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Electronics mystery solved

​Resistive memory with revolutionary properties is made possible by a phenomenon known as ovonic threshold switching, or OTS. Researchers at Leti, a CEA Tech institute, recently shed new light on the topic.

Published on 29 June 2020

​Imagine glass—an insulating material—that conducts electricity as well as metal when an electrical field is applied to it. This is something that can actually happen due to a phenomenon known as ovonic threshold switching (OTS), which occurs in some types of chalcogenide glass.  OTS was first observed around 50 years ago by American inventor Stanford R. Ovshinsky. It has been widely used in optical write systems like phase-change memory (DVD-RAM, CD-RW, and other technologies). More recently, OTS enabled the development of resistive memory with groundbreaking performance. This type of memory combines the speed of volatile memory with the non-volatility of flash and other solid-state storage technologies. But no one had ever explained how OTS occurs—until a team of researchers at Leti decided to unlock the mystery.

Leti and the University of Liège (Belgium) turned to the ESRF synchrotron in Grenoble for its X-ray absorption spectroscopy capabilities. Their strategy was to combine a very detailed description of the atomic structure and properties of these "OTS" glasses (obtained through electrical and optical measurements) with a novel ab initio molecular dynamic simulation method at the state of the art. And it worked!  They determined that OTS is tied to subtle rearrangements in the position of the atoms when an electrical field is applied.

Manufacturers like Intel had already taken advantage of the exciting properties of OTS in their cutting-edge memory devices. They did so mainly through trial-and-error backed by very substantial investments in R&D. However, nobody really knew how OTS worked. This new research, published in Science Advances, could lead to the development of atomic-scale design rules to enable new materials with even more powerful properties. The technology is also expected to benefit tomorrow's neuromorphic computing architectures for artificial intelligence. Neural networks made up of interconnected nano-scale memory devices could process information in much the same way the neurons in the human brain do.

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