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This article reports on the design, analysis and control of a new type of wheeled mobile robot based on a nonholonomic spherical continuously variable transmission (S-CVT). Our S-CVT based mobile robot is designed to increase the run time (i.e., the length of time in which the robot can be operated), and to achieve full planar accessibility with the design of a novel pivoting device that takes advantage of the flexibility of the S-CVT. We examine the sources of power loss in the S-CVT, in particular spin loss. For a quantitative analysis of spin loss of the S-CVT, we develop a friction model for the S-CVT, and perform an in-depth contact analysis based on the relative velocity field and normal pressure distribution. We also present a nonlinear shifting controller based on feedback linearization that takes into account the dynamics of the S-CVT. To evaluate the energy efficiency of our mobile robot and the performance of the S-CVT a

Plasma information-based virtual metrology (PI-VM) and mass production process control

© 2022, The Korean Physical Society.In this paper, we review the development of plasma engineering technology that improves dramatically the production efficiency of OLED (organic light-emitting diode) displays and semiconductor manufacturing by utilizing a process monitoring methodology based on the physical domain knowledge. The domain knowledge consists of plasma-heating and sheath physics, plasma chemistry and plasma-material surface reaction kinetics, and plasma diagnostics. Based on this, a plasma information-based virtual metrology (PI-VM) algorithm was developed drastically enhanced process prediction performance by parameterizing plasma information (PI) which can trace the states of processing plasmas. PI-VM has superior process prediction accuracy compared to the classical statistics-based virtual metrologies. The developed PI-VM algorithms adopted for practical processing issues such as the control and management of the OLED-display mass production demonstrated savings of approximately 25% of the yield loss over the past 5 years. This improvement was achieved with the development of FDC (fault detection and classification) and APC (advanced process control) logic, which can be developed through the analysis of the physical characteristics of the feature parameters used in PI-VM with the evaluation of their contributions and their correlations to the processing results. PI-VM provides leverage that can be applied in the development of process equipment and factory automation technologies.N