Rainer Hohenhoff & Felix Prischenk | BMW Group more...
"Product data and Product life cycle management in the face of new business models of the automotive industry"
Roman Kern | KNOW-CENTER GmbH more...
"Possibilities and Challenges of Digitalisation in the Semiconductor and Other Domains"
James Moyne | Applied Materials more...
"The Role of APC and Smart Manufacturing/Industrie 4.0 in New Reliability-Critical Markets Such Automotive"
Rainer Hohenhoff & Felix Prischenk | BMW Group
"Product data and Product life cycle management in the face of new business models of the automotive industry"
The requirements for future mobility are significantly influenced by four central directions of impact (autonomous driving, connectivity, electrification, sharing).
To ensure long-term competitiveness, it is essential to systematically implement the resulting innovative business models.
At the same time, the “ecosystem customer", associated with the increasing digitization of the products, requires a significantly increased documentation depth and data quality.
An efficient product data management is a central enabler to meet these requirements. Therefore, in the context of digitization it is necessary to establish a consistent end-to-end product data management throughout the entire product life cycle.
Curricula Vitae: Rainer Hohenhoff
Initiative integrated product data management(iPDM)
Head of digitalization strategy
- 2000–2017: Several business and IT functions at BMW Group
- 1996 –2000: System engineer gedasand gedastelematics
- Study of Mathematics and Computer Science
Curricula Vitae: Felix Prischenk
Initiative integrated product data management (iPDM)
Young Professional digitalizationstrategy
- since 2017: BMW Group
- M.Sc.Business Studies, Operations& Logistics Management
Roman Kern | KNOW-CENTER GmbH
Possibilities and Challenges of Digitalisation in the Semiconductor and Other Domains
These days many buzzwords grab the attention of a wider audience: data science, digitalisation, digital twin, big data, machine learning and artificial intelligence. In this talk these concepts will be briefly introduced and how they relate to each other. In a number of exemplary projects the possibilities of such technologies will be demonstrated - from the semiconductor domain, but also from other domains, like car manufacturing. But also the pre-requisites and, equally important, the limitations of these technology will be mentioned to give a realistic picture of today's technological landscape.
- Currently: Area Head @ Know-Center
- Currently: Postdoc @ Institute of Interactive Systems and Data Science @ Graz University of Technology
- Past: Visiting Researcher @ Mendeley (London, UK)
- Past: Researcher @ Hyperwave
- Past: Project manager & Software architect @ Daimler
James Moyne | Applied Materials
The Role of APC and Smart Manufacturing/Industrie 4.0 in New Reliability-Critical Markets Such Automotive
APC will continue to play an increasingly important role in microelectronics manufacturing, especially as we employ tenets of Smart manufacturing (SM) / Industry 4.0 such as predictive operations, digital twin and supply chain integration [1, 2]. APC will be especially critical in markets where SM is needed to achieve reliability levels required by the customer . A prime example here is automotive where electronics is identified as the primary source of 0Km (non-driving) failures. For example J.D. Power reports that “Audio/Communication/Entertainment/Navigation (ACEN) remains the most problematic category for new-vehicle owners” . These failures are due in large part to latent reliability issues rather than yield loss discovered at the fab. More generally, these issues arise because the focus of quality, throughput and cost continues to be managed largely within the “four walls” of the fab rather than across the entire manufacturing supply chain ecosystem. Addressing issues such as latent failures at the customer site will require that we understand how quality will be assessed and managed across the ecosystem, and how APC can evolve to meet these new or more stringent and complex demands.
SEMI defines APC as “...the manufacturing discipline for applying control strategies and/or employing analysis and computation mechanisms to recommend optimized machine settings and detect faults and determine their cause.” Thus, both run-to-run (R2R) control and fault detection and classification (FDC) technologies are considered part of the APC family . As we explore the tenets of SM, both R2R control and FDC surface as key components of the SM movement. Each enables aspects of SM, but the individual core technologies benefit from that same movement to provide “smart” APC solutions.
From the perspective of SM, R2R control can be considered part of the SM “digital twin” (DT) tenet. According to Wikipedia “A digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes.”  The pervasiveness of DT in microelectronics manufacturing is illustrated in Figure 1 . R2R control is a process DT because it typically uses a model of a process, updated on a run-to-run basis, to improve process capability. Given the pervasiveness of R2R control in microelectronics, we can say that the microelectronics industry is already successfully employing DT components fab-wide. FDC is typically used to create actionable information from trace data collected from equipment. It is a key component of the analytical base in today’s fabs and is employed fab-wide. From an SM perspective, as shown in Figure 1 FDC and the FDC data collection infrastructure are key components of predictive solutions ranging from predictive maintenance (PdM) to virtual metrology and yield prediction; they serve as inputs to these solutions but also complement their operation to provide a comprehensive evolving solution that the International Roadmap for Devices and Systems (IRDS) terms as “Augmenting Reactive with Predictive” [2, 3].
