Current transistors in computer chips must be miniaturised to the size of only few nanometres, and analysing and storing unprecedented amounts of data will require huge amounts of energy. Sayani Majumdar, from the Aalto University in Finland, along with her colleagues, designed and fabricated the basic building blocks of “neuromorphic” computers inspired by the human brain. “The technology and design of neuromorphic computing is advancing more rapidly than its rival revolution, quantum computing,” said Majumdar. “The key is to achieve the extreme energy-efficiency of a biological brain and mimic the way neural networks process information through electric impulses,” she said.
In a study published in the journal Advanced Functional Materials, researchers showed how they have fabricated a new breed of ‘ferroelectric tunnel junctions’, that is, few- nanometre-thick ferroelectric thin films sandwiched between two electrodes. They have abilities beyond existing technologies and bode well for energy-efficient and stable neuromorphic computing. The junctions work at low voltages of less than five volts and with a variety of electrode materials – including silicon used in chips in most of our electronics. They also can retain data for more than 10 years without power and be manufactured in normal conditions. “Our junctions are made out of organic hydrocarbon materials and they would reduce the amount of toxic heavy metal waste in electronics,” said Majumdar. “We can also make thousands of junctions a day in room temperature without them suffering from the water or oxygen in the air,” she said.
“What we are striving for now, is to integrate millions of our tunnel junction memristors into a network on a one square centimetre area,” she added. “We can expect to pack so many in such a small space because we have now achieved a record-high difference in the current between on and off-states in the junctions and that provides functional stability,” Majumdar said. “The memristors could then perform complex tasks like image and pattern recognition and make decisions autonomously,” she said.
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