The present disclosure relates to the technical field of mechanical precision manufacturing, in particular to a motor tracking error reduction method and an implementation device based on a micro-drive unit. A motor tracking error reduction method based on micro-drive unit includes: providing a motor mover as the working output end, and feeding back the position information of the motor mover to the micro-drive controller in real time by the sensor; controlling the micro-drive unit to compensate the displacement of the motor mover by the micro-drive controller; correcting the tracking error of the motor mover after the displacement compensation, and feeding back the tracking error information after correction to the motor controller. The error reduction method and implementation device in the present disclosure reduce the motor tracking error and solve the problem of coupling interference. In addition, the single position feedback is used to reduce the production cost.

The present disclosure relates to the technical field of mechanical precision manufacturing, in particular to a motor tracking error reduction method and an implementation device based on a micro-drive unit. A motor tracking error reduction method based on micro-drive unit includes: providing a motor mover as the working output end, and feeding back the position information of the motor mover to the micro-drive controller in real time by the sensor; controlling the micro-drive unit to compensate the displacement of the motor mover by the micro-drive controller; correcting the tracking error of the motor mover after the displacement compensation, and feeding back the tracking error information after correction to the motor controller. The error reduction method and implementation device in the present disclosure reduce the motor tracking error and solve the problem of coupling interference. In addition, the single position feedback is used to reduce the production cost.

An initial inspection or critical dimension measurement can be made at various sites on a wafer. The location, design clips, process tool parameters, or other parameters can be used to train a deep learning model. The deep learning model can be validated and these results can be used to retrain the deep learning model. This process can be repeated until the predictions meet a detection accuracy threshold. The deep learning model can be used to predict new probable defect location or critical dimension failure sites.

An initial inspection or critical dimension measurement can be made at various sites on a wafer. The location, design clips, process tool parameters, or other parameters can be used to train a deep learning model. The deep learning model can be validated and these results can be used to retrain the deep learning model. This process can be repeated until the predictions meet a detection accuracy threshold. The deep learning model can be used to predict new probable defect location or critical dimension failure sites.