Magnetization Vector Rotation Reservoir Computing Operated by Redox Mechanism.

Physical reservoir computing is a promising way to develop efficient artificial intelligence using physical devices exhibiting nonlinear dynamics. Although magnetic materials have advantages in miniaturization, the need for a magnetic field and large electric current results in high electric power consumption and a complex device structure. To resolve these issues, we propose a redox-based physical reservoir utilizing the planar Hall effect and anisotropic magnetoresistance, which are phenomena described by different nonlinear functions of the magnetization vector that do not need a magnetic field to be applied. The expressive power of this reservoir based on a compact all-solid-state redox transistor is higher than the previous physical reservoir. The normalized mean square error of the reservoir on a second-order nonlinear equation task was 1.69 × 10-3, which is lower than that of a memristor array (3.13 × 10-3) even though the number of reservoir nodes was fewer than half that of the memristor array.

Solid polymer electrolyte-based atomic switches: from materials to mechanisms and applications.

As miniaturization of semiconductor memory devices is reaching its physical and technological limits, there is a demand for memory technologies that operate on new principles. Atomic switches are nanoionic devices that show repeatable resistive switching between high-resistance and low-resistance states under bias voltage applications, based on the transport of metal ions and redox reactions in solids. Their essential structure consists of an ion conductor sandwiched between electrochemically active and inert electrodes. This review focuses on the resistive switching mechanism of atomic switches that utilize a solid polymer electrolyte (SPE) as the ion conductor. Owing to the superior properties of polymer materials such as mechanical flexibility, compatibility with various substrates, and low fabrication costs, SPE-based atomic switches are a promising candidate for the next-generation of volatile and nonvolatile memories. Herein, we describe their operating mechanisms and key factors for controlling the device performance with different polymer matrices. In particular, the effects of moisture absorption in the polymer matrix on the resistive switching behavior are addressed in detail. As potential applications, atomic switches with inkjet-printed SPE and quantum conductance behavior are described. SPE-based atomic switches also have great potential in use for neuromorphic devices. The development of these devices will be enhanced using nanoarchitectonics concepts, which integrate functional materials and devices.

This article reviews a series of works starting with the author's 2011 paper on solid polymer electrolyte-based atomic switches, and describes the current status and future prospects for this technology.

Nitrogen Plasma Enhanced Low Temperature Atomic Layer Deposition of Magnesium Phosphorus Oxynitride (MgPON) Solid-State Electrolytes.

Solid-state batteries (SSBs) that use solid electrolytes instead of flammable liquid electrolytes have the potential to generate higher specific capacity and offer better safety. Magnesium (Mg) based SSBs with Mg metal anodes are considered to be one of the most promising energy storage candidates, because it gives high theoretical volumetric capacities of 3830 mAh cm-3 . Here, we demonstrate an atomic layer deposition (ALD) process with a double nitrogen plasma process that successfully produces nitrogen-incorporated magnesium phosphorus oxynitride (MgPON) solid-state electrolyte (SSE) thin films at a low deposition temperature of 125 °C. The ALD MgPON SSEs exhibit an ionic conductivity of 0.36 and 1.2 µS cm-1 at 450 and 500 °C, respectively. The proposed ALD strategy shows the ability of conformal deposition nitrogen-doped SSEs on pattered substrates and is attractive for using nitride ion-conducing films as protective or wetting interlayers in solid-state Mg and Li batteries.

Neuromorphic System for Edge Information Encoding: Emulating Retinal Center-Surround Antagonism by Li-Ion-Mediated Highly Interactive Devices.

Center-surround antagonism, a key mechanism in the retina, contributes to the encoding of edge contrast rather than of the overall information on a visual image. Here, a neuromorphic system consisting of multiple ionic devices is built, where each device has a lithium cobalt oxide channel arranged on a common lithium phosphorus oxynitride electrolyte. Because of the migration of Li ions between the channels through the electrolyte, the devices are highly interactive, as is seen with retinal neurons. On the basis of the excitation of single devices and device-to-device inhibition, the system successfully emulates the antagonistic center-surround receptive field and the Mach band effect in which perceived contrast is enhanced at the edges between dark and bright regions. Furthermore, a two-dimensional array system is simulated to implement edge detection for real images. This scheme enables computer vision tasks with simple and effective operations, owing to the intrinsic properties of the materials employed.

Revival of "dead" memristive devices: case of WO3-x.

