Yield prediction is highly beneficial in semiconductor manufacturing and hence has attracted significant research interest. A number of recent techniques to do so have utilized machine-learning techniques to improve prediction accuracy. However, the diversity of algorithms used in the wide application domains of semiconductors fabrication make it difficult to compare them as well as to identify key models to use in future. In this paper, we navigate this diversity by conducting an extensive literature research with the aspects of the different data used and the different algorithms applied. Various ML algorithms have their unique strengths and weaknesses, which makes them differently suited for different aspects of yield prediction. In addition, we also consider whether regression analyses or classification analyses are used to nd potentials and pitfalls in this area. We will analyse the type of data being used and various pre-processing techniques proposed. In addition, we will also analyse the practical implementation of these models. A lot has already been achieved on yield prediction, but every future improvement that enables significant cost saving by making it easier to optimally coordinate the individual processes and perhaps detect errors earlier is more than welcome. This review paper provides a foundation for model and data selection for yield prediction based on current state of the art.
Innovative automotive systems require complex semiconductor devices currently only available in consumer grade quality. The European project TRACE will develop and demonstrate methods, processes, and tools to facilitate usage of Consumer Electronics (CE) components to be deployable more rapidly in the life-critical automotive domain. Consumer electronics increasingly use heterogeneous system integration methods and "More than Moore" technologies, which are capable to combine different circuit domains (Analog, Digital, RF, MEMS) and which are integrated within SiP or 3D stacks. Making these technologies or at least some of the process steps available under automotive electronics requirements is an important goal to keep pace with the growing demand for information processing within cars. The approach presented in this paper aims at a technology management and recommendation system that covers technology data, functional and non-functional constraints, and application scenarios, and that will comprehend test planning and cost consideration capabilities.
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