Monatomic phase change memory

Salinga, Martin; Kersting, Benedikt; Ronneberger, Ider; Jonnalagadda, Vara Prasad; Vu, Xuan Thang; Gallo, Manuel Le; Giannopoulos, Iason; Cojocaru‐Mirédin, Oana; Mazzarello, Riccardo; Sebastian, Abu

doi:10.1038/s41563-018-0110-9

Cited by 244 publications

(186 citation statements)

References 31 publications

Supporting

Mentioning

176

Contrasting

Order By: Relevance

“…To finalize this section, it is worthwhile to discuss recent fascinating work on monoatomic phase change memory. [ 150 ] The current paradigm in PCM research is to optimize properties and applications of PCMs by fine‐tuning complex alloys, generally containing several different elements. Basic ingredients are Sb and/or Te, but then to optimize properties like data retention, switching speed, endurance, etc., various other elements like Ge, In, Ag, Ga, Sc, N etc.…”

Section: Phase‐change Materials In Nanoscale Dimensionsmentioning

confidence: 99%

“…Therefore, an alternative to complex alloy optimization has been advocated. [ 150 ] It starts from a single element, Sb in this case. Referring back to the bond characterizing maps in Section 2, probably only Sb, Bi, and possibly As might be suitable as a single elemental phase‐change memory since they should exhibit MVB.…”

Section: Phase‐change Materials In Nanoscale Dimensionsmentioning

confidence: 99%

See 1 more Smart Citation

Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement

2020

View full text Add to dashboard Cite

unprecedented progress of the semiconductor industry and information technology. Yet, we have reached a stage where a simple evolution along established research lines might no longer bear much fruit. Advanced functional materials require increasingly complex and demanding property combinations. Their optimization would thus benefit from novel concepts. In thermoelectrics, which convert waste heat into electricity, for example, materials must show the unusual combination of high electrical and small thermal conductivity. This is demanding since a high electrical conductivity is usually accompanied by a high thermal conductivity. In phase change materials (PCMs) employed for data storage and processing, materials are required which possess a pronounced contrast in optical and/or electrical properties between two different states. Usually one of these states is a metastable one, which is typically amorphous, while the second state is then stable crystalline. The metastable state has to be stable at room temperature and slightly above for 10 years; but it should crystallize, i.e., return to the stable crystalline state in a few nanoseconds if heated to temperatures of typically around 500 °C. The combination of pronounced property contrast and hence presumably different atomic arrangements in the two different phases, yet rapid crystallization is indicative for an unusual correlation of chemical bonding, atomic arrangement, and resulting properties, including crystallization kinetics. Topological insulators, expected to help realize novel electronic functionalities, possess topologically protected spin-polarized surface states with high mobility. These states should govern the sample conductivity, if the bulk is insulating.This raises the question how these demanding requirements can be met and how superior materials can be identified. A number of different approaches have been developed in the past two decades to meet these needs. Combinatorial material synthesis, i.e., the fast preparation of stoichiometric libraries and their efficient analysis to identify superior compounds, has already been promoted over two decades ago. [1,2] While this scheme has indeed been successful in improving certain materials such as metal hydrides for hydrogen storage [3] and benchmarking electrocatalysts for solar water splitting, [4] for many material classes still empirical optimization schemes are employed. Machine learning is an emerging strategy to identify materials with a unique property portfolio. [5][6][7] This novel A unified picture of different application areas for incipient metals is presented. This unconventional material class includes several main-group chalcogenides, such as GeTe, PbTe, Sb 2 Te 3 , Bi 2 Se 3 , AgSbTe 2 and Ge 2 Sb 2 Te 5 . These compounds and related materials show a unique portfolio of physical properties. A novel map is discussed, which helps to explain these properties and separates the different fundamental bonding mechanisms (e.g., ionic, metallic, and covalent). The map also provides evidence ...

show abstract

Section: Phase‐change Materials In Nanoscale Dimensionsmentioning

confidence: 99%

Section: Phase‐change Materials In Nanoscale Dimensionsmentioning

confidence: 99%

Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement

2020

View full text Add to dashboard Cite

show abstract

“…More details of the methodology and the specific technical questions pertaining to GeTe have been reviewed elsewhere. On the one hand, ongoing work is concerned with the Ge-Sb-Te system, where further optimizations of scalability and switching speed are needed, and the recent development of a memory device based only on Sb ("monatomic" phase-change memory) [132] attests to the continued relevance of this material system. On the one hand, ongoing work is concerned with the Ge-Sb-Te system, where further optimizations of scalability and switching speed are needed, and the recent development of a memory device based only on Sb ("monatomic" phase-change memory) [132] attests to the continued relevance of this material system.…”

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

View full text Add to dashboard Cite

In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

show abstract

“…[ 23 ] Therefore monatomic phase change materials have been proposed. [ 25 ] Lastly, while many phase change switching devices are demonstrated, PCMs incorporated in such devices are either in blanket film format or nanostructures capped with other materials. Film structure holds limited spatial precision and does not access the vast parameter space of geometry dependent nanoresonators.…”

Section: Introductionmentioning

confidence: 99%

Fast Reversible Phase Change Silicon for Visible Active Photonics

Wang

Eliceiri

Deng

et al. 2020

Adv Funct Materials

View full text Add to dashboard Cite

Both amorphous and crystalline silicon are ubiquitous materials for electronics, photonics, and microelectromechanical systems. On-demand control of Si crystallinity is crucial for device manufacturing and to overcome the limitations of current phase-change materials (PCM) in active photonics. Fast reversible phase transformation in silicon, however, has never been accomplished due to the notorious challenge of amorphization. It is demonstrated that nanostructured Si can function as a PCM, since it can be reversibly crystallized and amorphized under nanosecond laser irradiation with different pulse energies. Reflection probing on a single nanodisk's phase transformations confirms the distinct mechanisms for crystallization and amorphization. The experimental results show that the relaxation time of undercooled silicon at 950 K is 10 ns. The phase change provides a 20% nonvolatile reflectivity modulation within 100 ns and can be repeated over 400 times. It is shown that such transformations are free of deformation upon solidification. Based on the switchable photonic properties in the visible spectrum, proof-of-concept experiments of dielectric color displays and dynamic wavefront control are shown. Therefore, nanostructured silicon is proposed as a chemically stable, deformation free, and complementary metal-oxide-semiconductor compatible (CMOS) PCM for active photonics at visible wavelengths.

show abstract

Monatomic phase change memory

Cited by 244 publications

References 31 publications

Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement

Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Fast Reversible Phase Change Silicon for Visible Active Photonics

Contact Info

Product

Resources

About