A New Phase-Change Memory For Processing Large Amounts Of Data 


A technical paper titled “Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory” was published by researchers at Stanford University, TSMC, NIST, University of Maryland, Theiss Research and Tianjin University.


“Data-centric applications are pushing the limits of energy-efficiency in today’s computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge4Sb6Te7), to achieve record-low power density ≈ 5 MW/cm2 and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 108 cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge4Sb6Te7 nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing.”

Find the technical paper here. Published January 2024. Read this related news article from Stanford University.

Wu, X., Khan, A.I., Lee, H. et al. Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory. Nat Commun 15, 13 (2024). https://doi.org/10.1038/s41467-023-42792-4

Further Reading
MRAM Getting More Attention At Smallest Nodes
Why this 25-year-old technology may be the memory of choice for leading edge designs and in automotive applications.
Increasing AI Energy Efficiency With Compute In Memory
How to process zettascale workloads and stay within a fixed power budget.

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