Scientists are getting better at creating neuron-like junctions for computers that mimic the random information processing, storage, and recall of the human brain. For the journal Science and Technology of Advanced Materials, Fei Zhuge of the Chinese Academy of Sciences and colleagues reviewed the latest developments in the design of these memristors.’
Computers use artificial intelligence programs to recall previously learned information and make predictions.
These programs are extremely energy and time-consuming: they must typically transfer large amounts of data between separate memory and processing units. Researchers have been working on computer hardware that, like the human brain, allows for more random and simultaneous information transfer and storage to address this issue.
Memristors, which resemble the junctions between neurons known as synapses, are used in the electronic circuits of these ‘neuromorphic’ computers. Energy flows through material from one electrode to the next. Similar to how a neuron sends a signal across the synapse to the next neuron.
Scientists are currently improving the tuning of this intermediate material so that the information flow is more stable and reliable.
“Oxides are the most commonly used materials in memristors,” Zhuge says. “Oxide memristors, on the other hand, have unsatisfactory stability and reliability. Hybrid structures based on oxides can significantly improve this.”
Typically, memristors are made of an oxide-based material sandwiched between two electrodes. When researchers combine two or more layers of different oxide-based materials between the electrodes, they get better results. An electrical current flowing through the network causes ions to drift within the layers. The ions’ movements eventually change the memristor’s resistance, required to send or stop a signal through the junction.
Memristors can be fine-tuned further by changing the electrode compounds or adjusting the intermediate oxide-based materials. Zhuge and his colleagues are currently working on optically-controlled oxide memristor-based optoelectronic neuromorphic computers. Photonic memristors are expected to have faster operation speeds and lower energy consumption than electronic memristors. They have the potential to be used to build next-generation artificial visual systems with high computing efficiency.