STM Tip Quality Automation
🔬 National Institute of Standards and Technology, U.S. Department of Commerce
Developed a CNN-based system to automate Scanning Tunneling Microscope (STM) tip quality assessment for atom-by-atom quantum device fabrication. This project enhances precision in STM usage by distinguishing sharp and dull tips, enabling automated real-time decisions crucial for high-accuracy atomic manipulation.
The project employs a convolutional neural network (CNN) to classify STM tip sharpness, with custom image processing and contour extraction. Data augmentation techniques bolster model resilience despite a limited dataset. An ROI-based cross-scan algorithm further enhances accuracy, ensuring reliable classifications and forming the foundation for full automation in STM-based fabrication.