Fault detection has been recognized in the semiconductor industry as an effective component of advanced processcontrol framework in increasing yield and product quality. Principal component analysis (PCA) has been ap...
详细信息
Fault detection has been recognized in the semiconductor industry as an effective component of advanced processcontrol framework in increasing yield and product quality. Principal component analysis (PCA) has been applied widely to semiconductor manufacturing process monitoring. However, the unique characteristics of semiconductor processes-high dimension of data, nonlinearity in most batch processes, and multimodal batch trajectories due to multiple operating conditions-significantly limit applicability of PCA to semiconductor manufacturing. This paper proposes a manifold learning algorithm, local and nonlocal preserving projection (LNPP), for feature extraction. Different from PCA, which aims to discover the global structure of Euclidean space, LNPP can find a good linear embedding that preserves local and nonlocal information. This may enable LNPP to find meaningful low-dimensional information hidden in high-dimensional observations. The Gaussian mixture model (GMM) is applied to handle processdata with nonlinearity or multimodal features. GMM-based Mahalanobis distance is proposed to assess process states, and a Bayesian inference-based method is proposed to provide the process failure probability. A variable replacing-based contribution analysis method is developed to identify the process variables that are responsible for the onset of process fault. The proposed monitoring model is demonstrated through its application to a batch semiconductor etch process.
This paper presents analytical modeling, system identification, and controller design for a "pneumatic-only" vibration isolation system. There exist extensive literature on traditional suspension designs but...
详细信息
ISBN:
(纸本)9780791844182
This paper presents analytical modeling, system identification, and controller design for a "pneumatic-only" vibration isolation system. There exist extensive literature on traditional suspension designs but the literature on purely pneumatic suspension devices is very sparse. This paper presents an extensive modeling and experimental work for characterizing dynamical behavior of the system. The experimental data was used to derive approximate but simple low-order dynamic models using system identification techniques. A solenoid operated orifice mechanism is used to control the flow of air mass between air spring and accumulator. The design of accumulated air spring with controlled orifice constitutes a Continuously Variable Natural Frequency and Damping (CVNFD) device introduced in previous work by the second author. The paper also presents a robust controlanalysis and design for disturbance rejection problem. The results show promising trends for potential use of such simple and inexpensive system in various vibration isolation applications.
Temperature distribution during polymer sheet reheat phase in thermoforming is the focus of the research, which directly affects the quality of the final products. There are 3 ways of heat transfer that affect tempera...
详细信息
ISBN:
(纸本)9781457720727
Temperature distribution during polymer sheet reheat phase in thermoforming is the focus of the research, which directly affects the quality of the final products. There are 3 ways of heat transfer that affect temperature distribution during the sheet heat process, which are conduction, convection, and radiation. The work aims to find the analogy of electrical and thermal parameters for electro-thermal model including the three impacts, and also to use the method of finite element analysis to get the simulation result of temperature distribution.
The slashing is a very important procedure in textile manufacturing process which can improve warp quality, loom efficiency and reduce warp break. A hybrid modeling method is proposed for textile slashing process. Dat...
详细信息
Intelligent dataanalysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the dataanalysisprocess, but also ...
详细信息
Intelligent dataanalysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the dataanalysisprocess, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic dataanalysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent dataanalysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.
Conceptual State analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State analysis enhances CSA with formal modelin...
详细信息
Conceptual State analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State analysis enhances CSA with formal modeling and analysis methods. We propose a process for generating the state vector of a given system architecture, based on a graph representation of the architecture's model in Object-process Methodology. A robust graph data structure that represents the model is queried to produce the architecture's state space. The process facilitates analysis, reasoning, and model revision based on improved understanding of state vectors and state spaces. State attribute refinements within the model allow for model validity, viability, and reusability as the architecture evolves, with multiple agents, attributes, and attribute state values. The CSA approach advocates and facilitates careful, dynamic, and interactive state space exploration in lieu of exhaustive upfront enumeration of state space permutations. The results can be fed into other analysis, simulation, or visualization tools. We demonstrate CSA on driver assistance technology. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems conference, June 2021.
Execution of process models requires a process engine to handle control flow and data dependencies. While control flow is well supported in available activity-oriented process engines, data dependencies have to be spe...
详细信息
Execution of process models requires a process engine to handle control flow and data dependencies. While control flow is well supported in available activity-oriented process engines, data dependencies have to be specified manually in an error-prone and time-consuming work. In this paper, we present an extension to the process engine Activiti allowing to automatically extract complex data dependencies from process models and to enact the respecting models. We also briefly explain required extensions to BPMN to allow a model-driven approach for data dependency specification easing the process of datamodeling.
The domain specific software architectures (DSSA) community has defined a philosophy for the development of complex systems. This philosophy improves productivity and efficiency by increasing the user's role in th...
详细信息
ISBN:
(纸本)0819415480
The domain specific software architectures (DSSA) community has defined a philosophy for the development of complex systems. This philosophy improves productivity and efficiency by increasing the user's role in the definition of requirements, increasing the systems engineer's role in the reuse of components, and decreasing the software engineer's role to the development of new components and component modifications only. The scenario-based engineering process (SEP), the first instantiation of the DSSA philosophy, has been adopted by the next generation controller project. It is also the chosen methodology of the trauma care information management system project, and the surrogate semi-autonomous vehicle project. SEP uses scenarios from the user to create domain models and define the system's requirements. Domain knowledge is obtained from a variety of sources including experts, documents, and videos. This knowledge is analyzed using three techniques: scenario analysis, task analysis, and object-oriented analysis. Scenario analysis results in formal representations of selected scenarios. Task analysis of the scenario representations results in descriptions of tasks necessary for object-oriented analysis and also subtasks necessary for functional system analysis. Object-oriented analysis of task descriptions produces domain models and system requirements. This paper examines the representations that support the DSSA philosophy, including reference requirements, reference architectures, and domain models. The processes used to create and use the representations are explained through use of the scenario-based engineering process. Selected examples are taken from the next generation controller project.
The Markov Modulated Poisson process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. Des...
详细信息
The Markov Modulated Poisson process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. Despite the rich literature on MMPP, very little is known on its intricate temporal dependence structure. No exact solution is available so far to capture the functional temporal dependence of MMPP at the stationary state over slotted times. This article tackles the above challenges with copula analysis. It not only presents a novel analytical framework to capture the temporal dependence of MMPP but also provides the exact copula-based solutions for single MMPP as well as the aggregate of independent MMPP. This theoretical contribution discloses functional dependence structure of MMPP. It also lays the foundation for many applications that rely on the temporal dependence of MMPP for adaptive control or predictive resource provisioning. We demonstrate case studies, with real-world trace data as well as simulation, to illustrate the practical significance of our analytical results.
Short-term load forecasting is one of the most important routine works for power dispatch departments. The accuracy of load forecasting will exert direct effects on the safety, economy and stabilization of the power s...
详细信息
暂无评论