AI Breaks Fundamental Limitations of Atomic Force Microscopy

AI technique that significantly improves Atomic Force Microscopy


An artificial intelligence (AI) method developed by researchers at the University of Illinois at Urbana-Champaign allows AFM to see material characteristics smaller than the tip of the probe, greatly enhancing AFM. This discovery might transform the fields of material science and nanoelectronics development by providing the first full three-dimensional profiles that go beyond traditional resolution limitations.

Atomic force microscopy, or AFM, is a commonly used approach for quantitatively mapping material surfaces in three dimensions. Fortunately, the precision of AFM is limited by the size of the microscope’s probe. A novel artificial intelligence method has been developed to get over this restriction and allow higher-resolution material study using microscopes.

A deep learning algorithm was created by scientists at the University of Illinois at Urbana-Champaign to remove probe width effects from images taken with an AFM microscope. The work, which was published in the journal Nano Letters, shows that the algorithm outperforms other methods in providing the first true three-dimensional surface profiles at resolutions lower than the width of the microscope probe tip.

Innovation in Material Surface Imaging

According to Yingjie Zhang, a materials science and engineering professor at the University of Illinois and the project lead, “accurate surface height profiles are crucial to nanoelectronics development as well as scientific studies of material and biological systems, and AFM is a key technique that can measure profiles noninvasively.” “We’ve shown how to use AI to overcome seemingly insurmountable limitations, as well as how to be even more accurate and see things that are even smaller.”

The majority of the time, researchers using microscopy techniques are only able to get two-dimensional pictures, which is equivalent to aerial shots of material surfaces. Complete topographical maps with precise height profiles of the surface features are provided by AFM. By advancing a probe across the material’s surface and measuring its vertical deflection, these three-dimensional pictures are produced.

Surface features cannot be resolved by a microscope if they get close to the probe’s tip size, which is around 10 nanometers. This is because the probe becomes too big to “feel out” the features. Although this constraint has been known to microscopists for decades, researchers from the University of Illinois are the first to provide a deterministic solution.

The study’s primary author, graduate student Lalith Bonagiri, of Zhang’s group, stated, “We turned to AI and deep learning because we wanted to get the height profile – the exact sharpness—without the inherent restrictions of more conventional mathematical methods.”

Deep Learning Methodology

The team created an encoder-decoder architecture and a deep learning system. By breaking down raw AFM pictures into abstract characteristics, it first “encodes” the images. The feature representation is “decoded” back into a recognisable image after being altered to eliminate the undesirable effects.

The scientists created synthetic pictures of three-dimensional objects and mimicked their AFM readouts in order to train the algorithm. Next, an algorithm was built to extract the underlying features and convert the simulated AFM pictures to probe-size effects.

To be able to do this, we were actually forced to take an unconventional step, according to Bonagiri. Expanding the brightness and contrast of the pictures in relation to a standard makes comparisons easier. This is the initial stage in most AI image processing processes. However, the element that matters in our instance is the absolute brightness and contrast, so we had to skip that initial stage. Because of this, the issue became considerably more difficult.

The scientists created known-dimension gold and palladium nanoparticles on a silicon host in order to test their method. The programme properly detected the three-dimensional properties of the nanoparticles and successfully eliminated the impacts of the probe tip.

“This work is just the beginning, but we’ve demonstrated a proof-of-concept and demonstrated how to use AI to significantly improve AFM images,” Zhang stated. “What it can do is clear, yet we can make it better by training it on more and better data, as we can with all AI algorithms.”


The innovative AI methodology built up by a group of researchers at the University of Illinois at Urbana-Champaign has dramatically altered atomic force microscopy (AFM), prevailing the traditional restrictions imposed by the probe’s size. By leveraging deep learning algorithms, researchers can now acquire true three-dimensional surface profiles at resolutions that exceed the diameter of the microscope’s probe tip. This discovery holds massive promise for advancing material science and nano-electronics development and generating unknown insights into material characteristics on a nanoscale level.


Q: What is atomic force microscopy (AFM)?

A: With nanometer-scale resolution, AFM is a potent tool for quantitatively mapping material surfaces in three dimensions. It operates by running a pointed probe across a sample’s surface and measuring the forces that interact with the sample.

Q: What are the limitations of traditional AFM?

A: The probe tip of a standard AFM microscope, which is normally about 10 nanometers in size, limits the accuracy of the instrument. The study of nanoscale materials is hampered by this constraint, which limits the capacity to discern surface characteristics smaller than the probe tip.

Q: How does the AI method developed by researchers overcome these limitations?

A: Deep learning algorithms are used in the AI technique created by University of Illinois Urbana-Champaign researchers to eliminate probe width effects from AFM pictures. The programme can produce accurate three-dimensional surface profiles at resolutions less than the width of the microscope probe tip by encoding and decoding picture characteristics.

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