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The prediction of average Material Removal Rate (MRR) in Chemical Mechanical Planarization (CMP) process is regarded as a crucial research objective of Virtual Metrology (VM) for semiconductor manufacturing. In this paper, a novel VM model is proposed to predict MRR in CMP process based on the integration of Gaussian Process Regression (GPR) with a reference-based strategy. The proposed method estimates the similarity of the changing trends of the sensor traces using Maximum Mean Discrepancy (MMD) as a metric to extract reliable references for further prediction. The temporal information is also combined in the strategy by filtering the data samples by timestamps. Afterwards, GPR is used to fuse the reference data samples to predict the metrology with a confidence interval. Compared with the benchmarks in the recent literature, the proposed method, referred as MMD-GPR model, gives better prediction performance than ensemble learning methods and equivalent accuracy compared with Deep Neural Networks (DNN) but with a more efficient framework.

Accurate battery capacity prediction is important to ensure reliable battery operation and reduce the cost. However, the complex nature of battery degradation and the presence of capacity regeneration phenomenon render the prediction task very challenging. To address this problem, this paper proposes a novel and efficient algorithm to predict the battery capacity trajectory in a multi-cell setting. The proposed method is a new variant of Gaussian process regression (GPR) model, and it utilizes similar trajectories in the historical data to enhance the prediction of desired capacity trajectory. More importantly, the proposed method adds no extra computation cost to the standard GPR. To demonstrate the effectiveness of the proposed method, validation tests on two different battery datasets are implemented in the case studies. The prediction results and the computation cost are carefully benchmarked with cutting-edge GPR approaches for battery capacity prediction.

This paper proposes a novel similarity-based algorithm for Remaining Useful Life (RUL) prediction and a methodology for machine prognostics. In the proposed RUL prediction algorithm, a Similarity Matching Procedure including the Kernel Two Sample Test (KTST) is developed to query similar run-to-failure (R2F) profiles from historical data library. Next, the preliminary predictions of RUL are obtained as remaining time-to-failure from the similar R2F records. In the last step, Weibull analysis is performed to fuse the preliminary predictions and to obtain the probability distribution of RUL. Moreover, a methodology for machine prognostics is developed based on the RUL prediction algorithm. Compared with existing similarity-based methods for RUL prediction, the proposed method holds several advantages: 1) the similarities between sensor readings or feature matrices are directly measured without extra health assessment procedure; 2) the proposed method presents good probabilistic interpretations of the prediction uncertainties; 3) the estimated RUL distribution is statistically sound by applying KTST to prescreening the historical R2F records. The effectiveness and the superiority of the proposed method are justified based on the public aero-engine dataset.

Maintenance scheduling and vessel routing are critical for the off-shore wind farm to reduce maintenance costs. In this research, a systematic framework that takes the advantage of predictive analysis for off-shore wind farm maintenance optimization is sketched and the optimization results are presented. The proposed framework consists of three different functional modules - the prognostic and diagnostic (P&D) module, the wind power prediction module, and the maintenance optimization module. The P&D module predicts and diagnoses the system failures based on the operational data of the wind turbine and generates the maintenance tasks for execution. The power prediction module predicts the weather conditions and the production of the wind turbine in the next 1-3 days, which will be helpful for maintenance task prioritization and scheduling. The optimization module absorbs information from the previous two modules as input and optimizes the overall maintenance costs. Comparing with the previous research works, this framework optimizes the maintenance cost in a more challenging situation by considering the predicted remaining useful life from the P&D module and also the future weather condition from the wind power prediction module. In the proposed framework, the maintenance scheduling and the vessel routing are optimized collaboratively with the consideration of real-time production loss. The result of the proposed framework is demonstrated on an off-shore wind farm and reduced maintenance cost is reported.

When abundant historical Run-to-Failure (R2F) data is available, the similarity-based method is one of the favored options for Remaining Useful Life (RUL) prediction due to its simplicity and satisfactory accuracy. In this study, a novel similarity-based methodology for RUL prediction is proposed. The proposed method has two important processing steps: similarity matching and Weibull fitting. The similarity matching screens the historical records by a similarity testing called Kernel Two Sample Test (KTST), and only those records that pass KTST are adopted as references for RUL prediction. For the selected similar records, the RUL is predicted as the remaining time to failure. The Weibull fitting fuses the multiple RUL predictions given by similar historical records. The PDF of RUL is estimated as the fitted Weibull distribution. To demonstrate the effectiveness and superiority of the proposed method, the famous CMAPSS data about aero-engine degradation is adopted for model validation. The results demonstrate improved prediction accuracy comparing with other similarity-based approaches and the state-of-the-art deep learning predictors.

This paper proposes a similarity-based Particle Filter (PF) method for Remaining Useful Life (RUL) prediction with improved performance. In the proposed methodology, Maximum Mean Discrepancy (MMD) and Kernel Two Sample Test are firstly adopted to query similar Run-To-Failure (R2F) profiles from historical data library. The states and parameters of degradation are initialized based on the similar R2F profiles. Next, Rao–Blackwellized​ Particle Filter (RBPF) is employed to update the degradation states based on the initialization. The RUL prediction results are obtained by extrapolating the degradation states updated by RBPF. The proposed RUL prediction method holds several advantages: (1) compared with other PF methods, the proposed model includes historical knowledge from similar R2F profiles; (2) compared with similarity-based methods, the proposed model presents good probabilistic interpretation of prediction uncertainties based on RUL distribution. The effectiveness and superiority over other peer algorithms are justified based on a public aero-engine dataset for prognostics.

The prediction of the average material removal rate (MRR) in chemical mechanical planarization (CMP) process has been recognized to be a critical factor of virtual metrology (VM) modeling for advanced process control (APC). This paper proposes a Gaussian process regression (GPR)-based model to dynamically predict MRR in CMP process. The proposed method uses K-nearest neighbor (KNN) to search for reference MRR samples in the historical dataset. Furthermore, a GPR model is trained to fuse the information from reference samples. Finally, the proposed method employs multi-task Gaussian process (MTGP) to predict the final MRR and quantify the prediction uncertainty based on the historical and the reference MRR. Compared with other methods in the recent literature, the proposed method, named KNN-MTGP model, yields better prediction accuracy than ensemble models, and comparable accuracy with deep neural networks (NN). Besides, KNN-MTGP model is capable to demonstrate the behavior of the past MRR changing with time and provide quantified prediction uncertainties. In this paper, the feasibility and advantages of KNN-MTGP model are evaluated based on the dataset of 2016 prognostic and health management (PHM) data challenge.