Phase-change memory via a phase-changeable self-confined nano-filament

• Published in: Nature (London) Vol. 628; no. 8007; pp. 293 - 298
• Main Authors: Park, See-On, Hong, Seokman, Sung, Su-Jin, Kim, Dawon, Seo, Seokho, Jeong, Hakcheon, Park, Taehoon, Cho, Won Joon, Kim, Jeehwan, Choi, Shinhyun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.04.2024
• Subjects: 639/166/987; 639/301/1005/1007; 639/301/1005/1008; 639/925/357/995; 639/925/927/1007; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-024-07230-5

Phase-change memory (PCM) has been considered a promising candidate for solving von Neumann bottlenecks owing to its low latency, non-volatile memory property and high integration density 1 , 2 . However, PCMs usually require a large current for the reset process by melting the phase-change material into an amorphous phase, which deteriorates the energy efficiency 2 – 5 . Various studies have been conducted to reduce the operation current by minimizing the device dimensions, but this increases the fabrication cost while the reduction of the reset current is limited 6 , 7 . Here we show a device for reducing the reset current of a PCM by forming a phase-changeable SiTe x nano-filament. Without sacrificing the fabrication cost, the developed nano-filament PCM achieves an ultra-low reset current (approximately 10 μA), which is about one to two orders of magnitude smaller than that of highly scaled conventional PCMs. The device maintains favourable memory characteristics such as a large on/off ratio, fast speed, small variations and multilevel memory properties. Our finding is an important step towards developing novel computing paradigms for neuromorphic computing systems, edge processors, in-memory computing systems and even for conventional memory applications. We present a device that can reduce the phase-change memory reset current while maintaining a high on/off ratio, fast speed and small variations, representing advances for neuromorphic computing systems.