A correlation-based optimization model to recover lost and distorted data from scanning tunneling microscopy images...

Area of Science:

• Nanoscience and Nanotechnology
• Surface Science
• Materials Science

Background:

• Scanning tunneling microscopy (STM) is crucial for nanoscience but susceptible to tip effects and thermal drift, distorting image data.
• Accurate STM imaging requires precise parameter extraction, which is challenging due to experimental artifacts.

Purpose of the Study:

• To develop a physics-guided optimization model for extracting STM imaging parameters.
• To address data loss and distortion caused by tip shape and thermal drift in STM images.
• To enhance the speed and accuracy of STM data acquisition.

Main Methods:

• A physics-guided optimization model was developed using partial charge densities from density functional theory (DFT) simulations.
• Image analysis employed a two-dimensional Pearson correlation for mass comparison between experimental and simulated STM images.
• Gaussian functions were utilized to model and compensate for tip-induced blurring effects.

Main Results:

• The model achieved over 96% correlation for Si(111)-7 × 7 reconstruction images across both biases.
• Tip effect correction recovered 1.5-6% of data lost due to blurring.
• Thermal drift was detected and corrected, mitigating significant data distortion (approx. 19% in negative bias images).

Conclusions:

• The proposed model effectively extracts STM imaging parameters, significantly improving image fidelity.
• It offers a solution for data loss and distortion, enhancing the reliability of STM results.
• The model enables faster microscopy by eliminating the need to slow down scanning to counteract thermal drift.