Atomic Neighborhoods in Semiconductors Provide New Avenue for Designing Microelectronics

The breakthrough moment came when first author Lilian Vogl, who was then a postdoctoral researcher in Minor’s lab, was studying a sample of germanium containing a small amount of tin and silicon using a powerful type of electron microscopy recently pioneered by the group called 4D-STEM. The initial results were too muddled to parse the faint signals from the electrons diffracting off the tin and silicon from the strong signals off the tidily arranged germanium, so she implemented an energy-filtering device on the system to improve contrast. When the next dataset started appearing on her monitor, she quickly realized there was a new kind of result. The faint signals were clearer, and repeating patterns emerged, indicating that the atoms have preferred order after all.

To validate her findings and learn what these patterns meant, Vogl collected more data with the energy-filtering 4D-STEM and used a pre-trained neural network to sort the diffraction images. The tool identified six recurring motifs representing particular atomic arrangements in the sample material, but the Berkeley Lab team still couldn’t determine the exact atomic structures that were generating the motifs. To interpret their experimental results, they turned to µ-Atoms collaborators at George Washington University led by co-lead author Tianshu Li, a professor of Civil and Environmental Engineering.

Li’s team generated a highly accurate and efficient machine-learning potential capable of modeling millions of atoms in the material’s structure, allowing Vogl to perform simulated 4D-STEM on different possible structural arrangements until she found matches for the motifs in the experimental data.

“It’s remarkable that modeling and experiment can work seamlessly to unravel SRO structural motifs for the first time,” said Li, whose team had previously predicted SRO and its impact and helped motivate the current study. “Proving SRO experimentally is not an easy task, let alone identifying its structural motifs. Signals from SRO can easily be obscured by defects or inherent movement of atoms at room temperature, and until now there was no clear way to separate them. This work represents the first step toward our broader goal.”

Shunda Chen, a research scientist in Li’s group who developed the model, said: “With these models, which combine machine learning with first-principles calculations, we can replicate experimental procedures with high fidelity and pinpoint the structural motifs that would otherwise remain hidden.”

Follow-up work initiated by other µ-Atoms members at the University of Arkansas and at Sandia National Laboratories is already yielding insights into how these short range-order motifs affect the semiconductor’s electronic properties, and the scientists hope that manipulating the order to enable new types of devices and processing routes will be possible soon.

“We’re going to be able to really push the boundaries beyond current capabilities by designing semiconductors at the atomic scale,” said Vogl, who is now group leader of the Environmental & Analytical Electron Microscopy Group at the Max Planck Institute for Sustainable Materials. “We are opening the door to a new era of information technology at the atomic scale, unlocking the deterministic placement of SRO motifs for tailoring of band structures that could impact a wide variety of technologies, from topological quantum materials to neuromorphic computing to optical detectors .”

This research was supported by the DOE Office of Science’s µ-Atoms Energy Frontier Research Center. The Molecular Foundry is a DOE Office of Science User Facility.

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