In recent years, there has been an unprecedented scale of growth in the computer industry as our technological needs continue to expand. The scientific community is actively working to improve computational methods, and two scientific articles have recently caught special attention. These articles are from groups of scientists led by Professor Gene Incordivia from the engineering school of Kokrell. Their studies focus on improving semiconductors and developing new generation computers that operate on the principle of the human brain.
“We are on the threshold of a new era in computing, and the reconstruction of the thinking processes of our brain presents a grand scientific challenge,” emphasizes Incorgia. “At the same time, the current computing methods have limitations, so it is important to continue innovating and transforming devices that support our everyday technologies.”
One of the studies published in acs nano focuses on the modernization of transistor design. Researchers have discovered a way to combine logical valves, which are key elements that process digital signals within microcircuits. The uniqueness of their approach lies in the valves’ ability to control both electrons and holes, which are produced when electrons move within atoms.
“Speaking about the future of computing, if we can utilize the natural behavior of these two-dimensional materials and scale them, we will be able to reduce the number of transistors required in our circuits,” said Incorgia.
This innovation has the potential to increase the efficiency and power of computers by enabling the placement of more transistors in a smaller space. The size of the devices themselves will also be reduced due to the freed-up space.
The other work, published in Applied Physics Letters, is dedicated to the development of a new generation of computers that can “think.” Researchers have created artificial neurons using magnetic materials. These neurons randomly respond to electrical impulses, making them particularly useful for processing noisy data.
Noisy data refers to information that has been distorted or polluted by random changes.
“Since the device itself reacts unpredictably to the input data, it performs better when dealing with noisy information,” explained Incorgius.
Artificial neurons have proven to outperform other neural networks, especially when interpreting images, particularly those that are blurry.
These new technologies have potential applications