Research Bits: Nov. 15

Low temperature 3D bonding; ultrathin barium titanate capacitor; ML for RHEED analysis.


Low temperature 3D bonding

Scientists from Osaka University developed a new method for the direct three-dimensional bonding of copper electrodes using silver layers. The method works at low temperatures and does not require external pressure.

“Our process can be performed under gentle conditions, at relatively low temperatures and without added pressure, but the bonds were able to withstand over one thousand cycles of thermal shocking from -55 to 125 °C,” said Zheng Zhang of the Flexible 3D System Integration Laboratory (F3D Lab) at Osaka University.

“In this new method, silver is first sputtered onto the two copper surfaces to be bonded at room temperature. Then, heat was applied to anneal the silver layers, which caused the surface to undergo microscopic changes in a process called stress migration,” the researchers explained. “The release of the stress during annealing led to surface roughening, which ensured a sufficient effective area between the two silver layers. As a result, bonding could be accomplished without applied pressure even at a comparative low annealing temperature. Permanent connections as small as 20 micrometers could be realized in just ten minutes this way. This process also requires only moderate temperatures (180 °C) and can work under atmospheric conditions.”

The team was able to confirm the surface roughness of the sputtered and annealed chips using images from scanning electron microscopy and atomic force microscopy. “This technology is expected to contribute to chips with a high density of interconnects and advanced 3D packaging,” said Katsuaki Suganuma of the F3D Lab.

Ultrathin barium titanate capacitor

Researchers from Lawrence Berkeley National Laboratory, University of California Berkeley, and Intel synthesized a thin-film version of barium titanate (BaTiO3) capable of ultra-low-voltage switching.

Crystals of bulk BaTiO3 respond quickly to a small electric field, flip-flopping the orientation of the charged atoms in a reversible but permanent manner even if the applied field is removed. However, it requires voltages larger than 1,000 mV.

“We’ve known about BaTiO3 for the better part of a century and we’ve known how to make thin films of this material for over 40 years. But until now, nobody could make a film that could get close to the structure or performance that could be achieved in bulk,” said Lane Martin, a faculty scientist in the Materials Sciences Division at Berkeley Lab and professor of materials science and engineering at UC Berkeley.

Pulsed-laser deposition was used to make low-defect BaTiO3 thin films that are less than 25nm thick. Firing an ultraviolet laser light onto a ceramic target of BaTiO3 causes the material to transform into a plasma, which then transmits atoms from the target onto a surface to grow the film. “It’s a versatile tool where we can tweak a lot of knobs in the film’s growth and see which are most important for controlling the properties,” said Martin.

By placing a film of BaTiO3 in between two metal layers, the team created tiny capacitors. Applying voltages of 100 mV or less and measuring the current that emerges showed that the film’s polarization switched within two billionths of a second and could potentially be faster.

“This is a good early victory in our pursuit of low-power electronics that go beyond what is possible with silicon-based electronics today,” said Martin.

Next, the plan to reduce the material’s thickness further and test feasibility in first-generation devices.

ML for RHEED analysis

Researchers from the Tokyo University of Science and Japan’s National Institute for Materials Science are exploring using machine learning to automatically analyze reflection high-energy electron diffraction (RHEED) data.

RHEED can be used for structural analysis of semiconductor nanofilms and can capture structural changes in real time as the thin film is being synthesized. However, RHEED output patterns are complex and difficult to interpret, requiring a skilled experimenter to make sense of the data.

“Our efforts will help automate the work that typically requires time-consuming manual analysis by specialists. We believe our study has the potential to change the way materials research is done and allow scientists to spend more time on creative pursuits,” said Naoka Nagamura, a visiting associate professor at Tokyo University of Science and a senior researcher at National Institute for Materials Science.

The researchers focused on the surface superstructures that form on the first atomic layers of clean single-crystal silicon. They first used different hierarchical clustering methods, which are aimed at dividing samples into different clusters based on various measures of similarity. This approach serves to detect how many different surface superstructures are present. They then sought to determine the optimal process conditions for synthesizing each of the identified surface superstructures.

While principal component analysis and other typical methods for dimensionality reduction did not perform well, non-negative matrix factorization, a different clustering and dimensionality reduction technique, could accurately and automatically obtain the optimal deposition times for each superstructure.

“Our approach can be used to analyze the superstructures grown not only on thin-film silicon single-crystal surfaces, but also metal crystal surfaces, sapphire, silicon carbide, gallium nitride, and various other important substrates. Thus, we expect our work to accelerate the research and development of next-generation semiconductors and high-speed communication devices,” said Nagamura.

Leave a Reply

(Note: This name will be displayed publicly)