Prediction of Tool Recipe Runtimes in Semiconductor Manufacturing

To improve throughput, due date adherence, or tool usage in semiconductor manufacturing, it is crucial to model the duration of individual processes such as coating, diffusion, or etching. Equipped with such data, production planning can develop dispatch schemes and schedules for optimized material routing. However, just a few tools indicate how long a process will take. Many variables affect the runtime of tool recipes that are used to realize processes. These variables include wafer processing mode, historical context, batch size, and job handling. In this thesis, a model that allows inferring tool recipe runtimes with adequate accuracy shall be developed. Firstly, predictive models shall be built for selected tools with known runtime behavior to establish a baseline for the methodology. Tools will be selected to cover a broad spectrum of processing modalities. The main predictors will be revealed using variable importance analysis. Furthermore, the analysis shall reveal under which conditions recipe runtime modeling is most accurate. Secondly, a generic approach shall be created to model recipe runtime. By accounting for tool, process, and material context, methods would be investigated from feature selection and automatic model selection. Finally, a pipeline for data cleansing, feature engineering, model building, and metrics will be developed using historical data from a wide range of factory data sources. Finally, a scheme to operationalize the findings shall be outlined. In particular, this requires establishing model serving to enable consumption in applications such as dispatching or operator interfaces

Assessment of Mixed-Layer Height Estimation from Single-Wavelength Ceilometer Profiles

An assessment of differing boundary/mixed-layer height measurement methods was performed with a focus on the Vaisala CL51 instrument and BLView and STRAT softwares. Of primary interest was determining how these differ- ng methodologies will intercompare when deployed as part of a larger instrument network. The intercomparisons were performed as part of ongoing measurements at the Chemistry And Physics of the Atmospheric Boundary Layer Experiment (CAPABLE) site in Hampton, VA and during the 2014 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign that took place in the Denver, CO area. It was observed that data collection methodology is not as important as the processing algorithm, and that, generally speaking, sonde-derived boundary layer heights are higher than LIDAR-derived mixed-layer heights

Charting the path towards a long-term knowledge brokerage function: an ecosystems view

Hybrid networks of actors such as policymakers, funders, scholars, and business practitioners are simultaneous producers and consumers of evidence use. While this diversity of evidence use is a strength, it also necessitates greater collaboration among interested parties for knowledge exchange. To address this need, we investigate how ecotones, which are hybrid networks operating in the transitional area between two distinct ecosystems, such as academic research and policy ecosystems, must involve, disseminate, and integrate different types of knowledge. Specifically, our research aims to unpack how an ecotone’s knowledge brokerage function evolves over its lifecycle. This paper presents the findings of a phenomenological investigation involving experts from the policy and academic research ecosystems. The study introduces a three-stage maturity transitions framework that outlines the trajectory of the brokerage function throughout the ecotone’s lifecycle: i. as a service function, ii. a programme-partnership, and iii. a network of networks. The paper contributes to the theory of knowledge brokerage for policy-making. We reflect on our findings and discuss the theoretical contributions within an ecosystem approach and their associated research and policy implications

A socio-technical approach for assistants in human-robot collaboration in industry 4.0

The introduction of technologies disruptive of Industry 4.0 in the workplace integrated through human cyber-physical systems causes operators to face new challenges. These are reflected in the increased demands presented in the operator's capabilities physical, sensory, and cognitive demands. In this research, cognitive demands are the most interesting. In this perspective, assistants are presented as a possible solution, not as a tool but as a set of functions that amplify human capabilities, such as exoskeletons, collaborative robots for physical capabilities, virtual and augmented reality for sensory capabilities. Perhaps chatbots and softbots for cognitive capabilities, then the need arises to ask ourselves: How can operator assistance systems 4.0 be developed in the context of industrial manufacturing? In which capacities does the operator need more assistance? From the current paradigm of systematization, different approaches are used within the context of the workspace in industry 4.0. Thus, the functional resonance analysis method (FRAM) is used to model the workspace from the sociotechnical system approach, where the relationships between the components are the most important among the functions to be developed by the human-robot team. With the use of simulators for both robots and robotic systems, the behavior of the variability of the human-robot team is analyzed. Furthermore, from the perspective of cognitive systems engineering, the workspace can be studied as a joint cognitive system, where cognition is understood as distributed, in a symbiotic relationship between the human and technological agents. The implementation of a case study as a human-robot collaborative workspace allows evaluating the performance of the human-robot team, the impact on the operator's cognitive abilities, and the level of collaboration achieved in the human-robot team through a set of metrics and proven methods in other areas, such as cognitive systems engineering, human-machine interaction, and ergonomics. We conclude by discussing the findings and outlook regarding future research questions and possible developments.La introducción de tecnologías disruptivas de Industria 4.0 en el lugar de trabajo integradas a través de sistemas ciberfísicos humanos hace que los operadores enfrenten nuevos desafíos. Estos se reflejan en el aumento de las demandas presentadas en las capacidades físicas, sensoriales y cognitivas del operador. En esta investigación, las demandas cognitivas son las más interesantes. En esta perspectiva, los asistentes se presentan como una posible solución, no como una herramienta sino como un conjunto de funciones que amplifican las capacidades humanas, como exoesqueletos, robots colaborativos para capacidades físicas, realidad virtual y aumentada para capacidades sensoriales. Quizás chatbots y softbots para capacidades cognitivas, entonces surge la necesidad de preguntarnos: ¿Cómo se pueden desarrollar los sistemas de asistencia al operador 4.0 en el contexto de la fabricación industrial? ¿En qué capacidades el operador necesita más asistencia? A partir del paradigma actual de sistematización, se utilizan diferentes enfoques dentro del contexto del espacio de trabajo en la industria 4.0. Así, se utiliza el método de análisis de resonancia funcional (FRAM) para modelar el espacio de trabajo desde el enfoque del sistema sociotécnico, donde las relaciones entre los componentes son las más importantes entre las funciones a desarrollar por el equipo humano-robot. Con el uso de simuladores tanto para robots como para sistemas robóticos se analiza el comportamiento de la variabilidad del equipo humano-robot. Además, desde la perspectiva de la ingeniería de sistemas cognitivos, el espacio de trabajo puede ser estudiado como un sistema cognitivo conjunto, donde la cognición se entiende distribuida, en una relación simbiótica entre los agentes humanos y tecnológicos. La implementación de un caso de estudio como un espacio de trabajo colaborativo humano-robot permite evaluar el desempeño del equipo humano-robot, el impacto en las habilidades cognitivas del operador y el nivel de colaboración alcanzado en el equipo humano-robot a través de un conjunto de métricas y métodos probados en otras áreas, como la ingeniería de sistemas cognitivos, la interacción hombre-máquina y la ergonomía. Concluimos discutiendo los hallazgos y las perspectivas con respecto a futuras preguntas de investigación y posibles desarrollos.Postprint (published version