Deep learning for automated focus quality detection in wafer inspection
Abstract
Scanning Electron Microscopes (SEM) and Dual Beam Focused Ion Beam Microscopes (FIB-SEM) are essential tools used in the semiconductor industry and in relation to this work, for wafer inspection in the production of hard drives at Seagate. These microscopes provide essential metrology during the build and help determine process bias and control. However, these microscopes will naturally drift out of focus over time, and if not immediately detected the consequences of this include: incorrect measurements, scrap, wasted resources, tool down time and ultimately delays in production.
This paper presents an automated solution that uses deep learning to remove anomalous images and determine the degree of blurriness for SEM and FIB-SEM images. Since its first deployment, the first of its kind at Seagate, it has replaced the need for manual inspection on the covered processes and mitigated delays in production, realizing return on investment in the order of millions of US dollars annually in both cost savings and cost avoidance.
The proposed solution can be broken into two deep learning steps. First, we train a deep convolutional neural network, a RetinaNet object detector, to detect and locate a Region Of Interest (ROI) containing the main feature of the image. For the second step, we train another deep convolutional neural network using the ROI, to determine the sharpness of the image. The second model identifies focus level based on a training dataset consisting of synthetically degraded in- focus images, based on work by Google Research, achieving up to 99.3% test set accuracy.
This paper presents an automated solution that uses deep learning to remove anomalous images and determine the degree of blurriness for SEM and FIB-SEM images. Since its first deployment, the first of its kind at Seagate, it has replaced the need for manual inspection on the covered processes and mitigated delays in production, realizing return on investment in the order of millions of US dollars annually in both cost savings and cost avoidance.
The proposed solution can be broken into two deep learning steps. First, we train a deep convolutional neural network, a RetinaNet object detector, to detect and locate a Region Of Interest (ROI) containing the main feature of the image. For the second step, we train another deep convolutional neural network using the ROI, to determine the sharpness of the image. The second model identifies focus level based on a training dataset consisting of synthetically degraded in- focus images, based on work by Google Research, achieving up to 99.3% test set accuracy.
Research Areas
