The semiconductor industry has been growing steadily ever since its first steps in the mid-twentieth century and, thanks to the high-speed information and communication technologies it enabled, it has given way to the rapid digitalization of society. Today, in line with a tight global energy demand, there is a growing need for faster, more integrated, and more energy-efficient semiconductor devices.

However, modern semiconductor processes have already reached the nanometer scale, and the design of novel high-performance materials now involves the structural analysis of semiconductor nanofilms. Reflection high-energy electron diffraction (RHEED) is a widely used analytical method for this purpose. RHEED can be used to determine the structures that form on the surface of thin films at the atomic level and can even capture structural changes in real time as the thin film is being synthesized!

Unfortunately, for all its benefits, RHEED is sometimes hindered by the fact that its output patterns are complex and difficult to interpret. In virtually all cases, a highly skilled experimenter is needed to make sense of the huge amounts of data that RHEED can produce in the form of diffraction patterns. But what if we could make machine learning do most of the work when processing RHEED data?

One of the most tedious, daunting tasks for undergraduate assistants in university research labs involves looking hours on end through a microscope at samples of material, trying to find monolayers.

These two-dimensional materials —less than 1/100,000th the width of a human hair—are highly sought for use in electronics, photonics, and optoelectronic devices because of their unique properties.

“Research labs hire armies of undergraduates to do nothing but look for monolayers,” says Jaime Cardenas, an assistant professor of optics at the University of Rochester. “It’s very tedious, and if you get tired, you might miss some of the monolayers or you might start making misidentifications.”

From action heroes to villainous assassins, biohybrid robots made of both living and artificial materials have been at the center of many sci-fi fantasies, inspiring today’s robotic innovations. It’s still a long way until human-like robots walk among us in our daily lives, but scientists from Japan are bringing us one step closer by crafting living human skin on robots. The method developed, presented June 9 in the journal Matter, not only gave a robotic finger skin-like texture, but also water-repellent and self-healing functions.

“The finger looks slightly ‘sweaty’ straight out of the culture medium,” says first author Shoji Takeuchi, a professor at the University of Tokyo, Japan. “Since the finger is driven by an electric motor, it is also interesting to hear the clicking sounds of the motor in harmony with a finger that looks just like a real one.”

Looking “real” like a human is one of the top priorities for humanoid robots that are often tasked to interact with humans in healthcare and service industries. A human-like appearance can improve communication efficiency and evoke likability. While current silicone skin made for robots can mimic human appearance, it falls short when it comes to delicate textures like wrinkles and lacks skin-specific functions. Attempts at fabricating living skin sheets to cover robots have also had limited success, since it’s challenging to conform them to dynamic objects with uneven surfaces.