Researchers are always searching for improved technologies, but the most efficient computer possible already exists. It can learn and adapt without needing to be programmed or updated. It has nearly limitless memory, is difficult to crash, and works at extremely fast speeds. It’s not a Mac or a PC; it’s the human brain. And scientists around the world want to mimic its abilities.
Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons.
“Computers are very impressive in many ways, but they’re not equal to the mind,” said Mark Hersam, the Bette and Neison Harris Chair in Teaching Excellence in Northwestern University’s McCormick School of Engineering. “Neurons can achieve very complicated computation with very low power consumption compared to a digital computer.”
A team of Northwestern researchers, including Hersam, has accomplished a new step forward in electronics that could bring brain-like computing closer to reality. The team’s work advances memory resistors, or “memristors,” which are resistors in a circuit that “remember” how much current has flowed through them.
The research is described in the April 6 issue of Nature Nanotechnology. Tobin Marks, the Vladimir N. Ipatieff Professor of Catalytic Chemistry, and Lincoln Lauhon, professor of materials science and engineering, are also authors on the paper. Vinod Sangwan, a postdoctoral fellow co-advised by Hersam, Marks, and Lauhon, served as first author. The remaining co-authors–Deep Jariwala, In Soo Kim, and Kan-Sheng Chen–are members of the Hersam, Marks, and/or Lauhon research groups.
“Memristors could be used as a memory element in an integrated circuit or computer,” Hersam said. “Unlike other memories that exist today in modern electronics, memristors are stable and remember their state even if you lose power.”
“A three-terminal memristor has been proposed as a means of realizing brain-like computing. We are now actively exploring this possibility in the laboratory.”
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