Inappropriate operation could make a memristive device "dead" and cause the loss of resistive switching performance. In this study, the revival of "dead" devices was investigated in the case of WO3-x-based memristive devices. It is believed that inappropriate operation with a high-voltage pulse creates an ordered structure of oxygen vacancies and such an ordered structure makes the normal reset process fail. By precisely controlled voltage sweeping at certain compliance currents, a "dead" device can be revived. The revival operation disrupts the ordered structure by Joule heating and recovers Schottky-like barrier modulation-based switching.

Size-Controlled AgI/Ag Heteronanowires in Highly Ordered Alumina Membranes: Superionic Phase Stabilization and Conductivity.

Nanoscaled ionic conductors are crucial for future nanodevices. A well-known ionic conductor, AgI, exhibited conductivity greater than 1 Ω(-1) cm(-1) in α-phase and transformed into poorly conducting ß-/γ-phase below 147 °C, thereby limiting applications. Here, we report that transition temperatures both from the ß-/γ- to α-phase (Tc↑) and the α- to ß-/γ-phase (Tc↓) are tuned by AgI/Ag heteronanowires embedded in anodic aluminum oxide (AAO) membranes with 10-30 nm pores. Tc↑ and Tc↓ shift to correspondingly higher and lower temperature as pore size decreases, generating a progressively enlarged thermal hysteresis. Tc↑ and Tc↓ specifically achieve 185 and 52 °C in 10 nm pores, and the final survived conductivity reaches ∼8.3 × 10(-3) Ω(-1) cm(-1) at room temperature. Moreover, the low-temperature stabilizing α-phase (down to 21 °C, the lowest in state of the art temperatures) is reproducible and survives further thermal cycling. The low-temperature phase stabilization and enhancement conductivity reported here suggest promising applications in silver-ion-based future nanodevices.

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.

Synaptic plasticity and memory functions achieved in a WO3-x-based nanoionics device by using the principle of atomic switch operation.

A compact neuromorphic nanodevice with inherent learning and memory properties emulating those of biological synapses is the key to developing artificial neural networks rivaling their biological counterparts. Experimental results showed that memorization with a wide time scale from volatile to permanent can be achieved in a WO3-x-based nanoionics device and can be precisely and cumulatively controlled by adjusting the device's resistance state and input pulse parameters such as the amplitude, interval, and number. This control is analogous to biological synaptic plasticity including short-term plasticity, long-term potentiation, transition from short-term memory to long-term memory, forgetting processes for short- and long-term memory, learning speed, and learning history. A compact WO3-x-based nanoionics device with a simple stacked layer structure should thus be a promising candidate for use as an inorganic synapse in artificial neural networks due to its striking resemblance to the biological synapse.

Room temperature redox reaction by oxide ion migration at carbon/Gd-doped CeO2 heterointerface probed by an in situ hard x-ray photoemission and soft x-ray absorption spectroscopies.

In situ hard x-ray photoemission spectroscopy (HX-PES) and soft x-ray absorption spectroscopy (SX-XAS) have been employed to investigate a local redox reaction at the carbon/Gd-doped CeO2 (GDC) thin film heterointerface under applied dc bias. In HX-PES, Ce3d and O1s core levels show a parallel chemical shift as large as 3.2 eV, corresponding to the redox window where ionic conductivity is predominant. The window width is equal to the energy gap between donor and acceptor levels of the GDC electrolyte. The Ce M-edge SX-XAS spectra also show a considerable increase of Ce3+ satellite peak intensity, corresponding to electrochemical reduction by oxide ion migration. In addition to the reversible redox reaction, two distinct phenomena by the electrochemical transport of oxide ions are observed as an irreversible reduction of the entire oxide film by O2 evolution from the GDC film to the gas phase, as well as a vigorous precipitation of oxygen gas at the bottom electrode to lift off the GDC film. These in situ spectroscopic observations describe well the electrochemical polarization behavior of a metal/GDC/metal capacitor-like two-electrode cell at room temperature.

Effects of Mg Doping to a LiCoO2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors.

