S23

Neuron-inspired memory based on plasmon particle networkwith phase change material
Takayuki Uchiyama, Takashi Hira, Kenta Kuwamura, Yuya Kihara, and Toshiharu Saiki
Graduate School of Science and Technology, Keio University
3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japansaiki@elec.keio.ac.jp
ABSTRACT
So far we have been studying the modulation of localized surface plasmon resonance (LSPR) of a metal nanoparticle
using a GeSbTe (GST) film as an active medium. We demonstrated high-contrast switching of LSPR upon phasechange for a Au nanoparticle (AuNP) on a GST thin film and for a AuNP/GST/Au thin film sandwich structure. To go
beyond this single particle switching functionality, in this study we discuss the plasmon particle network system withphase change material and explore the possibility of new intelligent memory functionality. It may be possible to build
brain-inspired computing devices by incorporating the single particle switch/memory functionality in laterally coupledplasmon particle systems. The network dynamics can be implemented by electromagnetic interaction between
plasmon particles. The plasticity and threshold properties of the synapse, which is the memory device in the brain, arereproduced using phase change materials. We demonstrate associative memory through some numerical simulations.
Key words: Localized surface plasmon resonance, GeSbTe, neuron circuit, synapse
All present computers are based on the von Neumann model and have been essentially the same for almost
seventy years. Such computers are comprised of memory, arithmetic logic unit, control unit, and input/output devices.
So far, these microprocessors, memories, and logic devices have strongly relied on high-speed CMOS technologies.
However since the scaling of CMOS technology is approaching to its physical limit, alternative architecture with newdevice concepts should be investigated to address increasing demands for massively parallel processing with low
energy consumption.
For information processing technologies beyond CMOS scaling, brain-inspired computing, in which an
electronic circuit simulates a neural network, has been attracting strong attentions. Human brain performs bothinformation storage and processing on the basis of analog memory and digital integrator and comparator. The key
device of analog memory is a synapse, in which the information is stored in non-binary fashion, and the brain
functionality including learning and association relies on the synaptic plasticity. To date several reports demonstrated
electronic devices that emulate synaptic functions using memrisive systems, which can store dynamic history of thesystem. Among a variety of material candidates and physical mechanisms for the synaptic device, chalcogenide phasechange materials (PCMs) are promising because they exhibit reversible switching between crystalline and amorphousstates and remarkable threshold behavior in both crystallization and amorphization processes.
Recently active control of the localized surface plasmon resonance (LSPR), which is supported by metal
nanoparticles and their complexes, has been intensively investigated to implement dynamic switching and tuning
functionality in plasmonic devices. Several schemes for LSPR modulation, including peak position, intensity andbandwidth changes has been explored, typically by embedding metal nanostructures in reversibly switchable media
including liquid crystals, photoconducting materials, photochromic molecules, electrochromic conducting polymer,
and so on. Plasmonic Fano resonant nanostructures, which arise from the near-field coupling between broadsuperradiant and narrow subradiant modes, has great potential for active control due to its sharp resonance andsensitivity to a variety of parameters. As an active media for the LSPR control, PCM is advantageous because they
exhibit a large contrast in refractive index between crystalline and amorphous phases, which will provide large
modulation of LSPR, and also their stable plasticity, which has been demonstrated by the commercial application as
rewritable optical recording media for decades.
For active control of LSPR using PCM, we demonstrated single particle spectroscopy of a Au nanosandwich,where a GST thin layer is inserted between interacting AuNRs and a Au film. A stable and repeatable switching with ahigh contrast was obtained at the hybridized mode between the AuNR and Au film upon phase change of the GSTlayer. Interestingly, at the initial stage of switching operation, a growth in the modulation contrast was observed,
which is a type of “learning” effect in terms of memory device functionality.In this presentation we discuss the challenge and the possibility for realizing brain-inspired computing
devices by incorporating the plasticity and threshold property of PCM in laterally coupled plasmon particle systems.
The network dynamics that imitate the neuron circuit with axons and dendrites can be implemented byelectromagnetic interaction between plasmon particles without physical connections. We demonstrate that spatial
evolution of plasmon-PCM network system can be equivalent to the algorithms based on cell automaton model (Fig.
1) and self-organizing map.
This research was supported by a Grant-in-Aid for Scientific Research (S) from the Ministry of Education,Culture, Sport, Science, and Technology of Japan.

Fig. 1 Implementation of a cellular automaton algorithm in plasmon-PCM network system.