科学者が新しいコンピューター コンポーネントを開発

ペロブスカイト ナノクリスタルの使用によるコンピューティングの強化。

技術の進歩にもかかわらず、人間の脳はいくつかの点でコンピューターよりも優れています。 コンピューターは人間よりも速く数学的計算を実行できますが、人間の脳は複雑な感覚情報を処理し、新しい経験に簡単に適応することができます。 この能力はまだコンピューターの到達範囲を超えており、人間の脳はラップトップが必要とするエネルギーのほんの一部しか消費せずにこの偉業を成し遂げています。

脳の構造は、そのエネルギー効率に大きく貢献しています。 記憶と処理が別々のエンティティであり、それらの間で情報を転送する必要があるコンピューターとは異なり、脳内のニューロンとシナプスは、情報の保存と処理を同時に行うことができます。 これにより、大量の情報を処理するときにコンピューターの速度低下の原因となるデータを常に転送する必要がなくなります。

このボトルネックに対する解決策の 1 つは、人間の脳をモデルにした新しいコンピューター アーキテクチャです。 この目的のために、科学者はいわゆるメモリスタを開発しています。これは、脳細胞のように、データの保存と処理を組み合わせたコンポーネントです。

Empa、ETH Zurich、および「Politecnico di Milano」の研究者チームは、以前のものよりも強力で製造が容易なメモリスタを開発しました。 研究者は最近、その結果をジャーナルに発表しました[{” 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