In 1971, a physicist conceptualized the existence of a fourth fundamental element in the electronic circuit, besides the three that were already in use at the time. His name was Leon Chua and he believed -- for reasons of symmetry -- that an extra component could one day be constructed to join the resistor, the capacitor and the inductor. He called it "memristor", a portmanteau of the words memory and resistor. It took 37 years for our engineering abilities to catch up with that idea: the first memristor was built by Hewlett Packard in 2008. And today, many researchers believe it could spark a revolution in computing. |
Simply put, the memristor could mean the end of electronics as we know it and the beginning of a new era called "ionics". The transistor, developed in 1947, is the main component of computer chips. It functions using a flow of electrons, whereas the memristor couples the electrons with ions, or electrically charged atoms. In a transistor, once the flow of electrons is interrupted by, say, cutting the power, all information is lost. But a memristor can remember the amount of charge that was flowing through it, and much like a memory stick it will retain the data even when the power is turned off. |
Like a brain
Initially, the technology will be mostly used to create super-fast memory chips that contain more data and consume less energy. This alone would make regular computers much more powerful, but down the line, the memristor could also take on the processing.
Jennifer Rupp is a Professor of electrochemical materials at ETH Zurich, and she's working with IBM to build a memristor- based machine. Memristors, she points out, function in a way that is similar to a human brain: "Unlike a transistor, which is based on binary codes, a memristor can have multi-levels. You could have several states, let's say zero, one half, one quarter, one third, and so on, and that gives us a very powerful new perspective on how our computers may develop in the future," she told CNN's Nick Glass. Such a shift in computing methodology would allow us to create "smart" computers that operate in a way reminiscent of the synapses in our brains. Free from the limitations of the 0s and 1s, these more powerful computers would be able to learn and make decisions, ultimately getting us one step closer to creating human-like artificial intelligence.
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