Yield Prediction with Machine Learning and Parameter Limits in Semiconductor Production

Busch, Rebecca Saltiel; Wahl, M.; Czerner, Peter; Choubey, Bhaskar

doi:10.1109/issm55802.2022.10027006

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The two examples in her work are based on regression methods. In general, we observed that Random Forest(RF) 6,9,11 and Supported Vector Machine(SVM) 8,9,25 are the most popular methods providing good results.…”

Section: Used Methods For Yield Predictionmentioning

confidence: 99%

“…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%

“…4 In the second method an attempt is made to identify errors by analysing the process and predicting the yields by optimising the process parameters. In earlier research, 6 we had a closer look at the best parameter limits and we predicted the yield with the fault-detection and -classification(FDC) data. In this paper, we first divide the studies in those that use machine learning methods for prediction and those who do not.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wafer yield prediction using AI: potentials and pitfalls

Busch

Wahl

Choubey

2023

Metrology, Inspection, and Process Control XXXVII

View full text Add to dashboard Cite

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.

show abstract

Section: Used Methods For Yield Predictionmentioning

confidence: 99%

Section: Used Methods For Yield Predictionmentioning

confidence: 99%

“…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%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations