How Southampton Scientists Built Atomically Flat Material Stacks

Scientists at the University of Southampton and the National University of Singapore have developed a new fabrication technique for building ultra-thin, atomically flat materials that could accelerate advances in quantum computing and nanoelectronics. The team reports replacing conventional synthetic polymers with the natural mineral muscovite, or mica, to assemble layers of two-dimensional materials, eliminating microscopic residues that previously contaminated and disrupted device performance. “Our new method allows us to precisely align the layers to create these complex structures that were previously difficult to make,” said Dr. Makars Šiškins, a Lecturer in Experimental Physics at the University of Southampton. This precision is crucial, the researchers say, because stacking 2D materials like graphene and hexagonal boron nitride at controlled angles can unlock entirely new properties.

Mica Fabrication Replaces Polymers for 2D Heterostructures

The pursuit of pristine 2D material stacking has led researchers to abandon conventional polymer-based manufacturing in favor of a natural alternative. Microscopic residues from polymers consistently contaminate these delicate structures, hindering the performance of advanced electronic devices. This shift addresses a critical challenge in nanotechnology, where even trace contamination can significantly impact experimental results and device functionality. Replacing synthetic polymers with mica not only creates cleaner surfaces but also streamlines the fabrication process, potentially reducing manufacturing costs. The ability to meticulously control the stacking angle between layers is particularly significant, as it unlocks entirely new material properties.

According to co-lead Prof. Alexey Berdyugin from the National University of Singapore, the inorganic crystalline structure of mica inherently avoids the contamination issues associated with softer polymers, allowing electronic components to operate at peak efficiency. “Because mica is an inorganic crystal, rather than a soft polymer, it avoids many of the contamination issues that plague conventional methods,” he stated, emphasizing the material’s potential to realize the full capabilities of 2D heterostructure electronics and drive advancements in both fundamental science and quantum technology.

When 2D materials, like graphene and hexagonal boron nitride, are stacked into layered structures with controlled angle between the layers, they exhibit entirely new properties – from exotic superconductivity to tunable magnetism.

Dr. Makars Šiškins, Lecturer in Experimental Physics at the University of Southampton

The pursuit of increasingly sophisticated quantum devices hinges on the ability to fabricate materials with atomic-scale precision, yet conventional layering techniques introduce contaminants that severely limit performance. Existing methods rely on synthetic polymers to facilitate stacking of two-dimensional materials, but these leave behind microscopic residues that disrupt the delicate electronic properties of these ultrathin structures. The team’s innovation centers on replacing the problematic polymers with mica, an inorganic crystal that inherently provides cleaner surfaces for material assembly. These engineered stacks exhibit phenomena such as exotic superconductivity and tunable magnetism, according to Dr. Šiškins. The researchers published their findings in Nature Communications, outlining a pathway toward faster, more reliable microchips and significant breakthroughs in both fundamental science and future quantum technology.

Because mica is an inorganic crystal, rather than a soft polymer, it avoids many of the contamination issues that plague conventional methods.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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