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

Abstract

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.

Inverted input method for computing performance enhancement of the ion-gating reservoir

“…An inverted input method is a simple method in which inverted input is generated by an inversion (linear transformation) of the original input and additionally applied to PRC with original input, leading to far lower processing costs for maintaining transient states of the reservoir than general masking methods. An "ion-gating reservoir (IGR)" which uses the nonlinear response of transistors with ions, [20][21][22][23] was employed as a model case to evaluate the effect of using inverted inputs on the prediction error of a second-order nonlinear equation task. Furthermore, to investigate the cause of the error reduction by the inverting input, we quantitatively evaluated high dimensionality and STM, which are requirements for PRC.…”

“…A masking process improves high dimensionality by adding a certain amount of modulation to the desired signal and inputting it into the physical reservoir to maintain its transient state. 20,24) However, this has its drawbacks, such as requiring complex processing with adjustment of hyperparameters and making the data length of input very long. This causes an urge for the development of different approaches for the improvement of high dimensionality.…”

“…1,25) In this present study, the effect of the inverted input has been investigated with a second-order nonlinear equation task, which is a typical benchmark task of PRCs. [16][17][18][19][20][21][22] The target output y t (k) for the task is determined from the…”

“…Moreover, the NMSE under condition with u′(k) was the smallest NMSE in the second-order nonlinear equation task among reported PRC. [16][17][18][19][20][21][22] In order to clarify the origin of such performance enhancement of the IGR due to the introduction of the inverted input method, we quantitatively analyzed the high dimensionality and STM of the system under the two conditions (i.e., input without u′(k) and with u′(k)). As discussed above, introducing an inverted input provides diverse reservoir states due to the nonlinearity and asymmetry of the mapping function.…”

“…Therefore, the inverted input surely improved the high dimensionality of this system. The high dimensionality in PRC is achieved by the presence of multiple nodes that behave independently, which can be characterized by a correlation coefficient r between each node X i and X j≠i defined by following equation 20,21) 5…”