In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pickup experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pickup results into success and failure. In addition, the optimal cost parameter C as well as the Gaussian kernel function parameter gamma (γ) has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work. INDEX TERMS Adhesive stamp, mass-transfer, micro-LEDs, support vector machine model, particle swarm optimization.
Heterogeneous ice nucleation is one of the most common and important process in the physical environment. AgI has been proved to be an effective ice nucleating agent in the process of ice nucleation. However, the microscopic mechanism of AgI in heterogeneous ice nucleation has not been fully understood.Molecular dynamics simulations are applied to investigate the ability of which kinds of -AgI substrate can promote ice nucleation by changing the dipole of -AgI on the substrate, we conclude that the dipole of -AgI on the substrate can affect the conformation of ice nucleation. The surface ions with positive charge on the substrate may promote ice nucleation, while there is no ice nucleation founded on the surface ions with negative charge. -AgI substrates affect ice nucleation through adjust the orientations of water molecules near the surfaces.
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