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 pick-up 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 pick-up results into success and failure. In addition, the optimal cost parameter $C$ as well as the Gaussian kernel function parameter gamma $(\gamma)$ 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.

中文翻译：

---

用支持向量机模型优化Micro-LEDs的胶印传质

在这项工作中，通过支持向量机（SVM）模型优化了微型发光二极管（micro-LED）的粘合剂印章传质过程。拾取实验已经重复进行了数百次，从中提取分离速度和印章与供体基板之间的力作为信号特征。具有高斯核函数的 SVM 模型旨在将拾取结果分为成功和失败。此外，最优成本参数 $C$ 以及高斯核函数参数 gamma $(\gamma)$ 经过优化，通过粒子群优化（PSO）算法改进了分类。最后，基于 SVM 模型实现了 85% 的分类准确率，这意味着在未来的工作中需要对信号特征进行更复杂的定义。