Des scientifiques développent de nouveaux composants informatiques

Amélioration du calcul grâce à l’utilisation de nanocristaux de pérovskite.

Malgré les progrès de la technologie, l’esprit humain reste supérieur aux ordinateurs à bien des égards. Alors que les ordinateurs peuvent effectuer des calculs mathématiques plus rapidement que les humains, le cerveau humain est capable de traiter des informations sensorielles complexes et de s’adapter facilement à de nouvelles expériences. Cette capacité est encore hors de portée des ordinateurs, et le cerveau humain accomplit cet exploit en ne consommant qu’une fraction de la puissance dont un ordinateur portable aurait besoin.

La structure du cerveau contribue de manière significative à l’efficacité énergétique. Contrairement aux ordinateurs, où la mémoire et le traitement sont deux entités distinctes et où les informations doivent être transférées entre elles, les neurones et les synapses du cerveau sont capables de stocker et de traiter les informations simultanément. Cela élimine le besoin de transférer constamment des données, ce qui peut entraîner des ralentissements dans les ordinateurs lorsqu’ils traitent de grandes quantités d’informations.

Une solution possible à ce goulot d’étranglement est une nouvelle architecture informatique calquée sur le cerveau humain. À cette fin, les scientifiques développent des soi-disant memristors : des composants qui, comme les cellules du cerveau, se combinent pour stocker et traiter les données.

Une équipe de chercheurs de l’Empa, de l’ETH Zurich et du Politecnico di Milano a développé un Memristor plus puissant et plus facile à fabriquer que ses prédécesseurs. Les chercheurs ont récemment publié leurs découvertes dans la revue[{ » attribute= » »>Science Advances.

Performance through mixed ionic and electronic conductivity

The novel memristors are based on halide perovskite nanocrystals, a semiconductor material known from solar cell manufacturing. “Halide perovskites conduct both ions and electrons,” explains Rohit John, former ETH Fellow and postdoctoral researcher at both ETH Zurich and Empa. “This dual conductivity enables more complex calculations that closely resemble processes in the brain.”

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.
DOI: 10.1126/sciadv.ade0072