Industries are increasingly adopting automatic and intelligent manufacturing in production lines, such as those of semiconductor wafers, optoelectronic devices, and light-emitting diodes. For example, automatic robot arms have been used for pick-and-place workpiece applications. However, repairing automatic robot arms is time-consuming and increases the downtime of equipment and the cycle time of manufacturing. In this study, various machine learning (ML) models, such as the general linear model (GLM), random forest, extreme gradient boosting, gradient boosting machine, and stacked ensemble, were used to predict the maximum Cartesian positioning shift (i.e. the maximum eccentric distance) in the next handling time period (e.g. 1 min). A charge-coupled-device-based fault diagnostic system was developed to measure the critical positions of the robotic arm when transferring wafers. A novel data augmentation method was used to determine the correlation parameters in the dataset for the ML models. The prediction error for each algorithm was determined using the root mean square error (RMSE). The results revealed that the GLM exhibited the lowest prediction errors. The RMSEs of the GLM were 0.024, 0.032, and 0.046 mm for 3421 pickups, the last 1000 pickups, and 100 pickups, respectively, for the prediction target. Thus, the GLM is a promising model for predicting the task failure of wafer-handling robotic arms.

工业界越来越多地在生产线中采用自动和智能制造，例如半导体晶片，光电器件和发光二极管的生产线。例如，自动机械臂已用于拾取和放置工件应用。但是，修理自动机械臂很费时间，并且会增加设备的停机时间和制造周期。在这项研究中，各种机器学习（ML）模型（例如通用线性模型（GLM），随机森林，极限梯度提升，梯度提升机和堆叠集成）用于预测最大笛卡尔定位位移（即最大下一个处理时间段（例如1分钟）内的“偏心距”。开发了基于电荷耦合器件的故障诊断系统，以测量转移晶圆时机械臂的关键位置。一种新颖的数据扩充方法用于确定ML模型的数据集中的相关参数。使用均方根误差（RMSE）确定每种算法的预测误差。结果表明，GLM表现出最低的预测误差。对于3421个拾取器，GLM的RMSE分别为0.024、0.032和0.046毫米，对于预测目标，最后一个拾取器和100个拾取器分别为0.024、0.032和0.046毫米。因此，GLM是用于预测晶圆处理机械臂的任务失败的有前途的模型。使用均方根误差（RMSE）确定每种算法的预测误差。结果表明，GLM表现出最低的预测误差。对于3421个拾取器，GLM的RMSE分别为0.024、0.032和0.046毫米，对于预测目标，最后一个拾取器和100个拾取器分别为0.024、0.032和0.046毫米。因此，GLM是用于预测晶圆处理机械臂的任务失败的有前途的模型。使用均方根误差（RMSE）确定每种算法的预测误差。结果表明，GLM表现出最低的预测误差。对于3421个拾取器，GLM的RMSE分别为0.024、0.032和0.046毫米，对于预测目标，最后一个拾取器和100个拾取器分别为0.024、0.032和0.046毫米。因此，GLM是用于预测晶圆处理机械臂的任务失败的有前途的模型。