Boosting computing by way of the use of perovskite nanocrystals.
Even with the enhancements in technology, the human brain stays outstanding to desktops in quite a few techniques. Though computers can complete mathematical calculations quicker than people, the human brain is capable of processing complex sensory information and facts and adapting to new experiences with ease. This means is continue to over and above the get to of personal computers, and the human brain accomplishes this feat although consuming only a portion of the vitality demanded by a laptop.
The construction of the mind contributes substantially to its electricity efficiency. In contrast to computer systems, wherever memory and processing are individual entities and data desires to be transferred amongst them, the neurons and synapses in the mind are able of both equally storing and processing information at the same time. This gets rid of the need for information to be consistently transported, which can cause slowdowns in computer systems when handling large quantities of information.
One particular achievable option to this bottleneck is novel laptop architectures that are modeled on the human mind. To this conclusion, experts are producing so-named memristors: elements that, like brain cells, merge details storage and processing.
A staff of scientists from the Swiss Federal Laboratories for Resources Science and Technological innovation (Empa), ETH Zurich, and the “Politecnico di Milano” has now created a memristor that is more potent and a lot easier to manufacture than its predecessors. The researchers have lately posted their results in the journal
The researchers conducted the experimental part of the study entirely at Empa: They manufactured the thin-film memristors at the Thin Films and Photovoltaics laboratory and investigated their physical properties at the Transport at Nanoscale Interfaces laboratory. Based on the measurement results, they then simulated a complex computational task that corresponds to a learning process in the visual cortex in the brain. The task involved determining the orientation of light based on signals from the retina.
“As far as we know, this is only the second time this kind of computation has been performed on memristors,” says Maksym Kovalenko, professor at ETH Zurich and head of the Functional Inorganic Materials research group at Empa. “At the same time, our memristors are much easier to manufacture than before.”
This is because, in contrast to many other semiconductors, perovskites crystallize at low temperatures. In addition, the new memristors do not require the complex preconditioning through the application of specific voltages that comparable devices need for such computing tasks. This makes them faster and more energy-efficient.
Complementing rather than replacing
The technology, though, is not quite ready for deployment yet. The ease with which the new memristors can be manufactured also makes them difficult to integrate with existing computer chips: Perovskites cannot withstand temperatures of 400 to 500 degrees Celsius that are needed to process silicon – at least not yet. But according to Daniele Ielmini, professor at the “Politecnico di Milano”, that integration is key to the success of new brain-like computer technologies.
“Our goal is not to replace classical computer architecture,” he explains. “Rather, we want to develop alternative architectures that can perform certain tasks faster and with greater energy efficiency. This includes, for example, the parallel processing of large amounts of data, which is generated everywhere today, from agriculture to space exploration.”
Promisingly, there are other materials with similar properties that could be used to make high-performance memristors. “We can now test our memristor design with different materials,” says Alessandro Milozzi, a doctoral student at the “Politecnico di Milano”. “It is quite possible that some of them are better suited for integration with silicon.”
Reference: “Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity” by Rohit Abraham John, Alessandro Milozzi, Sergey Tsarev, Rolf Brönnimann, Simon C. Boehme, Erfu Wu, Ivan Shorubalko, Maksym V. Kovalenko and Daniele Ielmini, 23 December 2022, Science Advances.