Hisashi Shima

Hydrogen (H₂) gas sensors are essential for detecting leaks and ensuring safety, thereby supporting the broader adoption of hydrogen energy. The performance of H₂ sensors has been shown to be improved by the incorporation of TiO₂ nanostructures. The key findings are summarized as follows: (1) Resistive random-access memory (ReRAM) technology was used to fabricate extremely compact H₂ sensors via various forming techniques, and substantial sensor performance enhancement was investigated. (2) A nanocontact composed of titanium oxide (TiO_x)/platinum (Pt) was subjected to various forming operations to establish a Schottky junction with a nanogap structure on a tantalum oxide (Ta₂O₅) layer, and its properties were assessed. (3) When the Pt electrode was on the positive side during the forming operation used for ReRAM technology, a Pt nanopillar structure was produced. By contrast, when the forming operation was conducted with a positive bias on the TiO_x side, a mixed oxide film of Ta and Ti was produced, which indicates local Ta doping into the TiO_x. A sensor response of over 1000 times was achieved at a minimal voltage of 1 mV at room temperature. (4) This sensor fabrication technology based on the forming operation is promising for the development of low-power consumption sensors.

We synthesized ionic liquids (ILs) containing Cu cations in which the stable valence state of the Cu cations was systematically controlled by introducing a cyano group (CN) into a cationic side chain. We also explored the current waveform and underlying electrochemical reaction appropriate for physical reservoir computing. An IL-based physical reservoir device (IL-PRD) using ILs with CN showed higher short-term memory capability, nonlinearity, and effective data dimensionality than an IL-PRD using an IL without CN. We found that the ratio of the oxidation current to the reduction current was an influencing factor for the computing capability of IL-PRDs.

Physical reservoir computing (PRC) is attracting considerable attention as a low-power, high-performance, edge artificial intelligence–friendly information technology. A physical reservoir device (PRD), which is key to PRC, converts input signals into higher-dimensional signals. In this study, we perform dimensionality analysis using principal component analysis (PCA) to investigate the data characteristics of output signals from an ionic liquid-based PRD (IL-PRD) from the viewpoint of its impact on the signal waveform and machine learning (ML) capability. More specifically, the data analysis algorism consisting of a low-rank approximation and data reconstruction is applied to the signal waveform derived from the electrochemical reactions in IL-PRD. Results show that nonlinear output signals, such as faradaic currents, increase the data dimension, thus improving the ML capability of PRC. When the data dimension increases from 1 to 9 owing to the faradaic currents, a short-term memory capacity, which is one of the indispensable performance indexes for PRC, improves by 15 times. In addition, we find that non-ideal electrical properties, such as cycle-to-cycle current variations, are included in the high-rank component of the data matrix obtained via PCA. Such non-ideality is essential for processing the raw data produced in real space because it includes the information of the input signal history as an intrinsic variability. Quantitative data dimensionality analysis, conducted using PCA, successfully explains the relationship between the ML capability of PRC and the output current waveform of the IL-PRD. These results provide guidelines for learning data selection in constructing optimal ML models.

Device implementation of reservoir computing, which is expected to enable high-performance data processing in simple neural networks at a low computational cost, is an important technology to accelerate the use of artificial intelligence in the real-world edge computing domain. Here, we propose an ionic liquid-based physical reservoir device (IL-PRD), in which copper cations dissolved in an IL induce diverse electrochemical current responses. The origin of the electrochemical current from the IL-PRD was investigated spectroscopically in detail. After operating the device under various operating conditions, X-ray photoelectron spectroscopy of the IL-PRD revealed that electrochemical reactions involving Cu, Cu2O, Cu(OH)(2), CuSx, and H2O occur at the Pt electrode/IL interface. These products are considered information transmission materials in IL-PRD similar to neurotransmitters in biological neurons. By introducing the Faradaic current components due to the electrochemical reactions of these materials into the output signal of IL-PRD, we succeeded in improving the time-series data processing performance of the nonlinear autoregressive moving average task. In addition, the information processing efficiency in machine learning to classify electrocardiogram signal waveforms was successfully improved by using the output current from IL-PRD. Optimizing the electrochemical reaction products of IL-PRD is expected to advance data processing technology in society.

