael was the operations manager of TRW’s European Manufacturing Solution Business Unit in Dresden, Germany, where he was also responsible for service delivery. Before this he worked for a variety of companies, developing software solutions and implementing industrial vision systems and factory automation solutions for European productions sites. Michael holds a Diploma degree in Physics and a Ph.D. from the Friedrich-Schiller University Jena.

11:45
Industry 4.0 driving business model transformation in Advanced Manufacturing Facilities
  Barry Kennedy, CEO, Irish Manufacturing Research
Industry 4.0 driving business model transformation in Advanced Manufacturing Facilities
Barry Kennedy

Barry Kennedy
CEO
Irish Manufacturing Research

Barry Kennedy

Abstract
Though the semi-conductor industry can rightly claim to have led the charge in the development and implementation of the technologies driving the Industry 4.0 revolution, it is now seeing wider affects happening with new business model transformation and emerging manufacturing technologies transforming the ways businesses are managed and run. This is driven by the products the Semiconductor industry make and the relentless drive to deliver these products with more capability and less cost allowing the wider manufacturing base to design these technologies into their manufacturing plants. Are these emerging technologies now being used back in semiconductor facilities or is there opportunities to engage with them. This paper will put a mirror onto the industry.

Biography
Barry is currently CEO of Irish Manufacturing Research, Irelands leading cutting edge industrially focused research centre for advanced manufacturing. Barry qualified with an MSc from University of Dublin, Trinity College Dublin in 1996. He has worked as New Business and Strategic Program director for Ireland Fab Operations in Intel. Prior to this he has held many senior management roles in Failure Analysis, Process Integration, Device, Process Control and Statistics, Yield Analysis, Quality and Reliability, Yield Q&R. He started his career in Intel working in many senior engineering roles as senior Process Integration and Failure Analysis engineer. Barry has worked in a research environment in Trinity College Dublin for 10 years before commencing work with Intel.

12:10
Yield & Process Improvement by Data Analysis along the Supply Chain
  Roberto Rapp, Director of Manufacturing Process Integration, Deputy Technical Plant Manager/Reutlingen, Robert Bosch GmbH
Yield & Process Improvement by Data Analysis along the Supply Chain
Roberto Rapp

Roberto Rapp
Director of Manufacturing Process Integration, Deputy Technical Plant Manager/Reutlingen
Robert Bosch GmbH

Roberto Rapp

Abstract
Along the semiconductor supply chain an increasing amount of production data are generated and are waiting to be used for continuous process-, yield- and quality-improvements. To classic SPC and data analysis, done by engineers, a variety of new applications were added over the years. Keywords are FDC, APC, ADC, data lakes, data mining - just to name a few. Looking at the supply chain from wafer start up to electrical wafer test (EWS) traceability is a given. Other than ASICs many MEMS elements can’t remember their origin, like wafer number or x/y position. So it is important to make use of data from different steps in production. In this presentation examples for automatic analysis of inline defectivity and EWS are shown. Also the successful implementation of traceability, from final product test of MEMS sensors to inline production data.

Biography
Roberto Rapp has been employed at Robert Bosch GmbH since 2009 as Director of Manufacturing Process Integration, where he is responsible for the implementation and production of the ASIC, Sensor and Power technologies. In parallel he is deputy technical plant manager of the Bosch Semiconductor Plant in Reutlingen, Germany. He studied physics at the University in Heidelberg, Germany and joined IBM in 1985, starting his professional career as Process Engineer in the Wafer-Fab in Böblingen, Germany. 1988 he worked in the first 200mm IBM Wafer-Fab in Burlington VT, USA. Working for IBM, SMST, Philips and NXP he has now over 30 years of experience in the semiconductor industry for consumer and automotive. Since 1995 he held different management positions in Engineering, Quality and Production.

12:35
Predictive maintenance for plasma tools
  Michael Klick, CEO, Plasmetrex GmbH
Predictive maintenance for plasma tools
Michael Klick

Michael Klick
CEO
Plasmetrex GmbH

Michael Klick

Abstract
Plasma processes are widely used in the semiconductor industry, they are completely distinct from mechanical manufacturing. Plasma processes are running in vacuum chambers and there are opened every month or quarter for maintenance. Each maintenance measure at a production chamber causes costs in the order of some 10 k€. Therefore, the prediction of the right time for maintenance can reduce manufacturing costs dramatically. On the other hand, plasma processes are usually treated as black box due to their complexity. All important process parameter as uniformity, rate, selectivity, and stability depend of the plasma’s parameters as flux of ions and reactive species. Thus, the main peculiarity of plasma processes can be compressed is one sentence: ‘The plasma is the tool’. Beyond this we have to take into account that plasmas can run in different modes, can oscillate, cause breakdowns at the chamber wall and depend on the state of the chamber wall. In particular the chamber wall changes its surface properties by the deposition of byproducts. So the only realistic approach for the predictive maintenance for plasma tools must be based on the plasma’s properties. It will be shown how plasma parameter can describe plasma and so also the effective chamber state, chamber differences and show undesired instabilities as arcing and wear of chamber parts. The early detection of changes and undesired effects are here the key for predictive maintenance. Examples show the early detection of process faults, real-time process characterization, and preconditions and methods for chamber matching.

Biography
Objective: CEO, Plasmetrex GmbH Education: Ph.D. in Plasma Physics, Ernst-Moritz-Arndt-University Greifswald, 1992 Dipl.-Ing., Technology of Electronic Devices, 1987 Grants and Fellowships: Lecturer at Ruhr University Bochum since 2010: Plasma technology for semiconductor, MEMS, and PV applications Research and manufacturing skills: Plasma etch process development Nonlinear Modeling of industrial RF Plasmas Development of Plasma Sensor Systems for Etch and Deposition 20 years experience in joined projects with semiconductor fabs

13:00 TBA
  Günter Leditzky, Project Manager Waferfab, ams AG
13:25 TBA
  Cassandra Pillay, Climate Change Expert, UNIDO
13:50 End