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

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.

Ultra-low-energy programmable non-volatile silicon photonics based on phase-change materials with graphene heaters.

Silicon photonics is evolving from laboratory research to real-world applications with the potential to transform many technologies, including optical neural networks and quantum information processing. A key element for these applications is a reconfigurable switch operating at ultra-low programming energy-a challenging proposition for traditional thermo-optic or free carrier switches. Recent advances in non-volatile programmable silicon photonics based on phase-change materials (PCMs) provide an attractive solution to energy-efficient photonic switches with zero static power, but the programming energy density remains high (hundreds of attojoules per cubic nanometre). Here we demonstrate a non-volatile electrically reconfigurable silicon photonic platform leveraging a monolayer graphene heater with high energy efficiency and endurance. In particular, we show a broadband switch based on the technologically mature PCM Ge2Sb2Te5 and a phase shifter employing the emerging low-loss PCM Sb2Se3. The graphene-assisted photonic switches exhibited an endurance of over 1,000 cycles and a programming energy density of 8.7 ± 1.4 aJ nm-3, that is, within an order of magnitude of the PCM thermodynamic switching energy limit (~1.2 aJ nm-3) and at least a 20-fold reduction in switching energy compared with the state of the art. Our work shows that graphene is a reliable and energy-efficient heater compatible with dielectric platforms, including Si3N4, for technologically relevant non-volatile programmable silicon photonics.

Ultralow-switching current density multilevel phase-change memory on a flexible substrate.

Phase-change memory (PCM) is a promising candidate for data storage in flexible electronics, but its high switching current and power are often drawbacks. In this study, we demonstrate a switching current density of ~0.1 mega-ampere per square centimeter in flexible superlattice PCM, a value that is one to two orders of magnitude lower than in conventional PCM on flexible or silicon substrates. This reduced switching current density is enabled by heat confinement in the superlattice material, assisted by current confinement in a pore-type device and the thermally insulating flexible substrate. Our devices also show multilevel operation with low resistance drift. The low switching current and good resistance on/off ratio are retained before, during, and after repeated bending and cycling. These results pave the way to low-power memory for flexible electronics and also provide key insights for PCM optimization on conventional silicon substrates.