For almost 70 years, transistors have formed the backbone of computer technology. They’re used in microprocessors to perform logic-based operations and in memory chips to store information. Modern microchips contain billions of transistors and are capable of processing enormous amounts of information, which has made them the hardware platform of choice for sophisticated artificial intelligence systems.
However, the exponential growth of data in recent years has highlighted flaws in traditional computer architecture. The hardware that we have now for artificial intelligence is based on complementary metal oxide semiconductor technology (CMOS) technology. This hardware has an important limitation: the continuous exchange of data between one microchip and one memory chip is slow, produces a lot of heat, and uses a lot of power.
But now there’s a new type of chip on the block that uses memristors. A mashup of the words “memory” and “resistor,” memristors allow for both memory and processing functions within the same device. By arranging multiple memristors in crossbar arrays (stacked grids), it’s possible to perform matrix multiplication, a key mathematical operation central to deep learning algorithms. This increases computing speed because data doesn’t have to travel back and forth between the processor and memory components, thereby making AI computing more efficient.
Mario Lanza, Associate Professor of Material Science and Engineering, at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, is a world leader in memristor technology research. Memristive memories have already cornered a small portion of the total memory market. “This type of memory is a hybrid—it’s not the fastest or slowest, it doesn’t have the lowest power consumption or highest,” Mario explains. “The properties can be customised depending on the material that we use for these memristors. It’s being used right now for some low-power applications, like devices that need to store some data for the Internet of Things.” He’s collaborating with industry leaders to capitalise on the commercial potential of memristors. In a recent article published in Science, Mario also explains that, “memristors are in fact very versatile electronic devices that find application in many different fields, including memory, computation for artificial neural networks, mobile communication (like 6G), and data encryption.”
Mario is also an expert on two-dimensional materials—nanomaterials only one or two atoms thick (a strand of hair is hundreds of thousands of atoms wide.) He explains, “The most famous 2D material is graphene, but I use another one called white graphene, which is hexagonal boron nitride—the only 2D insulator—and we use this to make memristors.” He’s establishing a system to grow hexagonal boron nitride in the lab to eliminate the problem of batch-to-batch variability often found with commercial suppliers. With this material, he is creating crossbar arrays of memristors with advanced performance, which have been published in top journals like Nature Electronics several times.
The excellent facilities at KAUST, comparable to those found in top research institutions worldwide, allow Mario to employ many different techniques to optimise his research. This in turn makes it possible for his team to meet high-quality standards. “I’m lucky, I have everything here. I even have access to one of the most powerful supercomputers in the world to do atomic-scale simulations,” he says. These resources enable him to be more productive and participate in interdisciplinary collaborations with colleagues both at KAUST and abroad.
Another major advantage of working at KAUST is the guaranteed baseline funding which covers salaries and all the expenses associated with running a research group. “Professors around the world spend more than 80 percent of their time writing proposals to get funding. I can use that time to do what I like, which is to learn. Since I came here, I’ve enjoyed extremely deep discussions, and my papers are improving a lot because I can add significantly more data and explanations than before,” Mario says.
Before he came to KAUST, Mario had several offers on the table but chose KAUST because of everything it has to offer. He says, “KAUST is like a high-performance center for scientists. You are surrounded by top scientists and amazing facilities, as well as convenient services that save us time. Almost all KAUST professors that I know have experienced significant growth in their number of publications, average impact factor, and number of citations since joining the university. Coming to KAUST is almost a warranty of fast progress. It’s an ideal place to work.”
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Mario Lanza is an Associate Professor of Material Science and Engineering at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He is a world leader in memristor technology research.
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