As we begin to embrace SM strategies and environments more fully, R2R control and FDC solutions will become more precise, robust (from a control system definition perspective) and granular, and will be more integrated with each other (e.g., for improved chamber matching), as well as with other DT components (e.g., with scheduling and dispatch DT models for improved yield-throughput optimization of process flows) . The precision will be tailored more to enhance and support prediction solutions, but also to improve fundamental R2R control and FDC capabilities. The roles of APC will also be expanded to more domains within the fab but will also be utilized across the ecosystem (supply chain). As an example of in-fab role expansion, R2R control has already been expanded to multi-process control and is being investigated for front-end yield/throughput optimization and control as well as conditional maintenance control and maintenance recovery (re-qualification) . As an example of expansion to the ecosystem, traceability and control will eventually be leveraged across the supply chain to control and optimize quality per end customer requirements, especially in reliability critical markets such as automotive.
The expanded role of “Smart” APC will present a number of technical challenges across the manufacturing eco-system that will impede the adoption of SM capabilities. Foremost among these are (1) methods for structured incorporation of subject matter expertise (SME) and (2) data partitioning and IP security . Despite the perceived trend towards data driven advanced analytics, the increased precision required for APC solutions spanning larger and more complex domains will require an even larger emphasis on SME and combining SME with analytics . Ultimately the SME-to-analytics interaction must become more structured and more automated . This improvement in SME use will help facilitate improved methods for data partitioning and IP security across the ecosystem, which is often cited as the greatest impediment to the adoption of SM capabilities [2, 9].
There is a growing literature base dedicated to the expanded role of APC in smart manufacturing. The APC conference is Europe, USA and Asia all have had APC smart manufacturing APC case studies over the past year (for example see ). One result highlighted here arose from a collaboration among a number of companies at the Integrated Measurement Association (IMA) APC Council 2018 . At that meeting the results of a survey on the role of SME in semiconductor manufacturing APC analytics was discussed. Key consensus results include (1) SME is required in effective fab analytics; and (2) SME needs to be mapped into the project plan and/or workflow of events. Figure 2 provides an illustration of the latter point. Note that that there are different types of SME that have to work together with analytics in an effective application and the interplay is structured and automated.
In this presentation the expanding role of APC in the manufacturing ecosystem will be presented including case study examples that illustrate how APC will help us address the needs of new reliability-critical markets such as automotive.
 M. Armacost, M. Li and J. Moyne, “Moving Towards Smart Manufacturing in Microelectronics Manufacturing,” Nanochip Fab Solution , Vol. 12, No. 2, December 2017. Available on-line:
 International Roadmap for Devices and Systems (IRDS): 2018 edition. Available online: irds.ieee.org. See especially Factory Integration chapter.
 J. Moyne and S. Banna, “Beyond traditional process control: APC in smart manufacturing,” e-Manufacturing & Design Collaboration Symposium, Taiwan, September 2018.
 “New-Vehicle Initial Quality Improves Again, J.D. Power Finds,”. Available at www.jdpower.com/business/press-releases/2018-us-initial-quality-study-iqs.
 SEMI E133: Specification for Automated process Control Systems Interface, Semiconductor Equipment and Materials International, October 2014. Available via: www.semi.org/en/Standards.
 Wikipedia: Digital Twin. Available online: en.wikipedia.org/wiki/Digital_twin.
 J. Moyne and J. Iskandar, “Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing,” Processes Journal, Vol. 5, No. 3, July 2017. Available online: www.mdpi.com/2227-9717/5/3/39/htm.
 IMA-APC Council Report-out: 2018 Meeting on Data-driven versus subject-matter-expertise (SME) enhanced modeling for APC, APC Conference XXX, Austin, Texas, October 2018. Contact James Moyne (email@example.com) for a copy.
 J. Moyne, S. Mashiro, J. Moyne, “Determining a Security Roadmap for the Microelectronics Industry,” Proceedings of the 28th Annual Advanced Semiconductor Manufacturing Conference (ASMC 2018), Saratoga Springs, New York, May 2018.
James Moyne is a Consultant for Standards and Technology in the Applied Global Services group at Applied Materials. He received his Ph.D. degree from the University of Michigan, where he is currently an Associate Research Scientist in the Department of Mechanical Engineering. Dr. Moyne has experience in advanced process control, and smart manufacturing / Industry 4.0 topics, focusing on analytics; he is the author of a number of refereed publications in each of these areas. He holds a number of patents on prediction and software control technologies, is co-author of Run-to-run Control in Semiconductor Manufacturing, and has co-authored SEMI (semiconductor manufacturing) standards in the areas of process control (including E133 and E126), sensor bus (E54), and big data (E148-Time Synchronization and E160-Data Quality). James is currently co-chair of the Factory Integration Thrust of the International Roadmap for Devices and Systems (IRDS), co-chair of the North America SEMI Information and Control Committee, and technical chair of the APC Conference XXXI to be held this October in the USA.