Taming nanoscale heat to satiate energy-hungry AI

Prof. Tae Geun Kim's team at Korea University published in the International Journal of Extreme Manufacturing a double-confined phase-change memory structure that uses layered thermal management to enable multi-bit storage while reducing atom displacement and data drift, potentially easing AI's energy and heat burden.