Científicos desarrollan nuevos componentes informáticos

Mejora informática mediante el uso de nanocristales de perovskita.

A pesar de los avances tecnológicos, la mente humana sigue siendo superior a las computadoras en muchos aspectos. Si bien las computadoras pueden realizar cálculos matemáticos más rápido que los humanos, el cerebro humano puede procesar información sensorial compleja y adaptarse a nuevas experiencias con facilidad. Esta capacidad aún está fuera del alcance de las computadoras, y el cerebro humano logra esta hazaña mientras consume solo una fracción de la energía que necesitaría una computadora portátil.

La estructura del cerebro contribuye significativamente a la eficiencia energética. A diferencia de las computadoras, donde la memoria y el procesamiento son dos entidades separadas y la información debe transferirse entre ellas, las neuronas y las sinapsis del cerebro pueden almacenar y procesar información simultáneamente. Esto elimina la necesidad de transferir datos constantemente, lo que puede causar ralentizaciones en las computadoras cuando se trata de grandes cantidades de información.

Una posible solución a este cuello de botella es una nueva arquitectura informática que se modele a partir del cerebro humano. Con este fin, los científicos están desarrollando los llamados memristores: componentes que, como las células cerebrales, se combinan para almacenar y procesar datos.

Un equipo de investigadores de Empa, ETH Zurich y Politecnico di Milano ha desarrollado un Memristor que es más potente y fácil de fabricar que sus predecesores. Los investigadores publicaron recientemente sus hallazgos en la revista[{» 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