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

Reservoir computing (RC) is a promising solution for achieving low power consumption neuromorphic computing, although the large volume of the physical reservoirs reported to date has been a serious drawback in their practical application. Here, we report the development of a few-molecule RC that employs the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly perturbated by the application of voltage-induced local pH changes in the vicinity of the molecules, and then used to perform RC of pattern recognition and prediction tasks. In spite of the small number of molecules employed, our system achieved good performance, including 95.1% to 97.7% accuracy in various nonlinear waveform transformations and 94.3% accuracy in solving a second-order nonlinear dynamic equation task. Our work provides a new concept of molecular computing with practical computation capabilities.Comment: 22 pages, 4 figure

A high-performance deep reservoir computing experimentally demonstrated with ion-gating reservoirs

While physical reservoir computing (PRC) is a promising way to achieve low power consumption neuromorphic computing, its computational performance is still insufficient at a practical level. One promising approach to improving PRC performance is deep reservoir computing (deep-RC), in which the component reservoirs are multi-layered. However, all of the deep-RC schemes reported so far have been effective only for simulation reservoirs and limited PRCs, and there have been no reports of nanodevice implementations. Here, as the first nanodevice implementation of Deep-RC, we report a demonstration of deep physical reservoir computing using an ion gating reservoir (IGR), which is a small and high-performance physical reservoir. While previously reported Deep-RC scheme did not improve the performance of IGR, our Deep-IGR achieved a normalized mean squared error of 0.0092 on a second-order nonlinear autoregressive moving average task, with is the best performance of any physical reservoir so far reported. More importantly, the device outperformed full simulation reservoir computing. The dramatic performance improvement of the IGR with our deep-RC architecture paves the way for high-performance, large-scale, physical neural network devices.Comment: 21 pages, 6 figure

Down-scaling of resistive switching to nanoscale using porous anodic alumina membranes

An advanced approach for resistive switching memory cells based on porous anodic alumina (Al2O3) membranes is reported. The effective resistive switching resulting in 6 orders of magnitude difference in resistivity between “on” and “off” states of the cell is achieved by specific electronic and ionic interaction between Ag nanowires filled in the membrane and an ionic conductor (AgxAsS2) deposited on the membrane by thermal evaporation. This easy and robust approach can be exploited for deposition of other ionic conductors for novel types of memories

Control of local ion transport to create unique functional nanodevices based on ionic conductors

The development of nanometer-scale devices operating under a new principle that could overcome the limitations of current semiconductor devices has attracted interest in recent years. We propose that nanoionic devices that operate by controlling the local transport of ions are promising in this regard. It is possible to control the local transport of ions using the solid electrochemical properties of ionic and electronic mixed conductors. As an example of this concept, here, we report a method of controlling the transport of silver ions of the mixed-conductor silver sulfide (Ag2S) crystal and basic research on nanoionic devices based on this mixed conductor. These devices show unique functions such as atom deposition, resistance switching, and quantum point contact switching. The switches operate through the formation and dissolution of an atomic bridge between the electrodes, and the behavior is realized by control of the local solid-state electrochemical reaction. Potential nanoionic devices utilizing the unique functions and characters that do not exist in conventional semiconductor devices are discussed

Experimental Demonstration of High‐Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10−2 and 8.37 × 10−5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection

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

Decision maker based on atomic switches

We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated ``decision-making'' capability that is known to be one of the most important intellectual abilities in human beings. We considered a popular decision-making problem studied in the context of reinforcement learning, the multi-armed bandit problem (MAB); the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by each volume of precipitated metal atoms, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. The ``tug-of-war (TOW) dynamics'' of the ASDM exhibits higher efficiency than conventional reinforcement-learning algorithms. We show analytical calculations that validate the statistical reasons for the ASDM to produce such high performance, despite its simplicity. Efficient MAB solvers are useful for many practical applications, because MAB abstracts a variety of decision-making problems in real-world situations where an efficient trial-and-error is required. The proposed scheme will open up a new direction in physics-based analog-computing paradigms, which will include such things as ``intelligent nanodevices'' based on self-judgment