The semiconductor industry is highly competitive environment where modest improvements in the manufacturing process can translate to significant cost savings. An area where improvements can be realized is reducing the number of wafers that fail to meet their electrical specifications. Wafers that fail to meet electrical specifications are scrapped, which negatively impacts yield and increases manufacturing costs. Most of the existing semiconductor process control research has focused on controlling individual steps during the manufacturing process via run-to-run control, but almost no work has looked at directly controlling device electrical characteristics. Since meeting electrical specifications is so critical to reducing scrap a fab-wide electrical parameter control scheme is proposed to directly control electrical parameter values. The goal of the controller is reducing the variation in the electrical parameters. The control algorithm uses a model to predict electrical parameter values after each processing step. Based on this prediction the decision to make a control move is made. If a control move is necessary, optimal adjustments for the subsequent processing steps are determined. The process model is continually updated so that it reflects the current process. A simple implementation using a least squares model is first proposed. Simulations and an industrial case study demonstrate the potential improvements that can be achieved with the algorithm and the limitations of the simple implementation are discussed. A partial least squares modeling and control algorithm combined with missing data algorithms are proposed as enhancements to the electrical parameter control algorithm to address many of the issues faced when implementing such a control strategy in real manufacturing environments. The enhancements take the input variable correlations into account when making control moves and utilize the correlation structure to make better model predictions. Simulations are performed to determine the effectiveness of the enhancements. A cost function formulation and a Bayesian based alternative are also presented and evaluated. The cost function implementation uses a different method to determine the optimal set points for the subsequent processing steps than the other implementations use. Simulations are used to compare the cost function formulation with the other methods presented. The Bayesian implementation addresses the stochastic nature of the manufacturing process by dealing with the probabilities of events occurring. A simulation of the Bayesian algorithm is preformed and the algorithms limitations are discussed.

No description available.

Diss. Stockholm : Handelshögsk.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.

<div>To continue the shift from batch operations to continuous operations for a wider range of products, advances in real-time process management (RTPM) are necessary. The key requirements for effective RTPM are to have reliable real-time data of the critical process parameters (CPP) and critical quality attributes (CQA) of the materials being processed, and to have robust control strategies for the rejection of disturbances and setpoint tracking.</div><div><br></div><div>Real-time measurements are necessary for capturing process dynamics and implement feedback control approaches. The mass flow rate is an additional important CPP in continuous manufacturing compared to batch processing. The mass flow rate can be used to control the composition and content uniformity of drug products as well as an indicator of whether the process is in a state of control. This is the rationale for investigating real-time measurement of mass flow of particulate streams. Process analytical technology (PAT) tools are required to measure particulate flows of downstream unit operations, while loss-in-weight (LIW) feeders only provide initial upstream flow rates. A novel capacitance-based sensor, the ECVT sensor, has been investigated in this study and demonstrates the ability to effectively measure powder mass flow rates in the downstream equipment.</div><div><br></div><div>Robust control strategies can be utilized to respond to variations and disturbances in input material properties and process parameters, so CQAs of materials/products can be maintained and the amount of off-spec production can be reduced. The hierarchical control system (Level 0 equipment built-in control, Level 1 PAT based PID control and Level 2 optimization-based model predictive control) was applied in the pilot plant at Purdue University and it was demonstrated that the use of active process control allows more robust continuous process operation under different risk scenarios compared to a more rigid open-loop process operation within predefined design space. With the aid of mass flow sensing, the control framework becomes more robust in mitigating the effects of upstream disturbances on product qualities. For example, excursions in the mass flow from an upstream unit operation, which could force a shutdown of the tablet press and/or produce off-spec tablets, can be prevented by proper control and monitoring of the powder flow rate entering the tablet press hopper.</div><div><br></div><div>In this study, the impact of mass flow sensing on the control performance of a direct compaction line is investigated by using flowsheet modeling implemented in MATLAB/Simulink to examine the control performance under different risk scenarios and effects of data sampling (sampling time, measurement precision). Followed by the simulation work, pilot plant studies are reported in which the mass flow sensor is integrated into the tableting line at the exit of the feeding-and-blending system and system performance data is collected to verify the effects of mass flow sensing on the performance of the overall plant-wide supervisory control.</div>

No description available.

