Wan J, McLoone S (2018) Gaussian process regression for virtual metrology-enabled run-to-run control in semiconductor manufacturing. Lee S-k, Kang P, Cho S (2014) Probabilistic local reconstruction for k-NN regression and its application to virtual metrology in semiconductor manufacturing. Expert Syst Appl 51:85–106ĭi Y, Jia X, Lee J (2017) Enhanced virtual metrology on chemical mechanical planarization process using an integrated model and data-driven approach. Kang P, Kim D, Cho S (2016) Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Lee K, Kim C (2020) Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process. Wu X, Chen J, Xie L, Lee Y, Chen C-I (2021) Convolutional neural networks for multi-stage semiconductor processes. Hsieh Y, Wang T, Lin C, Peng L, Cheng FT, Shang SY (2021) Convolutional neural networks for automatic virtual metrology. Khan A, Moyne J, Tilbury D (2007) An approach for factory-wide control utilizing virtual metrology. J Process Control 52:66–74Ĭai H, Feng J, Yang Q, Li F, Li X, Lee J (2021) Reference-based virtual metrology method with uncertainty evaluation for material removal rate prediction based on Gaussian process regression. Kang S, Kang P (2017) An intelligent virtual metrology system with adaptive update for semiconductor manufacturing. Yang H, Adnan M, Huang C, Cheng F, Lo Y, Hsu C (2019) An intelligent metrology architecture with AVM for metal additive manufacturing. IEEE Int Conf Emerg Technol Factory Autom ETFA 1–4 Susto GA, Beghi A, De Luca C (2011) A virtual metrology system for predicting CVD thickness with equipment variables and qualitative clustering. The proposed AAL algorithm can enhance the learning accuracy of the MTGPVM model with a small sample size. The proposed MTGPVM achieved prediction performance with 1.44–1.79% mean-absolute-percentage error (MAPE) in thickness and 0.39–0.49% MAPE in refractive index. Finally, the evaluation of the proposed methods was carried out using the practical dataset in a factory. Furthermore, an adaptive algorithm is promoted to update the VM model according to the temporary performance of active learning. Subsequently, active learning methods based on different sampling criteria are improved to address the limited training data issue. Initially, a multitask Gaussian processes-based virtual metrology (MTGPVM) model is built based on the intrinsic coregionalization model (ICM). Accordingly, this paper presents the improved method to combine multitask Gaussian process (MTGP) and adaptive active learning (AAL) for the VM modeling in CVD systems. However, the multitask learning problem and limited labeled data from available real metrology are challenges for VM modeling. Virtual metrology (VM) can assist the quality prediction in CVD based on control variables and preceding metrology results. Chemical vapor deposition (CVD) has been widely applied to create thin films in semiconductor manufacturing.
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