Beyond von-Neumann Computing with Nanoscale Phase-Change Memory Devices

• Published in: Advanced functional materials Vol. 23; no. 18; pp. 2248 - 2254
• Main Authors: Wright, C. David, Hosseini, Peiman, Diosdado, Jorge A. Vazquez
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 13.05.2013; WILEY‐VCH Verlag
• Subjects: Arithmetic; chalcogenides; Computation; Devices; Electronics; Nanomaterials; Nanostructure; neuromorphic; non-von Neumann; phase-change computing; phase-change materials; phase-change memories; Position (location); Synapses
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.201202383

Historically, the application of phase‐change materials and devices has been limited to the provision of non‐volatile memories. Recently, however, the potential has been demonstrated for using phase‐change devices as the basis for new forms of brain‐like computing, by exploiting their multilevel resistance capability to provide electronic mimics of biological synapses. Here, a different and previously under‐explored property that is also intrinsic to phase‐change materials and devices, namely accumulation, is exploited to demonstrate that nanometer‐scale electronic phase‐change devices can also provide a powerful form of arithmetic computing. Complicated arithmetic operations are carried out, including parallel factorization and fractional division, using simple nanoscale phase‐change cells that process and store data simultaneously and at the same physical location, promising a most efficient and effective means for implementing beyond von‐Neumann computing. This same accumulation property can be used to provide a particularly simple form phase‐change integrate‐and‐fire “neuron”, which, by combining both phase‐change synapse and neuron electronic mimics, potentially opens up a route to the realization of all‐phase‐change neuromorphic processing. Nanometer‐scale GeSbTe phase‐change memory type devices are shown to provide an efficient and effective route to computing in which processing and storage are carried out simultaneously and at the same physical location, circumventing the “von Neumann bottleneck”. Advanced processing including factorization and fractional division are demonstrated, with single GeSbTe cells performing computations that require around 100 transistors in conventional Si processors.