Dynamic Nonlinear Behavior of Ionic Liquid-Based Reservoir Computing Devices

Controlling Ionic Conductivity in Organometallic Ionic Liquids through Light-Induced Coordination Polymer Formation and Thermal Reversion

Owing to their high ionic conductivity and negligible vapor pressure, ionic liquids (ILs) find applications in various electronic devices. However, fabricating IL-based photocontrollable devices remains a challenge. In this study, we developed organometallic ILs with reversible light- and heat-controlled ionic conductivities for potential use in tunable devices. The physical properties and stimulus responses of ILs containing a cationic sandwich Ru complex with two coordinating substituents were investigated. UV photoirradiation of these ILs triggered cation photodissociation, resulting in their transformation into viscoelastic coordination polymers wherein the coordinating substituents bridged the Ru centers. Owing to the anion coordination, salts with coordinating anions such as CF3SO2NCN-, B(CN)4-, and BF2(CN)2- exhibited faster response and higher conversion than those with noncoordinating anions including (FSO2)2N- and (CF3SO2)2N-. All photoproducts reverted to their original ILs upon heating, except for the photoproduct of the BF2(CN)2 salt, which decomposed upon heating. Light- and heat-induced reversible changes occur in most cases between the high-ionic-conductive IL state and low-ionic-conductive coordination polymer state. Unlike previously reported ILs with three or one cation substituent, the current ILs exhibited both high reactivity and large ionic conductivity changes.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Unveiling transient current response in bilayer oxide-based physical reservoirs for time-series data analysis

Physical reservoirs employed to map time-series data and analyze extracted features have attracted interest owing to their low training cost and mitigated interconnection complexity. This study reports a physical reservoir based on a bilayer oxide-based dynamic memristor. The proposed device exhibits a nonlinear current response and short-term memory (STM), satisfying the requirements of reservoir computing (RC). These characteristics are validated using a compact model to account for resistive switching (RS) via the dynamic evolution of the internal state variable and the relocation of oxygen vacancies. Mathematically, the transient current response can be quantitatively described according to a simple set of equations to correlate the theoretical framework with experimental results. Furthermore, the device shows significant reliability and ability to distinguish 4-bit inputs and four diverse neural firing patterns. Therefore, this work shows the feasibility of implementing physical reservoirs in hardware and advances the understanding of the dynamic response.

Photonic Physical Reservoir Computing with Tunable Relaxation Time Constant

Recent years have witnessed a rising demand for edge computing, and there is a need for methods to decrease the computational cost while maintaining a high learning performance when processing information at arbitrary edges. Reservoir computing using physical dynamics has attracted significant attention. However, currently, the timescale of the input signals that can be processed by physical reservoirs is limited by the transient characteristics inherent to the selected physical system. This study used an Sn-doped In2 O3 /Nb-doped SrTiO3 junction to fabricate a memristor that could respond to both electrical and optical stimuli. The results show that the timescale of the transient current response of the device could be controlled over several orders of magnitude simply by applying a small voltage. The computational performance of the device as a physical reservoir is evaluated in an image classification task, demonstrating that the learning accuracy could be optimized by tuning the device to exhibit appropriate transient characteristics according to the timescale of the input signals. These results are expected to provide deeper insights into the photoconductive properties of strontium titanate, as well as support the physical implementation of computing systems.

Characterization of Information-Transmitting Materials Produced in Ionic Liquid-based Neuromorphic Electrochemical Devices for Physical Reservoir Computing

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.

Reservoir computing with the electrochemical formation and reduction of gold oxide in aqueous solutions with a three-electrode electrochemical setup

Supervised classification of handwritten digits via physical reservoir computing (PRC) using electrochemistry with a three-electrode electrochemical setup was demonstrated. Short-term memory required for the PRC was realized for 3 bit pulse patterns by adjusting the formation/reduction ratio of gold oxides, showing a wide potential of electrochemistry as resources of PR devices.