Ag-Ag2Sコアシェル型ナノ粒子を用いたリザーバーコンピューティングデバイスの作製

The performance of the von Neumann computer was greatly improved by miniaturizing transistors and increasing the density according to Moore\u27s Law. However, in recent years, the maximum permissible number of CPU transistors has remained constant, and further performance improvements have not been possible. In today\u27s nanoscale era, scaling to smaller sizes represents a major challenge in device manufacturing, circuit, and system design and integration. On the other hand, nanoscale technology has the potential to develop new materials and devices with unique properties. Memristors exhibit nonlinear current-voltage characteristics and have unique memory characteristics. That is, such a new nanoscale device whose current state depends on the past. It has the potential to create new computing paradigms for both non-linear and memory characteristics of Memristors. The purpose of this paper is to investigate the possibility of using wet chemical synthesis and Ag-Ag2S core-shell nanoparticles to develop a new computing paradigm called “Reservoir Computing” (RC) which belong to such a new paradigm. However, it differs from the traditional Recurrent Neural Network (RNN) method in that the pre-processor (ie, the reservoir) is composed of nonlinear elements that are randomly connected repeatedly. This greatly reduces the complexity of learning. In this thesis, we reported RC devices with low power consumption. The synthesis conditions of Ag-Ag2S core-shell nanoparticles operating at low voltage were searched. Next, synthesis parameters such as Ag / S molar ratio were examined, to control the particle size. We confirmed that the nanoparticle agglomerates have nonlinear electrical conductivity necessary for the development of RC computations, such as constantly exhibiting hysteresis in the current-voltage (I-V) curve, and investigated other conditions necessary for RC hardware. Since the linear regression of the output channel was trained to fit the target waveform, the potential of the nanoparticle-based RC device was shown.九州工業大学博士学位論文 学位記番号:生工博甲第359号 学位授与年月日:令和元年12月27日1 Introduction and Literature Review|2 Methodology|3 Effect of various synthesis procedure to electrical characteristics of the nanoparticles-based device|4 Effect of the Ag-Ag2S volume ratio to the electrical properties|5 Switching mechanism of Ag-Ag2S nanoparticles-based device and neuromorphic learning properties|6 Recurrent neural network properties of Ag-Ag2S nanoparticles-based device and its application as reservoir computing|7 Conclusions and Suggestions九州工業大学令和元年

Control of the Neuromorphic Learning Behavior Based on the Aggregation of Thiol-protected Ag/Ag2S Core–Shell Nanoparticles

The neuromorphic learning switching behavior was investigated in electric devices that were constructed based on the aggregation of thiol-stabilized Ag/Ag2S core–shell nanoparticles (NPs). The NPs were synthesized using the two-phase modified Brust–Schiffrin procedure and exhibited Ag–S and Ag–S–R bonding states at the surfaces of Ag NPs. The memristive behavior of such a device at room temperature under ambient pressure, which can be used to emulate the functions of biological synapses with long and short memories, is achieved. The importance of the Ag2S and the thiol layer at the surface of Ag NPs for the demonstration of learning behavior, such as the potentiation and depression of synapses in the human brain is explained

Performance of Ag–Ag2S core–shell nanoparticle-based random network reservoir computing device

Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag–Ag2S core–shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag–Ag2S NPs for the development of next-generation RC hardware

Frequency dependence dielectrophoresis technique for bridging graphene nanoribbons

We succeeded in bridging unzipped graphene nanoribbons (GNRs) and separating them from unwanted single-walled carbon nanotubes (SWNTs) using a frequency-dependent dielectrophoresis (DEP) method by varying the frequency and applied voltage used for future assembly. Atomic force micrographs and Raman spectra proved that unzipped GNRs were successfully bridged by the DEP method at frequencies higher than 13 MHz. The theoretical calculation also supported the finding that only GNRs were collected from a mixture of SWNTs/GNRs suspensions

Control of the neuromorphic learning behavior based on the aggregation of thiol-protected Ag-Ag2S core–shell nanoparticles

The neuromorphic learning switching behavior was investigated in electric devices that were constructed based on the aggregation of thiol-stabilized Ag/Ag2S core–shell nanoparticles (NPs). The NPs were synthesized using the two-phase modified Brust–Schiffrin procedure and exhibited Ag–S and Ag–S–R bonding states at the surfaces of Ag NPs. The memristive behavior of such a device at room temperature under ambient pressure, which can be used to emulate the functions of biological synapses with long and short memories, is achieved. The importance of the Ag2S and the thiol layer at the surface of Ag NPs for the demonstration of learning behavior, such as the potentiation and depression of synapses in the human brain is explained

Frequency dependence dielectrophoresis technique for bridging graphene nanoribbons

We succeeded in bridging unzipped graphene nanoribbons (GNRs) and separating them from unwanted single-walled carbon nanotubes (SWNTs) using a frequency-dependent dielectrophoresis (DEP) method by varying the frequency and applied voltage used for future assembly. Atomic force micrographs and Raman spectra proved that unzipped GNRs were successfully bridged by the DEP method at frequencies higher than 13 MHz. The theoretical calculation also supported the finding that only GNRs were collected from a mixture of SWNTs/GNRs suspensions

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