Artificial synapses with ideal functionalities are essential in hardware neural networks to allow for energy-efficient analog computing. However, the realization of linear and symmetric weight updates in real synaptic devices has proven challenging and ultimately limits the online training capabilities of neural network systems. Herein, we investigate the effect of Mg doping on a LiCoO2 (LCO) channel in a Li ion-gated synaptic transistor, so as to improve long-term and short-term plasticity. Two transistor structures, based on a lithium phosphorus oxynitride electrolyte, were examined by using undoped LCO and Mg-doped LCO as the channel material between the source and drain electrodes. It was found that Mg doping increased the initial channel conductance by 3 orders of magnitude, which is probably due to the substitution of Co3+ by Mg2+ and the compensation of hole creation. It was further found that the doped channel transistor showed good retention characteristics and better linearity of long-term potentiation and depression when voltage pulses were applied to the gate electrode. The improved retention and linearity are attributed to an extended range of the insulator-to-conductor transition by Mg doping and Li-ion extraction/insertion cooperated in the LCO channel. Using the obtained synaptic weight update, artificial neural network simulations demonstrated that the doped channel transistor shows an image recognition accuracy of ∼80% for handwritten digits, which is higher than ∼65% exhibited by the undoped channel transistor. Mg doping also improved short-term plasticity such as paired-pulse facilitation/depression and Hebbian spike timing-dependent plasticity. These results indicate that elemental doping to the channel of Li ion-gated synaptic transistors could be a useful procedure for realizing robust neuromorphic systems based on analog computing.

Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking.

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.

Proton-Gated Synaptic Transistors, Based on an Electron-Beam Patterned Nafion Electrolyte.

Neuromorphic processors using artificial neural networks are the center of attention for energy-efficient analog computing. Artificial synapses act as building blocks in such neural networks for parallel information processing and data storage. Herein we describe the fabrication of a proton-gated synaptic transistor using a Nafion electrolyte thin film, which is patterned by electron-beam lithography (EBL). The device has an active channel of indium-zinc-oxide (IZO) between the source and drain electrodes, which shows Ohmic behavior with a conductance level on the order of 100 µS. Under voltage applications to the gate electrode, the channel conductance is changed due to the injection and extraction of protons between the IZO channel and the Nafion electrolyte, emulating various synaptic functions with short-term and long-term plasticity. When positive (negative) gate voltage pulses are consecutively applied, the device exhibits long-term potentiation (depression) at the same number of steps as the number of input pulses. Based on these characteristics, an artificial neural network using this transistor shows ∼84% image recognition accuracy for handwritten digits. The subject transistor also successfully mimics paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. Finally, dynamical pattern image memorization is demonstrated in a 5 × 5 array of these synaptic transistors. The results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.

Short-term plasticity and long-term potentiation mimicked in single inorganic synapses.

Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1-4). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag(2)S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag(2)S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag(2)S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.

Conductance quantization and synaptic behavior in a Ta2O5-based atomic switch.

Quantized conductance was observed in a cation-migration-based resistive switching memory cell with a simple metal-insulator-metal (MIM) structure using a thin Ta(2)O(5) layer. The observed conductance changes are attributed to the formation and dissolution of a metal filament with an atomic point contact of different integer multiples in the Ta(2)O(5) layer. The results demonstrate that atomic point contacts can be realized in an oxide-based MIM structure that functions as a nanogap-based atomic switch (Terabe et al 2005 Nature 433 47). By applying consecutive voltage pulses at periodic intervals of different times, we also observed an effect analogous to the long-term potentiation of biological synapses, which shows that the oxide-based atomic switch has potential for use as an essential building block of neural computing systems.

Effects of Oxygen Partial Pressure and Substrate Temperature on the Structure and Morphology of Sc and Y Co-Doped ZrO2 Solid Electrolyte Thin Films Prepared via Pulsed Laser Deposition.

Scandium (Sc) and yttrium (Y) co-doped ZrO2 (ScYSZ) thin films were prepared on a SiO2-Si substrate via pulsed laser deposition (PLD) method. In order to obtain good quality thin films with the desired microstructure, various oxygen partial pressures (PO2) from 0.01 Pa to 10 Pa and substrate temperatures (Ts) from 25 °C to 800 °C were investigated. X-ray diffraction (XRD) patterns results showed that amorphous ScYSZ thin films were formed at room substrate temperature while cubic polycrystalline thin films were obtained at higher substrate temperatures (Ts = 200 °C, 400 °C, 600 °C, 800 °C). Raman spectra revealed a distinct Raman shift at around 600 cm-1 supporting a cubic phase. However, a transition from cubic to tetragonal phase can be observed with increasing oxygen partial pressure. Photoemission spectroscopy (PES) spectra suggested supporting analysis that more oxygen vacancies in the lattice can be observed for samples deposited at lower oxygen partial pressures resulting in a cubic structure with higher dopant cation binding energies as compared to the tetragonal structure observed at higher oxygen partial pressure. On the other hand, dense morphologies can be obtained at lower  PO2 (0.01 Pa and 0.1 Pa) while more porous morphologies can be obtained at higher PO2 (1.0 Pa and 10 Pa).

