Anomaly Detection in Time Series Data and its Application to Semiconductor Manufacturing

Anomaly detection is essential for the monitoring and improvement of product quality in manufacturing processes. In the case of semiconductor manufacturing, where large amounts of time series data from equipment sensors are rapidly accumulated, identifying anomalous signals within this data presents a significant challenge. The data in question is multivariate and of varying lengths, with an often highly imbalanced ratio of normal to abnormal signals. Given the nature of this data, traditional data-driven methods may not be appropriate for its analysis. This paper proposes a novel unsupervised anomaly detection model for the analysis of multivariate time series data. The model utilizes a unique recurrent neural network architecture and a special objective function to detect anomalies. Furthermore, a relevance analysis method is introduced to facilitate the interpretation and analysis of the detected anomalous signals. Our experimental results indicate that this deep anomaly detection model, which summarizes sensor data of different lengths into a low-dimensional latent space, enabling the easy visualization and distinction of anomalous signals, can be applied in real-world semiconductor manufacturing factories and used by on-site engineers for both analysis and execution purposes

Solving PDE-constrained Control Problems using Operator Learning

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Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes

Steelmaking requires precise calculation at several steps of the manufacturing processes. We focus on the hot rolling process using Steckel mills, almost the end step in steel coil manufacturing. The rolling process is a type of plastic working in which a slab passes between two rolls and is stretched to reach the target thickness. It is necessary to predetermine the exact rolling force to obtain a coil with an accurate thickness after the rolling process. First, we introduced a machine learning model for calculating the rolling force, which can be used in-line in real plants. However, a direct calculation of the rolling force can cause stability problems, because the model output directly affects the process. In order to avoid such a problem, we determined a special temperature of the coil by inverse calculation of the classical mechanical model of hot rolling and set it as the model output value. As learning models, deep neural networks (DNN) and gradient boosting-based decision tree models were used. We preprocessed the collected process history data and added artificial features to the model input by creating physical variables used in the classical models. Moreover, to supplement the black-box nature of DNN, feature importance was analyzed from the decision tree model, and utilization and interpretation of each feature in the process are presented. Thus, our methods take advantage of both the classical mathematical model and the deep neural network model.11Ysciescopu