Autofocus control using adaptive region selection and reinforcement learning applied in wafer micro-imaging

We propose a generalized reinforcement learning (RL) approach for personalized autofocus control in wafer micro-imaging, aiming to address the issue of inconsistent focal distances across different wafer regions. Our method integrates region selection with focus control by creating a deep network that estimates focal distances based on the current image frame. Through multiple rounds of image capture and evaluation in the RL framework, the network is fine-tuned to develop personalized models that predict optimal focal distances for interest regions based on engineer feedback. The Gaussian policy gradient algorithm is used to update the model's policy network during the fine-tuning process. To validate our approach, we constructed a dataset of wafer images captured at varying focal distances for training and prediction. Experimental results show that our network not only resolves the limited generalization of focus adjustment algorithms across regions but also achieves an average improvement of approximately 4.0% in focusing quality. This method eliminates the need for manual focus adjustment and region selection in wafer inspection, offering new insights for improving wafer micro-imaging quality.