Journal ArticleDOI
Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes
Qinghua He,Jin Wang +1 more
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TLDR
In this paper, a fault detection method using the k-nearest neighbor rule (FD-kNN) is developed for the semiconductor industry, which makes decisions based on small local neighborhoods of similar batches, and is well suited for multimodal cases.Abstract:
It has been recognized that effective fault detection techniques can help semiconductor manufacturers reduce scrap, increase equipment uptime, and reduce the usage of test wafers. Traditional univariate statistical process control charts have long been used for fault detection. Recently, multivariate statistical fault detection methods such as principal component analysis (PCA)-based methods have drawn increasing interest in the semiconductor manufacturing industry. However, the unique characteristics of the semiconductor processes, such as nonlinearity in most batch processes, multimodal batch trajectories due to product mix, and process steps with variable durations, have posed some difficulties to the PCA-based methods. To explicitly account for these unique characteristics, a fault detection method using the k-nearest neighbor rule (FD-kNN) is developed in this paper. Because in fault detection faults are usually not identified and characterized beforehand, in this paper the traditional kNN algorithm is adapted such that only normal operation data is needed. Because the developed method makes use of the kNN rule, which is a nonlinear classifier, it naturally handles possible nonlinearity in the data. Also, because the FD-kNN method makes decisions based on small local neighborhoods of similar batches, it is well suited for multimodal cases. Another feature of the proposed FD-kNN method, which is essential for online fault detection, is that the data preprocessing is performed automatically without human intervention. These capabilities of the developed FD-kNN method are demonstrated by simulated illustrative examples as well as an industrial example.read more
Citations
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Journal ArticleDOI
Fault detection and diagnosis in process data using one-class support vector machines
Sankar Mahadevan,Sirish L. Shah +1 more
TL;DR: It is shown that the proposed algorithm outperformed PCA and DPCA both in terms of detection and diagnosis of faults.
Journal ArticleDOI
Deep convolutional neural network model based chemical process fault diagnosis
Hao Wu,Jinsong Zhao +1 more
TL;DR: A fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis and the benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance.
Journal ArticleDOI
A deep belief network based fault diagnosis model for complex chemical processes
Zhanpeng Zhang,Jinsong Zhao +1 more
TL;DR: An extensible deep belief network (DBN) based fault diagnosis model is proposed and individual fault features in both spatial and temporal domains are extracted by DBN sub-networks, aided by the mutual information technology.
Journal ArticleDOI
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi,Ataollah Shirzadi,Kayvan Ghaderi,Ebrahim Omidvar,Nadhir Al-Ansari,John J. Clague,Marten Geertsema,Khabat Khosravi,Ata Amini,Sepideh Bahrami,Omid Rahmati,Kyoumars Habibi,Ayub Mohammadi,Hoang Nguyen,Assefa M. Melesse,Baharin Bin Ahmad,Anuar Ahmad +16 more
TL;DR: The results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models and should be more widely applied for the sustainable management of flood-prone areas.
Journal ArticleDOI
Time Series Classification With Multivariate Convolutional Neural Network
TL;DR: A tensor scheme along with a novel deep learning architecture called multivariate convolutional neural network (MVCNN) for multivariate time series classification, in which the proposed architecture considers multivariate and lag-feature characteristics.
References
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Journal ArticleDOI
Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
Journal ArticleDOI
Process Analysis: Estimating Mediation in Treatment Evaluations
Charles M. Judd,David A. Kenny +1 more
TL;DR: In this paper, the authors present the rationale and procedures for conducting a process analysis in evaluation research, which attempts to identify the process that mediates the effects of some treatment, by estimating the parameters of a causal chain between the treatment and some outcome variable.
Journal ArticleDOI
Statistical process monitoring: basics and beyond
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.