Yield prediction in semiconductor manufacturing using an AI-based cascading classification system

Stich, Peter; Wahl, M.; Czerner, Peter; Weber, Christian; Fathi, Madjid

doi:10.1109/eit48999.2020.9208250

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Other architectures exist for modeling a multistep process. For example, a predictive modeling conceptual framework using classifiers has been discussed by Stich et al 26 . In the framework from Stich et al.…”

Section: Resultsmentioning

confidence: 99%

“…Other architectures exist for modeling a multistep process. For example, a predictive modeling conceptual framework using classifiers has been discussed by Stich et al 26 In the framework from Stich et al either machine learning or neural net classifiers are used to model yield on a process tool. This proposal also suggests that a cascading classifier approach, for sequential process tools, with feedforward corrections into the process recipe might be achieved.…”

Section: Comparison To Other Prediction Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Halle¹,

Dunn²,

Gabor³

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: Predictive estimates of the final process outcome(s) of multistep, coupled processes can be difficult to make based on data measured at the various process steps. Self-aligned quadruple patterning (SAQP) is an example of such a process where the prediction of pitch-walk is desired at the various process steps.Aims: Be able to both predict pitch-walk values and the uncertainty in the predicted values at SAQP process steps based on optical critical dimension (OCD) spectroscopy outputs (dimensions, angles, thicknesses, and so on) of mandrel, spacer, and other SAQP features.Approach: Train a neural network using OCD-modeled values of an SAQP process to be able to predict SAQP pitch-walk at early process steps. Use Bayesian dropout approximation (BDA), a methodology using Bayesian inference with stochastic neural networks, to estimate uncertainty in the predicted SAQP pitch-walk.Results: Able to predict pitch-walk values, and the uncertainty in the predictions, of the final SAQP structure after the deposition of the first spacer. The pitch-walk predictions become more accurate as OCD information from the bottom mandrel RIE and bottom spacer are added as inputs to the BDA network. Conclusions:In contrast to a single output value that traditional neural networks would predict, BDA makes an estimated distribution of predictions, where the BDA network gives both a most likely value as well as a distribution of potential values. While this paper shows the power of BDA to predict SAQP pitch walk, it is expected that BDA will be a valuable tool to analyze many data sets in semiconductor manufacturing to help improve yield and performance.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison To Other Prediction Approachesmentioning

confidence: 99%

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Halle¹,

Dunn²,

Gabor³

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

View full text Add to dashboard Cite

show abstract

“…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%

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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Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study

et al. 2022

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Yield prediction in semiconductor manufacturing using an AI-based cascading classification system

Cited by 7 publications

References 3 publications

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Wafer yield prediction using AI: potentials and pitfalls

Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study

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