Energy Efficient Neuro-Inspired Phase-Change Memory Based on Ge4 Sb6 Te7 as a Novel Epitaxial Nanocomposite

Asir Intisar Khan; Heshan Yu; Huairuo Zhang; John R Goggin; Heungdong Kwon; Xiangjin Wu; Christopher Perez; Kathryn M Neilson; Mehdi Asheghi; Kenneth E Goodson; Patrick M Vora; Albert Davydov; Ichiro Takeuchi; Eric Pop

doi:10.1002/adma.202300107

Energy Efficient Neuro-Inspired Phase-Change Memory Based on Ge₄ Sb₆ Te₇ as a Novel Epitaxial Nanocomposite

Adv Mater. 2023 Jul;35(30):e2300107. doi: 10.1002/adma.202300107. Epub 2023 Jun 15.

Authors

Asir Intisar Khan¹, Heshan Yu², Huairuo Zhang^{3

4}, John R Goggin^{5

6}, Heungdong Kwon⁷, Xiangjin Wu¹, Christopher Perez⁷, Kathryn M Neilson¹, Mehdi Asheghi⁷, Kenneth E Goodson⁷, Patrick M Vora^{5

6}, Albert Davydov^{3

6}, Ichiro Takeuchi², Eric Pop^{1

8

9}

Affiliations

¹ Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
² Department of Materials Science and Engineering, University of Maryland, College Park, MD, 20742, USA.
³ Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
⁴ Theiss Research, Inc., La Jolla, CA, 92037, USA.
⁵ Department of Physics and Astronomy, George Mason University, Fairfax, VA, 22030, USA.
⁶ Quantum Science and Engineering Center, George Mason University, Fairfax, VA, 22030, USA.
⁷ Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
⁸ Department of Materials Science & Engineering, Stanford University, Stanford, CA, 94305, USA.
⁹ Precourt Institute for Energy, Stanford University, Stanford, CA, 94305, USA.

PMID: 36720651
DOI: 10.1002/adma.202300107

Abstract

Phase-change memory (PCM) is a promising candidate for neuro-inspired, data-intensive artificial intelligence applications, which relies on the physical attributes of PCM materials including gradual change of resistance states and multilevel operation with low resistance drift. However, achieving these attributes simultaneously remains a fundamental challenge for PCM materials such as Ge₂ Sb₂ Te₅ , the most commonly used material. Here bi-directional gradual resistance changes with ≈10× resistance window using low energy pulses are demonstrated in nanoscale PCM devices based on Ge₄ Sb₆ Te₇ , a new phase-change nanocomposite material . These devices show 13 resistance levels with low resistance drift for the first 8 levels, a resistance on/off ratio of ≈1000, and low variability. These attributes are enabled by the unique microstructural and electro-thermal properties of Ge₄ Sb₆ Te₇ , a nanocomposite consisting of epitaxial SbTe nanoclusters within the Ge-Sb-Te matrix, and a higher crystallization but lower melting temperature than Ge₂ Sb₂ Te₅ . These results advance the pathway toward energy-efficient analog computing using PCM.

Keywords: analog memory; low-energy memory; nanocomposites; neuro-inspired phase-change memory.

Abstract

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