<p>Among all additive manufacturing processes, material jetting, or inkjet 3D printing, builds the product similar to the traditional inkjet printing, either by drop-on-demand or continuous printing. Aside from the common advantages as other additive manufacturing methods, it can achieve higher resolution than other additive manufacturing methods. Combining its ability to accept a wide range of functional inks, inkjet 3D printing is predominantly used in pharmaceutical and biomedical applications. A height profile model is necessary to achieve better estimation of the geometry of a printed product. Numerical height profile models have been documented that can estimate the inkjet printing process from when the droplet hits the substrate till fully cured. Although they can estimate height profiles relatively accurately, these models generally take a long time to compute. A simplified model that can achieve sufficient accuracy while reducing computational complexity is needed for real-time process control. In this work, a layer-to-layer height propagation model that aims to balance computational complexity and model accuracy is proposed and experimentally validated. The model consists of two sub-models where one is dedicated to multi-layer line printing and the other is more broadly applicable for multi-layer 2D patterns. Both models predict the height profile of drops through separate volume and area layer-to-layer propagation. The layer-to-layer propagation is based on material flow and volume conservation. The models are experimentally validated on an experimental inkjet 3D printing system equipped with a heated piezoelectric dispenser head made by Microdrop. There are notable similarities between inkjet 3D printing and inkjet image printing, which has been studied extensively to improve color printing quality. Image processing techniques are necessary to convert nearly continuous levels of color intensities to binary printing map while satisfying the human visual system at the same time. It is reasonable to leverage such image processing techniques to improve the quality of inkjet 3D printed products, which might be more effective and efficient. A framework is proposed to adapt image processing techniques for inkjet 3D printing. Standard error diffusion method is chosen as a demonstration of the framework to be adapted for inkjet 3D printing and this adaption is experimentally validated. The adapted error diffusion method can improve the printing quality in terms of geometry integrity with low demand on computation power. Model predictive control has been widely used for process control in various industries. With a carefully designed cost function, model predictive control can be an effective tool to improve inkjet 3D printing. While many researchers utilized model predictive control to indirectly improves functional side of the printed products, geometry control is often overlooked. This is possibly due to the lack of high quality height profile models for inkjet 3D printing for real-time control. Height profile control of inkjet 3D printing can be formulated as a constrained non-linear model predictive control problem. The input to the printing system is always constrained, as droplet volume not only is bounded but also cannot be continuously adjusted due to the limitation of the printhead.  A specific cost function is proposed to account for the geometry of both the final printed product and the intermediate layers better. The cost function is further adjusted for the inkjet 3D printing system to reduce memory usage for larger print geometries by introducing sparse matrix and scaler cost weights. Two patterns with different parameter settings are simulated using model predictive controller. The simulated results show a consistent improvement over open-loop prints. Experimental validation is also performed on both a bi-level pattern and a P pattern, same as that printed with adapted error diffusion for inkjet 3D printing. The model predictive controlled printing outperforms the open-loop printing. In summary, a set of layer-to-layer height propagation profile models for inkjet 3D printing are proposed and experimentally validated. A framework to adapt error diffusion to improve inkjet 3D printing is proposed and validated experimentally. Model predictive control can also improve geometric integrity of inkjet 3D printing with a carefully designed cost function to address memory usage. It is also experimentally validated.</p>

<p>The industrial application of production planning and process scheduling optimization is addressed in this thesis. The first part of the thesis addresses the research into process scheduling application. Several scheduling models are developed based on both discrete and continuous time modelling frameworks. Extensions to both frameworks are presented to address unique production policies and maintenance activities. The potential benefits of schedule optimization is determined through several comparative industrial case studies. The weekly production schedules of the actual plant are compared against the schedules generated by optimization. The historical plant performance is ascertained and areas where efficiency gains are possible are highlighted. In addition, the scheduling model is used to investigate potential changes to production policies.</p> <p>The second part of the thesis addresses the research conducted in production planning application. The main goal of production planning is the efficient generation of a plan that specifies production targets for products over a medium term horizon. Direct application of previously proposed planning models fails to model several unique and key processing features of the production facility. A production planning model is presented that relaxes the detailed scheduling model structure and exploits the use of traveling salesman type constraints to accurately model sequence dependent changeovers. Two case studies are presented to investigate the benefits of optimization in production planning. The first case study investigates the lowest cost planning solution over a three month planning horizon. The second case study investigates the effects of a key production parameter on the optimality of solution. The results highlight the potential benefit of optimization application in increasing plant processing efficiency and reducing unnecessary production downtime.</p> <p>Finally, a modelling framework is presented that allows for the combined scheduling of production and maintenance. The framework allows for maintenance with various timing requirements and extends the capabilities of current frameworks.</p> / Master of Applied Science (MASc)