Employee Profile

Wei-Ting Yang

Assistant Professor - Department of Data Science and Analytics


Wei-Ting Yang is Assistant Professor at the Department of Data Science and Analytics. She received her PhD from École des Mines de Saint-Étienne. Her current research focuses on probabilistic graphical models, predictive models, multivariate analysis, and their application to anomaly detection, quality prediction, and process control. She is particularly interested in combining data-driven approaches and domain expert knowledge to improve business and quality problems.


Yang, Wei-Ting; Blue, Jakey, Roussy, Agnès, Pinaton, Jacques & Reis, Marco (2020)

A physics-informed Run-to-Run control framework for semiconductor manufacturing

Expert Systems With Applications, 155(113424) Doi: 10.1016/j.eswa.2020.113424

For decades, Run-to-Run (R2R) controllers have been widely implemented in semiconductor manufacturing. They operate over key process parameters on the basis of the metrological measurements acquired from the process and their deviations from the target setpoints. Conventionally, R2R controllers have been implemented independently of the actual equipment condition, which is obviously affecting the process stability and performance. Therefore, both equipment signals and process states shall be considered to make the R2R controllers more robust to the equipment condition drifts. In this paper, we propose a novel physics-informed framework to integrate the real-time equipment condition, based on the Fault Detection and Classification (FDC) data, into the R2R controllers. By utilizing Dynamic Bayesian Networks (DBN), the implicit relationship structure between metrology measurements, FDC indicators, and R2R regulators can be learned and reviewed explicitly. The structure shall be further reviewed to valid with the existing relationships and expert knowledge. Infeasible causalities on the structure will be constrained via setting up the blacklist at the structure learning stage. The proposed framework consists of the offline modeling stage, which incorporates the process, equipment variables, and the expert knowledge in the structure learning, and the online control stage, which constructs the Structured R2R controller (SRC) based on the relationship structure. As a result, the model is consistent by design with empirically known relationships and fundamental physical laws. The proposed SRC not only optimizes the operation with respect to the target control values but also considers the equipment and process states simultaneously. The effectiveness of SRC and the derivative control strategy are validated through a real dataset of a Chemical-Mechanical Polishing (CMP) process, and two simulated studies.

Yang, Wei-Ting; Blue, Jakey, Roussy, Agnès, Pinaton, Jacques & Reis, Marco (2019)

A structured data-driven framework for virtual metrology modeling

IEEE Transactions on Automation Science and Engineering, 17, s. 1297- 1306. Doi: 10.1109/TASE.2019.2941047

Virtual metrology (VM) has been widely studied in the semiconductor industry with the purpose of decreasing the cycle time and reducing the expensive metrology measurements. Ideally, a VM model should not only be able to provide accurate predictions but also present an interpretable and rational structure to accommodate fundamental restrictions and relationships that are known to be present in the process. The last aspects have been missing in the VM models proposed hitherto. Therefore, in this article, we propose a novel framework by combining in a single VM model the capability to learn from data with the ability to incorporate the domain knowledge on the process. Thus, the new methodology can use the best of both information sources: data and the subject-matter expert (SME) knowledge. The framework consists of two phases. In the first phase, a Gaussian Bayesian network (GBN) is used to extract the implicit relationships between the metrology and production/process variables. In the second phase, the target response variable is defined, the predictors are selected through the associated Markov blanket, and finally, an empirical model is estimated to accurately predict the response. The proposed framework was tested and its effectiveness was confirmed through a real industrial data from a chemical-mechanical polishing (CMP) process in semiconductor fabrication. The physical meaning of the model obtained was also scrutinized by an SME.

Academic Degrees
Year Academic Department Degree
2020 École des Mines de Saint-Étienne Ph.D.