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Spatial Correlated Data Monitoring in Semiconductor Manufacturing Using Gaussian Process Model
摘要： In semiconductor manufacturing, various wafer tests are conducted in each stage. The analysis and monitoring of collected wafer testing data plays an important role in identifying potential problems and improving process yield. There exists three variation sources: lot-to-lot variation, wafer-to-wafer variation and site-to-site variation, which means the measurements cannot be considered independently. However, most existing control charts for monitoring wafer quality are based on the assumption that data are independently and identically distributed. To deal with the variations, we propose a mixed-effects model incorporating a Gaussian process to account for the variations. Based on the model, two control charts are implemented to detect anomalies of the measurements which can monitor the changes of the variations and the quality of products respectively. Simulation studies and results from real applications show that this model and control scheme is effective in estimating and monitoring the variation sources in the manufacturing process.
关键词： semiconductor manufacturing,mixed-effects model,statistical process control (SPC),Gaussian process
更新于2025-09-23 15:21:01
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A Magnetic linked Multilevel Active Neutral Point Clamped Converter with an Advanced Switching Technique for Grid Integration of Solar Photovoltaic Systems
摘要： Complex multi-cluster tools have been extensively used in semiconductor manufacturing. It is crucial to increase their productivity by their effective operation. With structural complexity, multiple robots, and the interaction among individual tools, it is very challenging to schedule a tree-like multi-cluster tool. This paper investigates the scheduling problem of such a tool whose bottleneck individual tool is process-bound. The system is modeled by well-known discrete-event models, i.e., resource-oriented Petri nets. Based on the models, for the ?rst time, this work develops necessary and suf?cient conditions under which a one-unit (wafer) periodic schedule exists and shows that an optimal one-unit periodic schedule can always be found. Algorithms with polynomial complexity are presented to ?nd the optimal cycle time and the one-unit periodic schedule. Industrial examples are used to illustrate the proposed method, and they show that a signi?cant reduction in cycle time can be obtained in comparison with the existing method.
关键词： multi-robot systems,semiconductor manufacturing,Cluster tool,Petri net (PN),scheduling
更新于2025-09-16 10:30:52
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On Optimising Spatial Sampling Plans for Wafer Profile Reconstruction ? ?The second author gratefully acknowledges the financial support provided by Irish Manufacturing Research (IMR) for this research.
摘要： Wafer metrology is an expensive and time consuming activity in semiconductor manufacturing, but is essential to support advanced process control, predictive maintenance and other quality assurance functions. Keeping metrology to a minimum is therefore desirable. In the context of spatial sampling of wafers this has motivated the development of a number of data driven methodologies for optimizing wafer sampling plans. Two such methodologies are considered in this paper. The first combines Principal Component Analysis and Minimum Variance Estimation (PCA-MVE) to determine an optimum subset of sites from historical metrology data from a larger candidate set, while the second employs Forward Selection Component Analysis (FSCA), an unsupervised variable selection technique, to achieve the same result. We investigate the relationship between these two approaches and show that under specific conditions a regularized extension of FSCA, denoted FSCA-R, and PCA-MVE are equivalent. Numerical studies using simulated data verify the equivalence conditions. Results for simulated and industrial case studies show that the improvement in wafer profile reconstruction accuracy with regularization is not statistically significant for the case studies considered, and that when PCA-MVE is implemented with a denoising step as originally proposed, it is outperformed by FSCA. Therefore, FSCA is the preferred methodology.
关键词： spatial sampling,Principal Component Analysis,Forward Selection Component Analysis,semiconductor manufacturing,wafer site selection,metrology
更新于2025-09-12 10:27:22