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Finding shortest paths in a graph is relevant for numerous problems in computer vision and graphics, including image segmentation, shape matching, or the computation of geodesic distances on discrete surfaces. Traditionally, the concept of a shortest path is considered for graphs with scalar edge weights, which makes it possible to compute the length of a path by adding up the individual edge weights. Yet, graphs with scalar edge weights are severely limited in their expressivity, since oftentimes edges are used to encode significantly more complex interrelations. In this work we compensate for this modelling limitation and introduce the novel graph-theoretic concept of a shortest path in a graph with matrix-valued edges. To this end, we define a meaningful way for quantifying the path length for matrix-valued edges, and we propose a simple yet effective algorithm to compute the respective shortest path. While our formalism is universal and thus applicable to a wide range of settings in vision, graphics and beyond, we focus on demonstrating its merits in the context of 3D multi-shape analysis., published at 3DV

In this paper, we propose a novel protocol to represent the human factor on a blockchain environment. Our approach allows single or groups of humans to propose data in blocks which cannot be validated automatically but need human knowledge and collaboration to be validated. Only if human-based consensus on the correctness and trustworthiness of the data is reached, the new block is appended to the blockchain. This human approach significantly extends the possibilities of blockchain applications on data types apart from financial transaction data.

Vendor Managed Inventory (VMI) is a mainstream supply chain collaboration model. Measurement approaches defining minimum and maximum inventory levels for avoiding product shortages and over-stocking are rampant. No approach undertakes the responsibility aspect concerning inventory level status, especially in semiconductor industry which is confronted with short product life cycles, long process times, and volatile demand patterns. In this work, a root-cause enabling VMI performance measurement approach to assign responsibilities for poor performance is undertaken. Additionally, a solution methodology based on reinforcement learning is proposed for determining optimal replenishment policy in a VMI setting. Using a simulation model, different demand scenarios are generated based on real data from Infineon Technologies AG and compared on the basis of key performance indicators. Results obtained by the proposed method show improved performance than the current replenishment decisions of the company.

Horizontal and vertical integration rely on flexible interaction, i.e. communication and cooperation, of heterogeneous systems and subsystems on several abstraction layers. A large ontology (the Digital Reference) that describes semiconductor supply chains and supply chains containing semiconductors is needed so as to leverage the highly distributed and decentralized data sources. The Digital Reference Platform Application (DRPA) is a tool designed to explore the power of the Digital Reference Ontology, offering features for ontology exploration and data creation along with Arrowhead Framework integration. With this, the DRPA offers interoperability between arbitrary software applications and industrial cyber-physical systems - integrated into Arrowhead Local Clouds. This approach applied for a voting wristband prototype shows the potential of the DPRA. The final target is to extend the potential of DRPA beyond Ontology exploration.

Within the public-private ECSEL Joint Undertaking, the project Productive4.0 aims for improvements with regard to digitalization for electronic components like semiconductors and systems as key enabling technologies. The semiconductor industry has a high growth potential within the frame of digitalization as manufactured products are intensively used in production and B2B environment. Thus, Supply chains employing and fabricating semiconductors become the core driver and beneficent of digitalization. In order to exploit digitalization potentials, Semantic Web technologies are used to create a digital twin for semiconductor supply chains and supply chains containing semiconductors. Exemplary benefits of this lingua franca which can be understood by humans and machines alike for various applications like simulation, deep learning, security, trust, and automation are shown in this paper.

Semiconductor manufacturing is highly complex, invest intensive and constantly changing due to innovation. The semiconductor product market is highly volatile due to short product life cycles with difficult-to-predict ramps and end-of-life demands. These challenges are mitigated via flexible production capabilities, e.g. dynamic routing or rescheduling, used by planning systems to transfer volatile demands to well-utilized factories. The product structure is one of the keys for enabling the desired result. Product structure representations include linear, tree, and network. In this paper, definitions of several product structure representations are given and hypothesized benefits and drawbacks are discussed. Research questions are posed, current research efforts are introduced, and the hypothesis that a time dependent network-tree representation would be beneficial is postulated. The problem statement is explained by a real case merger where risk and opportunities based on the choice of product structure representation were relevant and no final solution was determined.

The question of the paper is: "How does governmental funded projects influence the demand forecast for semiconductor manufacturing". For Semiconductor Manufacturing an accurate demand is of paramount advantage due to the challenges arising from a mix of high capital cost and short product life cycles. We consider the Bass Model for new technology and product introduction as the most sophisticated model for supporting demand forecast for new products and technologies. In this paper it is described how the boost from governmental funded projects can be parameterized in the Bass Model from a theoretical and practical point of view. From a practical point of view the model is applied for the German funded projects GeniusTex and Productive4.0 on the example of a selected pressure sensor. A perspective is given on how this model can be validated in future research once actual data are available.

Moore's Law indicates that the amount of transistors which can be accommodated on integrated circuit (IC) double every two years in semiconductor manufacturing industry. As a capital intensive and competitive industry, supply chains in the semiconductor industry feature high structural complexity and high demand uncertainty. In order to enhance the effectiveness of the enterprise, total resource management (TRM) for semiconductor industry is increasingly crucial. Past studies focused on measuring the effectiveness of either demand fulfillment or capacity utilization, but little researches have done for investigating the inter-coordination on both aspects. This paper proposes overall supply chain effectiveness (OSCE) for the incorporation of demand planning and capacity portfolio based on PDCCCR framework to evaluate the effectiveness of semiconductor supply chain industry. The proposed index is implemented in an empirical study based on a simulation model for semiconductor backend production. Finally, the paper concludes with the discussion of future research directions.

Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision-making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K-means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.

Enhancing supply chain resilience is of vital importance in today's business to manage and mitigate the risks, especially in the semiconductor industry challenged with intrinsic long cycle times and short product life-cycles. Transferring production from a primary site to an alternative site after a disaster is one of the strategies to ensure resilience of the supply network. In this study, different types of alternative sites with various levels of preparedness are investigated. A discrete-event simulation is used to evaluate their operational and financial impacts under four different disruption scenarios. The simulation outcomes demonstrate unexpected positive benefits of various alternative sites in terms of fast recovery and resilience building.