KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH was founded in 2006. Today it is a well-established competence center with 44 employees (06/2016). The main focus is on power electronics reliability, materials research, modeling and simulation.

KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH is a legally independent subsidiary, 100% owned by Infineon Technologies Austria AG. KAI is a well-established industrial research center with a large national and international network of partners. Besides core competences in the area of power electronics reliability, test concept and methodology development, advanced semiconductor materials research, Bayesian statistical lifetime modelling and multi-physics FEM simulation, KAI maintains a well-equipped electronics design laboratory, dedicated measurement equipment for materials characterization as well as state of the art simulation computing resources, complemented by a long proven experience in the coordination of interdisciplinary research projects.

KAI is a well-established industrial research center with a large national and international network of partners.

Role

Since 2008, KAI develops and applies advanced statistical methods for manufacturing data to improve semiconductor reliability. Currently, the statistical methods and degradation modeling research group at KAI uses data mining concepts to frequently check the quality of end-of-life data and develops screening approaches to identify risk devices within wafer test data. With the tasks defined in SemI40, KAI will go one step further towards a mathematical/statistical description of the semiconductor manufacturing process by combining data from the manufacturing processes and analyzing them with advanced statistical methods.

Key contribution

Today semiconductor manufacturing processes are controlled via local monitoring of process and equipment parameters. In case of serious consequences, e.g. yield loss, wafer scraps and customer returns, root causes are identified with retrograde analysis and process control parameters are adapted. In the current situation, an early warning system would be needed to increase the quality in the fab. Within SemI40, KAI will develop advanced statistical methods to identify patterns in wafer test data and link them to data from individual process steps. The target is to achieve a reasoning algorithm, which provides health factors for wafers or lots. This information will then be passed on to product and quality engineering for decision making support and for the subsequent improvement of equipment and process control.