On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Wan, Jian; Pampuri, Simone; O'Hara, Paul G.; Johnston, Adrian; McLoone, Seán

doi:10.1049/cp.2014.0718

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…However, there is a lot of machine learning algorithms that are commonly used in soft metrology that have not been explored in the case of flow rate estimation, both for linear and not linear regression. Popular linear machine learning approaches in soft metrology are Multiple Linear Regression (MLR) [39] [47] and Gaussian Process Regression (GPR) [40] [38]. In nonlinear regression, some of the alternatives are Support Vector Regression (SVR) [48], K Nearest Neighbor (KNN) regression [49] and Extreme Learning Machine (ELM) [50] [51].…”

Section: E Machine Learning Routinesmentioning

confidence: 99%

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Metrological Advantages of Applying Vibration Analysis to Pipelines: A Review

et al. 2021

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Flow rate is a necessary variable in industrial processes and, therefore, there is a wide variety of instruments designed to measure it. However, the most accepted measuring devices have the problem of being invasive or intrusive. The scientific and technological challenge is to achieve measurement by exploiting all the phenomenological possibilities using a non-intrusive, easy-to-install, portable and low-cost mechanism. This paper presents a literature review on the use of vibration analysis in flow rate metrological systems in order to identify research opportunities for the indirect measurement of this magnitude. A promising line of work was found based on soft flow rate sensors that use the analysis of pipeline vibrations integrated into computational intelligence routines, which allows inference of the flow rate value. The findings promote to continue with new technical and scientific challenges.

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Section: E Machine Learning Routinesmentioning

confidence: 99%

“…[40] [37][41], Principal Component Regression (PCR)[37] [42], Partial Least Squares (PLS) [43] [44] [45] [38] [46], Ridge Regression (RR) [77], Least Absolute Shrinkage Selection Operator (LASSO)[40] …”

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confidence: 99%

Metrological Advantages of Applying Vibration Analysis to Pipelines: A Review

et al. 2021

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Soft metrology based on machine learning: a review

Vallejo

Espriella

Gómez-Santamaría

et al. 2019

Meas. Sci. Technol.

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Soft metrology has been defined as a set of measurement techniques and models that allow the objective quantification of properties usually determined by human perception such as smell, sound or taste. The development of a soft metrology system requires the measurement of physical parameters and the construction of a model to correlate them with the variables that need to be quantified. This paper presents a review of indirect measurement with the aim of understanding the state of development in this area, as well as the current challenges and opportunities; and proposes to gather all the different designations under the term soft metrology, broadening its definition. For this purpose, the literature on indirect measurement techniques and systems has been reviewed, encompassing recent as well as a few older key documents to present a time line of development and map out application contexts and designations. As machine learning techniques have been extensively used in indirect measurement strategies, this review highlights them, and also makes an effort to describe the state of the art regarding the determination of uncertainty. This study does not delve into developments and applications for human and social sciences, although the proposed definition considers the use that this term has had in these areas.

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On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Cited by 2 publications

References 8 publications

Metrological Advantages of Applying Vibration Analysis to Pipelines: A Review

Metrological Advantages of Applying Vibration Analysis to Pipelines: A Review

Soft metrology based on machine learning: a review

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