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Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, hardware-induced delays may produce overshoots and even instability when controlling the system. In this case, Smith predictor controllers can be used because they are well suited for delayed processes. This paper addresses the accurate positioning of a mobile robot on a terrain of an unknown slope. This slope produces disturbance torques of unknown amplitudes in the robot actuators that yield a steady-state error in the positioning. Because our actuators are integrating plus time delay plants, the standard Smith predictor cannot remove these disturbances. This paper proposes a modification of this control scheme in order to remove these disturbances yielding a zero steady-state error in the actuators. Our new scheme is compared with other modified S P s existing in the literature by means of simulations. These simulations show the superior performance of our scheme in the sense of removing the steady-state error more efficiently (i.e., faster) than other schemes. Finally, the performance of our control scheme is tested experimentally in a low-cost mobile robot. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality. [ABSTRACT FROM AUTHOR]

Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, hardware-induced delays may produce overshoots and even instability when controlling the system. In this case, Smith predictor controllers can be used because they are well suited for delayed processes. This paper addresses the accurate positioning of a mobile robot on a terrain of an unknown slope. This slope produces disturbance torques of unknown amplitudes in the robot actuators that yield a steady-state error in the positioning. Because our actuators are integrating plus time delay plants, the standard Smith predictor cannot remove these disturbances. This paper proposes a modification of this control scheme in order to remove these disturbances yielding a zero steady-state error in the actuators. Our new scheme is compared with other modified SPs existing in the literature by means of simulations. These simulations show the superior performance of our scheme in the sense of removing the steady-state error more efficiently (i.e., faster) than other schemes. Finally, the performance of our control scheme is tested experimentally in a low-cost mobile robot.

Digital twins have emerged as a powerful concept for real-time monitoring and analysis, facilitating Quality by Design integration into biopharmaceutical manufacturing. Traditionally, lyophilization processes are developed through trial-and-error, incorporating high security margins and inflexible process set points. Digital twins enable the integration of adaptable operating conditions and implementation of automation through Advanced Process Control (APC) with Process Analytical Technology (PAT) and validated physicochemical models that rely on heat and mass transfer principles, allowing us to overcome the challenges imposed by the lyophilization process. In this study, a digital twin for freeze-drying processes is developed and experimentally validated. Using the digital twin, primary drying conditions were optimized for controlled nucleation and annealing methods by carrying out a few laboratory tests beforehand. By incorporating PAT and modeling, the digital twin accurately predicts the product's temperature and drying endpoint, showing smaller errors than the experiments. The digital twin significantly increases productivity by up to 300% while reducing the costs by 74% and the Global Warming Potential by 64%. [ABSTRACT FROM AUTHOR]