Application of PCA for efficient multivariate FDC of semiconductor manufacturing equipment

Thieullen, Alexis; Ouladsine, Mustapha; Pinaton, Jacques

doi:10.1109/asmc.2013.6552755

Cited by 8 publications

(2 citation statements)

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“…Given ( 5) and ( 6), the F-score is expressed in (7). F weighted , as expressed in (8), is used as the main score, which is a weighted sum of F 0 and F 1 that takes into account the imbalanced dataset.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…This imposes a great limitation on the usage of supervised learning methods for fault detection. The scarcity of faulty samples has led to the widespread use of self-supervised learning methods based on Principal Component Analysis (PCA) [7], Independent Component Analysis (ICA) [8], and Partial Least Squares (PLS) [9], which can be combined with supervised learning methods such as support vector machine (SVM) [10] and k-Nearest Neighbors (k-NN) [11] for fault identification.…”

Section: Introductionmentioning

confidence: 99%

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Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment

Tchatchoua,

Graton,

Ouladsine

et al. 2023

Sensors

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Amid the ongoing emphasis on reducing manufacturing costs and enhancing productivity, one of the crucial objectives when manufacturing is to maintain process tools in optimal operating conditions. With advancements in sensing technologies, large amounts of data are collected during manufacturing processes, and the challenge today is to utilize these massive data efficiently. Some of these data are used for fault detection and classification (FDC) to evaluate the general condition of production machinery. The distinctive characteristics of semiconductor manufacturing, such as interdependent parameters, fluctuating behaviors over time, and frequently changing operating conditions, pose a major challenge in identifying defective wafers during the manufacturing process. To address this challenge, a multivariate fault detection method based on a 1D ResNet algorithm is introduced in this study. The aim is to identify anomalous wafers by analyzing the raw time-series data collected from multiple sensors throughout the semiconductor manufacturing process. To achieve this objective, a set of features is chosen from specified tools in the process chain to characterize the status of the wafers. Tests on the available data confirm that the gradient vanishing problem faced by very deep networks starts to occur with the plain 1D Convolutional Neural Network (CNN)-based method when the size of the network is deeper than 11 layers. To address this, a 1D Residual Network (ResNet)-based method is used. The experimental results show that the proposed method works more effectively and accurately compared to techniques using a plain 1D CNN and can thus be used for detecting abnormal wafers in the semiconductor manufacturing industry.

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Section: Evaluation Metricsmentioning

confidence: 99%