Anomaly detection for sensor data of semiconductor manufacturing equipment using a GAN

Hashimoto, Miki; Ide, Y.; Aritsugi, Masayoshi

doi:10.1016/j.procs.2021.08.090

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…It is easy to understand that the left side has no anomaly s with little variation, while the right side has lots of anomaly situation with larger variation. Between the two, it is reasonable that display panels which are manufactured by the abnormal state equipment are more likely to have more defects, while panels produced by stable equipment are qualified [8].…”

Section: Figure 2 Data Elimination By Under-sampling (C) Over Samplingmentioning

confidence: 99%

P‐58: Novel Defect Data Augmentation in Display Manufacturing Process

Hahn,

Na,

Lee

2024

Symp Digest of Tech Papers

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In this paper, we propose novel defect data augmentation procedure to overcome imbalanced class in development of machine learning models for display manufacturing process. As the amount of defective samples is extremely small compared to that of good samples, it causes severe overfitting and poor performance of models. In addition, numeric or tabular data augmentation is not suitable by GAN or VAE algorithms and its accuracy or similarity is lower than that of image augmentation. To settle this problem, we utilize both over and under sampling, and introduce anomaly score to figure out which process or equipment is in anomaly state. Kernel function or XAI algorithms are also utilized to discover effective features which differentiates defective samples and normal samples. After these process, we use to CT‐GAN to increase the number of defect datasets. In this way, we have demonstrated the enhanced the performance of GAN algorithm in tabular and numeric data.

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Section: Figure 2 Data Elimination By Under-sampling (C) Over Samplingmentioning

confidence: 99%

P‐58: Novel Defect Data Augmentation in Display Manufacturing Process

Hahn,

Na,

Lee

2024

Symp Digest of Tech Papers

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“…RAIM (recurrent attentive and intensive model; Xu et al, 2018 ) was a model including an attention mechanism, which used multi-channel attention to improve the prediction of the model. Hashimoto et al (2021) introduced RAIM into GAN to detect time series anomalies generated by semiconductor sensors. To this end, we consider a multi-channel attention mechanism, and an attention module is connected before both the encoder and the decoder.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The input data

is a series of time series data of length

, where the data

at time

is an

-dimensional vector. Let the

-th subsequence be

, when X is split by a sliding window

( Hashimoto et al, 2021 ). Then the importance

of the time dimension is calculated by the following equation:…”

Section: Proposed Approachmentioning

confidence: 99%

Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture

Cheng

Wang

et al. 2022

Front. Plant Sci.

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More recently, smart agriculture has received widespread attention, which is a deep combination of modern agriculture and the Internet of Things (IoT) technology. To achieve the aim of scientific cultivation and precise control, the agricultural environments are monitored in real time by using various types of sensors. As a result, smart agricultural IoT generated a large amount of multidimensional time series data. However, due to the limitation of applied scenarios, smart agricultural IoT often suffers from data loss and misrepresentation. Moreover, some intelligent decision-makings for agricultural management also require the detailed analysis of data. To address the above problems, this article proposes a new anomaly detection model based on generative adversarial networks (GAN), which can process the multidimensional time series data generated by smart agricultural IoT. GAN is a deep learning model to learn the distribution patterns of normal data and capture the temporal dependence of time series and the potential correlations between features through learning. For the problem of generator inversion, an encoder–decoder structure incorporating the attention mechanism is designed to improve the performance of the model in learning normal data. In addition, we also present a new reconstruction error calculation method that measures the error in terms of both point-wise difference and curve similarity to improve the detection effect. Finally, based on three smart agriculture-related datasets, experimental results show that our proposed model can accurately achieve anomaly detection. The experimental precision, recall, and F1 score exceeded the counterpart models by reaching 0.9351, 0.9625, and 0.9482, respectively.

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“…Lee et al explored GAN‐based anomaly detection on temporal data by proposing a new evaluation framework, using simulated data and water treatment system data 8 . Hashimoto et al analyzed multivariate timing data of semiconductor manufacturing sensors with GAN and successfully detected anomalous defects of components that could not be directly observed 9 . Li et al proposed MAD‐GAN, an unsupervised multivariate anomaly detection method based on GAN, to perform fault detection on timing data from the cyber–physical system 10 …”

Section: Introductionmentioning

confidence: 99%

“…8 Hashimoto et al analyzed multivariate timing data of semiconductor manufacturing sensors with GAN and successfully detected anomalous defects of components that could not be directly observed. 9 Li et al proposed MAD-GAN, an unsupervised multivariate anomaly detection method based on GAN, to perform fault detection on timing data from the cyber-physical system. 10 To avoid blind training of traditional GAN, many studies have proposed GAN variants, among which the auxiliary classifier GAN 11 (ACGAN) based on CGAN has shown superior performance.…”

Section: Introductionmentioning

confidence: 99%

FC‐ACGAN‐based data augmentation for terahertz time‐domain spectral concealed hazardous materials identification

Jiang

Zhan

Zhao

et al. 2022

Int J of Intelligent Sys

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Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training.

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Anomaly detection for sensor data of semiconductor manufacturing equipment using a GAN

Cited by 11 publications

References 3 publications

P‐58: Novel Defect Data Augmentation in Display Manufacturing Process

P‐58: Novel Defect Data Augmentation in Display Manufacturing Process

Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture

FC‐ACGAN‐based data augmentation for terahertz time‐domain spectral concealed hazardous materials identification

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