The temperature dependence of an intelligent connection (IConnect) device, in which an ionic liquid (IL) plays an essential role in a memristive function is presented in this study. An appropriate choice of IL and dissolved metal ion species can control the volatility of the IL-IConnect device. The temperature dependence of the IL-IConnect device was strong, although the change in current–voltage characteristics was reversible in response to temperature variations. The device’s potential as a physical reservoir-computing device is studied.

Herein, a physical reservoir device that uses faradaic currents generated by redox reactions of metal ions in ionic liquids was developed. Synthetic time-series data consisting of randomly arranged binary number sequences ("1" and "0") were applied as isosceles-triangular voltage pulses with positive and negative voltage heights, respectively, and the effects of the faradaic current on short-term memory and parity-check task accuracies were verified. The current signal for the first half of the triangular voltage-pulse period, which contained a much higher faradaic current component compared to that of the second half of the triangular voltage-pulse period, enabled higher short-term memory task accuracy. Furthermore, when parity-check tasks were performed using a faradaic current generated by asymmetric triangular voltage-pulse levels of 1 and 0, the parity-check task accuracy was approximately eight times higher than that of the symmetric triangular voltage pulse in terms of the correlation coefficient between the output signal and target data. These results demonstrate the advantage of the faradaic current on both the short-term memory characteristics and nonlinear conversion capabilities and are expected to provide guidance for designing and controlling various physical reservoir devices that utilize electrochemical reactions.

A physical reservoir device with tunable transient dynamics is strongly required to process time-series data with various timescales generated in the edge region. In this study, we proposed using the dielectric relaxation at an electrode–ionic liquid (IL) interface as the physical reservoir by making the most of designable physicochemical properties of ILs. The transient dynamics of a Au/IL/Au reservoir device were characterized as a function of the alkyl chain length of cations in the IL (1-alkyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide). By considering a weighted sum of exponentials expressing a superposition of Debye-type relaxations, the transient dynamics were well reconstructed. Although such complex dynamics governed by multiple relaxation processes were observed, each extracted relaxation time scales with a power law as a function of IL’s viscosity determined by the alkyl chain length of cations. This indicates that the relaxation processes are characterized by bulk properties of the ILs that obey the widely received Vogel-Fulcher-Tammann law. We demonstrated that the 4-bit time-series signals were transformed into the 16 classifiable data, and the data transformation, which enables to achieve higher accuracy in an image classification task, can be easily optimized according to the features of the input signals by controlling the IL’s viscosity.

Research and development into resistive switching memories, such as resistive random access memory (ReRAM) and memristors, are being actively promoted toward the realization of new computing techniques. To improve the reliability of these devices, it is important to clarify their resistance characteristics. Low‐frequency‐noise spectroscopy (LFNS) is a powerful method for investigating the nature of traps in conduction paths, even at the nanoscale. In this article, the results of LFNS measurements on a TaOx‐based ReRAM device are presented. The temperature dependence of the noise spectra at given frequencies reveals the existence of multiple trap levels, which are observed over a wide range of resistance values. These experimental results show that the ReRAM device is particularly advantageous when used in analog resistive switching applications.

We fabricated an ionic-liquid conductive-bridge memory (IL-CBRAM) in which the solid-state electrolyte in a conventional CBRAM sandwich structure of Cu- and Pt-electrodes was replaced with IL. To stabilize the Cu-filaments in IL, an additional metal layer with a standard electrode potential (SEP) lower than that of Cu was inserted into the memory cell. We found Ta to be promising for this purpose, because the SEP in IL exhibited the Ta < Cu < Pt relation. Actually, inserting the Ta-adhesion layer increased the data-retention time. The data-retention characteristics control through cell design considering the SEP arrangement of metals was successfully demonstrated.