Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corre

Geometric quality variables of wafers, such as BOW and WARP, are critical in their applications. A large variation of these quality variables reduces the number of conforming products in the downstream production. Therefore, it is important to reduce the variation by variation modeling, analysis and control for multistage wafer manufacturing processes (MWMPs). First, an intermediate feedforward control strategy is developed to adjust and update the control actions based on the online measurements of intermediate wafer quality measurements. The control performance is evaluated in a MWMP to transform ingots into polished wafers. However, in a complex multistage manufacturing process, the quality variables may have nonlinear relationship with the parameters of the predictors. In this case, piecewise linear regression tree (PLRT) models are used to address nonlinear relationships in MWMP to improve the model prediction performance. The obtained PLRT model is further reconfigured to be complied with the physical layout of the MWMP for feedforward control purposes. The procedure and effectiveness of the proposed method is shown in a case study of a MWMP. Furthermore, as the geometric profiles and quality variables are important quality features for a wafer, fast and accurate measurements of those features are crucial for variation reduction and feedforward control. A sequential measurement strategy is proposed to reduce the number of samples measured in a wafer, yet provide adequate accuracy for the quality feature estimation. A Gaussian process model is used to estimate the true profile of a wafer with improved sensing efficiency. Finally, we study the multistage multimode process monitoring problem. We propose to use PLRTs to inter-relate the variables in a multistage multimode process. A unified charting system is developed. We further study the run length distribution, and optimize the control chart system by considering the modeling uncertainties. Finally, we compare the proposed method with the risk adjustment type of control chart systems based on global regression models, for both simulation study and a wafer manufacturing process.

Manufacturing variations refer to the uncertainties in the processes and inconsistency in the products produced. There have been increasing efforts to minimize the manufacturing variations, and reducing manufacturing variations in advanced manufacturing processes and systems is becoming more important. Advanced manufacturing processes and systems integrate manufacturing with innovative science and technologies and boost manufacturing efficiency and productivity. The integration with sensor technology now provides massive data, creating unprecedented research opportunities to model and analyze through data-driven models and methods. However, at the same time, advanced manufacturing processes and systems involve new critical challenges in modeling and managing the manufacturing variations. Many advanced manufacturing processes and systems have complex dynamics and transformation and multiple components involved, which create significant variations and uncertainties. However, physics-based models are often unavailable and often fail to address the uncertainties. This dissertation addresses multiple challenges in modeling and managing the manufacturing variations: (1) Variation source identification in multistage manufacturing systems: In multistage manufacturing systems, where multiple operations are performed in a series of stages (e.g., workstations), the variations produced from operations propagate to downstream measurements. In such systems, it is crucial to identify faulty operations with excessive variations among a large number of operations based on the quality measurements. We consider a common case where the measurements are not directly taken from the operations but from products in the downstream stage and the number of operations is much larger than the number of measurements. However, inferring underlying variations of numerous operations by limited measurements cause technical challenges in statistical inference. Therefore, we want to establish a statistical model that can identify faulty operations by leveraging the Engineering domain knowledge. Three types of domain knowledge are considered: a) The fact that faults occur sporadically; b) Practitioners' empirical knowledge of the faults occurrence frequency; c) Various tolerance levels on variations across operations. (2) Modeling inkjet printing manufacturing process: The inkjet printing manufacturing process involves significant random variations due to the complex physical and chemical dynamics of the nanomaterial pieces in the printed ink. Process variations create significant uncertainties in the manufactured product quality, but such uncertainties have not been studied. Therefore, it is crucial to model the randomness in the manufacturing outcome in terms of process parameters. Building upon the statistical model, this work further aims to establish a statistic that evaluates the manufacturing outcome quality, and ultimately identifies abnormal manufacturing outcomes. (3) Statistical calibration of underlying physical variable: In designing manufacturing processes and products, inferring the underlying physical input variable, called statistical calibration, is often needed. For example, by using the GFET nanosensor outputs, inferring the amount of the target substance in the environment is important. Furthermore, the uncertainty of the inferred variable needs to be quantified. However, due to significant process variations in manufacturing, the GFET nanosensor outputs involve significant random variations, and thus precise inferring is challenging. Specifically, random shapes and random locations of functional data need to be modeled for precise calibration. (4) Optimal parameter design through Bayesian optimization: It is very crucial to design manufacturing processes or products so that they have small quality variations while satisfying the overall quality (i.e., robust design). However, data are often costly to acquire especially in the designing stage. Furthermore, the underlying exact relationships between the design variables and the mean and variance of the outputs are not known and are in complex forms. Therefore, a sample efficient data-driven method to find the robust design needs to be established. To address these challenges listed above, four problems are investigated in this dissertation. (i) To build a special sparsity-enhanced Bayesian linear random-effects model to reflect Engineering domain knowledge. With the proposed model, Engineering domain knowledge on sparse faults with excessive variations is incorporated into the model, and the variation sources are successfully identified. (ii) To model the uncertainties in the inkjet printing manufacturing process in terms of physical process parameters. Building upon the proposed model, abnormal manufacturing outcomes are successfully identified. (iii) To establish a non-parametric model to characterize functional data with significant variations. The issue with random shapes and random shifting of functional data is addressed. (iv) To establish sample-efficient stochastic constrained optimization method for constrained robust parameter design. The proposed technique minimizes the variations while satisfying a constraint on the mean of the quality measurements by conducting a small number of experiments. Because the proposed methods are driven by data, these models and methods are very flexible and can be used to address many general problems in other manufacturing processes.

The Encyclopedia of Systems and Control collects a broad range of short expository articles that describe the current state of the art in the central topics of control and systems engineering as well as in many of the related fields in which control is an enabling technology. The editors have assembled the most comprehensive reference possible, and this has been greatly facilitated by the publisher’s commitment continuously to publish updates to the articles as they become available in the future. Although control engineering is now a mature discipline, it remains an area in which there is a great deal of research activity, and as new developments in both theory and applications become available, they will be included in the online version of the encyclopedia. A carefully chosen team of leading authorities in the field has written the well over 250 articles that comprise the work. The topics range from basic principles of feedback in servomechanisms to advanced topics such as the control of Boolean networks and evolutionary game theory. Because the content has been selected to reflect both foundational importance as well as subjects that are of current interest to the research and practitioner communities, a broad readership that includes students, application engineers, and research scientists will find material that is of interest.

This book includes state-of-the-art and original research contributions from two well-established conferences, which collectively focus on the joint design, development, and management of products, advanced production systems, and business for sustainable customization and personalization. The book includes wide range of topics within these subjects, ranging from industrial success factors to original contributions within the field. The authors represent worldwide leading research institutions.

This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.