Deep Learning-Guided Surface Characterization for Autonomous Hydrogen Lithography

TL;DR

This paper introduces a deep learning-based automation method for defect detection on hydrogen-terminated silicon surfaces, enabling precise atomic lithography with minimal user intervention.

Contribution

It presents a convolutional neural network approach to identify and differentiate surface defects, improving the automation and accuracy of atomic-scale hydrogen lithography.

Findings

High accuracy defect detection on silicon surfaces

Reduced user intervention in atomic lithography

Effective avoidance of charged defects and edges

Abstract

As the development of atom scale devices transitions from novel, proof-of-concept demonstrations to state-of-the-art commercial applications, automated assembly of such devices must be implemented. Here we present an automation method for the identification of defects prior to atomic fabrication via hydrogen lithography using deep learning. We trained a convolutional neural network to locate and differentiate between surface features of the technologically relevant hydrogen-terminated silicon surface imaged using a scanning tunneling microscope. Once the positions and types of surface features are determined, the predefined atomic structures are patterned in a defect-free area. By training the network to differentiate between common defects we are able to avoid charged defects as well as edges of the patterning terraces. Augmentation with previously developed autonomous tip shaping and patterning modules allows for atomic scale lithography with minimal user intervention.