Research Bits: March 6

2D TMDs on silicon; AI finds new nanostructures; printing liquid metal circuits.


2D TMDs on silicon

Engineers at MIT, University of Texas at Dallas, Institute for Basic Science, Sungkyunkwan University, Washington University in St. Louis, University of California at Riverside, ISAC Research, and Yonsei University found a way to grow 2D materials on industry-standard silicon wafers while preserving their crystalline form.

Using a new “nonepitaxial, single-crystalline growth” method, the team fabricated a simple functional transistor from a type of 2D materials called transition-metal dichalcogenides, or TMDs, which are known to conduct electricity better than silicon at nanometer scales.

“We expect our technology could enable the development of 2D semiconductor-based, high-performance, next-generation electronic devices,” said Jeehwan Kim, associate professor of mechanical engineering at MIT. “We’ve unlocked a way to catch up to Moore’s Law using 2D materials.”

2D materials can be grown of wafers of sapphire, which has a hexagonal structure. Silicon lacks that structure, which acts as a supporting scaffold, leading to a random patchwork of crystals that merge haphazardly, forming numerous grain boundaries that stymie conductivity.

“It’s considered almost impossible to grow single-crystalline 2D materials on silicon,” Kim said. “Now we show you can. And our trick is to prevent the formation of grain boundaries.”

Instead of manually exfoliating an atom-thin flake from a bulk material, the researchers used conventional vapor deposition methods to pump atoms across a silicon wafer. The atoms eventually settle on the wafer and nucleate, growing into two-dimensional crystal orientations. To prevent them from growing in random orientations across the silicon wafer and instead align each growing crystal to create single-crystalline regions across the entire wafer, they first covered a silicon wafer in a silicon dioxide mask that they patterned into tiny pockets, each designed to trap a crystal seed. Across the masked wafer, they then flowed a gas of atoms that settled into each pocket to form a 2D TMD. The mask’s pockets corralled the atoms and encouraged them to assemble on the silicon wafer in the same, single-crystalline orientation.

By depositing atoms on a wafer coated in a “mask” (top left), MIT engineers can corral the atoms in the mask’s individual pockets (center middle), and encourage the atoms to grow into perfect, 2D, single-crystalline layers (bottom right). (Credit: Courtesy of the researchers. Edited by MIT News.)

With their masking method, the team fabricated a simple TMD transistor and showed that its electrical performance was just as good as a pure flake of the same material.

They also applied the method to engineer a multilayered device. After covering a silicon wafer with a patterned mask, they grew one type of 2D material to fill half of each square, then grew a second type of 2D material over the first layer to fill the rest of the squares. The result was an ultrathin, single-crystalline bilayer structure within each square. Kim said that going forward, multiple 2D materials could be grown and stacked together in this way to make ultrathin, flexible, and multifunctional films.

Printing liquid metal circuits

Researchers from Tianjin University developed a method to print functional liquid metal circuits onto a wide variety of items and surfaces using a desktop laser printer.

To create the circuits, the researchers printed out a connected design onto heat-transferrable thermal paper with an ordinary laser printer. The printer laid down a carbon-based toner, which was transferred to a pane of glass by heating it. These toner patterns roughened the surface and created a hydrophobic gap of air between the carbon and the liquid metal. This prevented the metal from sticking when brushed on top, so the electronic ink-based pattern only adhered on the exposed parts of the surface.

This circuit could then be stuck directly to a smooth surface, such as a plastic soda bottle. If the surface was too uneven, like the bumpy skin of an orange, the device was first placed on a piece of flexible plastic, then onto the rougher surface.

Regardless of how they were attached, the simple electronics all functioned as intended on their various substrates, with functions including displaying images, to RFID tagging, to sensing temperature and sound. The researchers hope the method will expand the applications of liquid metal circuits.

AI finds new nanostructures

Scientists at Brookhaven National Laboratory and Lawrence Berkeley National Laboratory used an AI-driven technique to discover new self-assembling nanostructures, including a novel ladder-like structure.

“Self-assembly can be used as a technique for nanopatterning, which is a driver for advances in microelectronics and computer hardware,” said Gregory Doerk, a scientists at Brookhaven’s Center for Functional Nanomaterials (CFN). “These technologies are always pushing for higher resolution using smaller nanopatterns. You can get really small and tightly controlled features from self-assembling materials, but they do not necessarily obey the kind of rules that we lay out for circuits, for example. By directing self-assembly using a template, we can form patterns that are more useful.”

“The fact that we can now create a ladder structure, which no one has ever dreamed of before, is amazing,” added Kevin Yager, CFN group leader. “Traditional self-assembly can only form relatively simple structures like cylinders, sheets, and spheres. But by blending two materials together and using just the right chemical grating, we’ve found that entirely new structures are possible.”

The AI framework can autonomously define and perform all the steps of an experiment and was used to find the right combination of parameters in the self-assembly process to create new and useful structures. To accelerate materials discovery using their new algorithm, the team first developed a complex sample with a spectrum of properties for analysis. Researchers fabricated the sample using the CFN nanofabrication facility and carried out the self-assembly in the CFN material synthesis facility.

“An old school way of doing material science is to synthesize a sample, measure it, learn from it, and then go back and make a different sample and keep iterating that process,” Yager said. “Instead, we made a sample that has a gradient of every parameter we’re interested in. That single sample is thus a vast collection of many distinct material structures.”

Masa Fukuto, a scientist at Brookhaven’s National Synchrotron Light Source II, explained how ultrabright x-rays were used: “By analyzing how these microbeam x-rays get scattered by the material, we learn about the material’s local structure at the illuminated spot. Measurements at many different spots can then reveal how the local structure varies across the gradient sample. In this work, we let the AI algorithm pick, on the fly, which spot to measure next to maximize the value of each measurement.”

The algorithm identified three key areas in the complex sample for the researchers to study more closely. They used electron microscopy to image those key areas in fine detail, uncovering the rails and rungs of a nanoscale ladder and other novel features.

From start to finish, the experiment ran about six hours. The researchers estimate they would have needed about a month to make this discovery using traditional methods.

“Autonomous methods can tremendously accelerate discovery,” Yager said. “It’s essentially ‘tightening’ the usual discovery loop of science, so that we cycle between hypotheses and measurements more quickly. Beyond just speed, however, autonomous methods increase the scope of what we can study, meaning we can tackle more challenging science problems.”

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