Pre-Silicon Yield Estimation using Machine Learning Regression

Sandru, Elena-Diana; David, Emilian; Pelz, Georg

doi:10.1109/icecs46596.2019.8964997

Cited by 2 publications

(1 citation statement)

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“…It is interesting to look at the performance of the methods in relation to the underlying data. A number of authors 4,11,13,14,17,19,20,23,26 work with Wafer Test data/ Wafer Acceptance Test(WAT) which is also refered to Process Control Monitoring(PCM) data. Jiang et al 13,14 and Kim 20 et al had results above 0.9 for R 2 values.…”

Section: Regression Modelsmentioning

confidence: 99%

Wafer yield prediction using AI: potentials and pitfalls

Busch

Wahl

Choubey

2023

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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