Determining golden process routes in semiconductor manufacturing process for yield management

Lee, Chang-Ho; Lee, Dong-Hee; Bae, Young-Mok; Kim, Kwang-Jae

doi:10.1109/ieem.2017.8290315

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Some or all of these values have been used in different measures in ML/AI training systems to predict wafer yield. A number of authors [5][6][7][8][9][10][11][12] have studied wafer yield prediction with classification supervised learning. The differences between their approaches are shown in Table 1.…”

Section: Used Methods For Yield Predictionmentioning

confidence: 99%

“…Stich and co-authors 10 wrote a conceptual paper of yield prediction with a cascading classification learning method on the parameter data. Lee et al 5 described in two approaches for controlling the yield. In the first method, one finds the optimal process parameters to increase the yield.…”

Section: Used Methods For Yield Predictionmentioning

confidence: 99%

“…In this review, we have a closer look on the potentials and pitfalls to predict or enhance the yield in semiconductor manufacturing. Lee et al 5 describe in two approaches how to control the yield in their conceptual paper. In their first method, an attempt is made to find the optimal process parameters to increase the yield.…”

Section: Introductionmentioning

confidence: 99%

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Wafer yield prediction using AI: potentials and pitfalls

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Metrology, Inspection, and Process Control XXXVII

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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.

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