Data-Driven Adaptive Virtual Metrology for Yield Prediction in Multibatch Wafers

Xu, Hongwei; Qin, Wei; Lv, Youlong; Zhang, Jie

doi:10.1109/tii.2022.3162268

Cited by 21 publications

(4 citation statements)

References 29 publications

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“…A lot of papers, 2,4,6,11,[13][14][15][16][17][18][19][20][21][22][23] however, investigate yield prediction with supervised regression models. They are summarised in Table 2.…”

Section: Regression Modelsmentioning

confidence: 99%

“…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%

“…A small number of authors 13,14,23,25 worked on unsupervised learning methods for the wafer yield prediction. While Jiang et al 13,14,25 used unsupervised learning to find suitable classes, they predicted yield with supervised learning.…”

Section: Unsupervised Learning Modelsmentioning

confidence: 99%

“…While Jiang et al 13,14,25 used unsupervised learning to find suitable classes, they predicted yield with supervised learning. Xu et al 23 used unsupervised learning to find the best parameters for prediction. In a second step, they predicted the wafer yield with a Multitask Learning Deep Belief Network(MLDBN).…”

Section: Unsupervised Learning Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Wafer yield prediction using AI: potentials and pitfalls

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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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“…A lot of papers, 2,4,6,11,[13][14][15][16][17][18][19][20][21][22][23] however, investigate yield prediction with supervised regression models. They are summarised in Table 2.…”

Section: Regression Modelsmentioning

confidence: 99%