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

See-On Park, Seokman Hong, Su-Jin Sung, Dawon Kim, Seokho Seo, Hakcheon Jeong, Taehoon Park, Won Joon Cho, Jeehwan Kim and Shinhyun Choi ()
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See-On Park: Korea Advanced Institute of Science and Technology (KAIST)
Seokman Hong: Korea Advanced Institute of Science and Technology (KAIST)
Su-Jin Sung: Korea Advanced Institute of Science and Technology (KAIST)
Dawon Kim: Korea Advanced Institute of Science and Technology (KAIST)
Seokho Seo: Korea Advanced Institute of Science and Technology (KAIST)
Hakcheon Jeong: Korea Advanced Institute of Science and Technology (KAIST)
Taehoon Park: Korea Advanced Institute of Science and Technology (KAIST)
Won Joon Cho: Samsung Electronics Co., Ltd.
Jeehwan Kim: Samsung Electronics Co., Ltd.
Shinhyun Choi: Korea Advanced Institute of Science and Technology (KAIST)

Nature, 2024, vol. 628, issue 8007, 293-298

Abstract: Abstract 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 density1,2. 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 efficiency2–5. 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 limited6,7. Here we show a device for reducing the reset current of a PCM by forming a phase-changeable SiTex 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.

Date: 2024
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DOI: 10.1038/s41586-024-07230-5

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