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. Doubling up Cooper pairs to protect qubits in quantum computers from noise More information: Ye-Fei Li et al, Smallest Stable Si/SiO2 Interface that Suppresses Quantum Tunneling from Machine-Learning-Based Global Search, Physical Review Letters (2022). They have published their results in Physical Review Letters. They then used a machine learning application to study approximately 2,500 structures as possible candidate interface configuration replacements. But such efforts are in jeopardy due to the laws of physics—most particularly, those related to quantum tunneling that degrade performance. The researchers discovered that the configuration of the material that made up the interface played a major role in the degree of quantum tunneling. 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. (Phys.org). Continue reading.
Bobby Gray, head of analytics and data marketing at Artefact, looks at how organizations can tackle the resulting data loss with a first-party data mindset – and the adoption of artificial intelligence and machine learning.
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.