Edge-of-chaos learning achieved by ion-electron-coupled dynamics in an ion-gating reservoir.

Physical reservoir computing has recently been attracting attention for its ability to substantially reduce the computational resources required to process time series data. However, the physical reservoirs that have been reported to date have had insufficient computational capacity, and most of them have a large volume, which makes their practical application difficult. Here, we describe the development of a Li+ electrolyte-based ion-gating reservoir (IGR), with ion-electron-coupled dynamics, for use in high-performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which were stored as transient charge density patterns in an electric double layer, at the Li+ electrolyte/diamond interface. Performance for a second-order nonlinear dynamical equation task is one order of magnitude higher than memristor-based reservoirs. The edge-of-chaos state of the IGR enabled the best computational capacity. The IGR described here opens the way for high-performance and integrated neural network devices.

A floating gate negative capacitance MoS2 phototransistor with high photosensitivity.

Monolayer MoS2 exhibits interesting optoelectronic properties that have been utilized in applications such as photodetectors and light emitting diodes. For image sensing applications, improving the light sensitivity relies on achieving a low dark current that enables the detection weak light signals. Although previous reports on improving the detectivity have been explored with heterostructures and pn junction devices, some of these approaches lack CMOS compatibility processing and sufficient low dark current suppression. Steep slope transistors that overcome the Boltzmann tyranny can further enhance the performance in photodetectors by providing efficient extraction of photogenerated charges. Here, we report a monolayer MoS2 floating gate negative capacitance phototransistor with the integration of a hafnium-zirconium oxide ferroelectric capacitor. In this study, a SSmin of 30 mV dec-1, very low dark currents of 10-13-10-14 A, and a high detectivity of 7.2 × 1015 cm Hz1/2 W-1 were achieved under weak light illumination due to an enhancement in the photogating effect. In addition, its potential as an optical memory and as an optical synapse with excellent long-term potentiation characteristics in an artificial neural network was also explored. Overall, this device structure offers high photosensitivity to weak light signals for future low-powered optoelectronic applications.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications.

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Temperature effects on the switching kinetics of a Cu-Ta2O5-based atomic switch.

Voltage-current (I-V) measurements in a wide temperature range from 88 to 573 K demonstrated the effects of temperature on the switching behavior of a Cu/Ta(2)O(5)/Pt resistive memory cell that is referred to as a gapless-type atomic switch. After the forming process, the cells were SET from the OFF state to the ON state at a positive bias to the Cu electrode and then RESET from the ON state to the OFF state at a negative bias. In a previous study (Tsuruoka et al 2010 Nanotechnology 21 425205), it was demonstrated that the SET process corresponds to the reformation of a metal filament between the electrodes by the inhomogeneous nucleation and subsequent growth of Cu whereas the RESET process can be attributed to the Joule-heating-assisted dissolution of the metal filament. In the work described here, we observed that the voltages at which the cells are SET and RESET (SET and RESET voltages) decreased in magnitude with an increase in temperature. From calculations of the nucleation rate of Cu nuclei based on the classical nucleation theory, it was found that the observed temperature variation of the SET voltage is primarily determined by supersaturation in the vicinity of the Pt electrode, which is controlled by the application of positive bias. The supersaturation required for spontaneous growth of a Cu nucleus decreases with increasing temperature, resulting in lower SET voltages at higher temperatures. The RESET voltage is determined by the thermal stability of the metal filament formed. Moreover, using the temperature variation in cell resistances of the ON state, the growth speed of the Cu nucleus after the nucleation was found to decease with increasing temperature. These results are consistent with our switching model.

Switching kinetics of a Cu2S-based gap-type atomic switch.

The switching time of a Cu(2)S-based gap-type atomic switch is investigated as a function of temperature, bias voltage, and initial off-resistance. The gap-type atomic switch is realized using a scanning tunneling microscope (STM), in which the formation and annihilation of a Cu-atom bridge in the vacuum gap between the Cu(2)S electrode and the Pt tip of the STM are controlled by a solid-electrochemical reaction. Increasing the temperature decreases the switching time exponentially with an activation energy of about 1.38 eV. Increasing the bias voltage also shortens the switching time exponentially, exhibiting a greater exponent for the lower bias than for the higher bias. Furthermore, faster switching has been achieved by decreasing the initial off-resistance between the Cu(2)S electrode and STM tip. On the basis of these results, we suggest that, in addition to the chemical reaction, the electric field in the vacuum gap plays a significant role in the operation of a gap-type atomic switch. This investigation advances our understanding of the operating mechanism of an atomic switch, which is a new concept for future electronic devices.