June 23, 2022                                                    report

Using machine learning to narrow down the possibilities for a better quantum tunneling interface

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2022-06-24 11:00:04

June 23, 2022 report

by Bob Yirka , Phys.org

A pair of researchers at Fudan University in China has used machine learning to narrow the list of possible improved tunneling interface configurations for use in transistors. They have published their results in Physical Review Letters.

Over the past several decades, engineers have worked to uphold Moore's law, faithfully doubling the number of transistors that could be placed on an integrated circuit roughly every two years. But such efforts are in jeopardy due to the laws of physics—most particularly, those related to quantum tunneling that degrade performance. More specifically, the material that is used to separate gates on chips (interfaces) from channels has become so thin that charge carriers can wiggle their way through via quantum tunneling. In this new effort, the researchers sought stable configurations that would minimize such tunneling, thereby allowing Moore's law to continue, at least for a while.

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