Researchers have discovered that atoms within semiconductors can spontaneously organize into distinct, localized patterns known as short-range order (SRO). This atomic self-organization significantly impacts the material's electronic characteristics. The findings, published in the journal *Science* in September 2025, are the result of a collaboration between Lawrence Berkeley National Laboratory (Berkeley Lab) and George Washington University. The study focused on germanium-tin (GeSn) alloys, which are of interest for quantum computing and optoelectronics.
Using advanced 4D scanning transmission electron microscopy (4D-STEM), scientists directly observed these ordered atomic arrangements in GeSn samples, providing the first experimental validation of SRO in semiconductor materials. The precision of 4D-STEM allowed for detailed nanoscale mapping of crystal symmetry, lattice parameters, and strain. To interpret these complex atomic structures, the team collaborated with Tianshu Li's group at George Washington University, who developed a sophisticated machine-learning model capable of simulating millions of atoms. This computational tool enabled a precise correlation between the experimentally observed atomic motifs and specific structural configurations, bridging the gap between theoretical modeling and experimental observation of SRO in GeSn alloys.
Machine learning is increasingly crucial in materials science for predicting properties and accelerating the discovery of new materials by analyzing vast datasets. These discoveries hold significant implications for the future of microelectronic device development. The ability to precisely control SRO offers a pathway to tailor the electronic properties of semiconductors with unprecedented accuracy, paving the way for more efficient and specialized electronic components and marking a pivotal step towards atomic-scale semiconductor design.
The control over SRO could revolutionize the semiconductor industry, potentially leading to reconfigurable transistors and novel electronic structures. The implications of this research extend across various technologies, including quantum materials, neuromorphic computing, and optical detectors. GeSn alloys, in particular, are being explored for their tunable bandgap properties, making them suitable for optoelectronic devices operating in the short-wave infrared (SWIR) and mid-wave infrared (MWIR) spectrums. The research was supported by the U.S. Department of Energy's Office of Science and the Molecular Foundry. Key contributors to the work include Anis Attiaoui, John Lentz, Lilian Vogl, Joseph C. Woicik, Jarod Meyer, Shunda Shen, Kunal Mukherjee, Tianshu Li, Andrew Minor, and Paul McIntyre.