Emerging memristors and applications in reservoir computing

doi:10.1007/s11467-023-1335-x

Front. Phys.

2024, Vol. 19

Issue (1) : 13401 https://doi.org/10.1007/s11467-023-1335-x

TOPICAL REVIEW

Emerging memristors and applications in reservoir computing

Hao Chen, Xin-Gui Tang(

), Zhihao Shen, Wen-Tao Guo, Qi-Jun Sun, Zhenhua Tang, Yan-Ping Jiang

School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China

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Abstract

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

Keywords reservoir computing memristor resistive switching artificial synapse neuromorphic computing

Corresponding Author(s): Xin-Gui Tang

Issue Date: 13 September 2023

Cite this article:

Hao Chen,Xin-Gui Tang,Zhihao Shen, et al. Emerging memristors and applications in reservoir computing[J]. Front. Phys. , 2024, 19(1): 13401.

URL:

https://academic.hep.com.cn/fop/EN/10.1007/s11467-023-1335-x
https://academic.hep.com.cn/fop/EN/Y2024/V19/I1/13401

Fig.1 (a) Schematic illustration of Pt/Mg:HfO_x/TiN structure. (b) The RS characteristics by changing the maximum sweep voltages. (a, b) Reproduced from Ref. [39]. (c) Schematic illustration of Pt/Ta₂O₅/HfO₂/TiN structure. (d) Analysis of the RS characteristics. (c, d) Reproduced from Ref. [40]. (e) Typical cross-sectional TEM images of device A and device B. (f) Neural network classification accuracy. (e, f) Reproduced from Ref. [41].

Fig.2 (a) Fabrication procedures of the Gd_xO_y memristors with CSA graphene Bes [44]. (b) The structure of Graphene sheets with different types of defects and varying sizes/concentrations. (c) The process of CF modulated by defective graphene. (b, c) Reproduced from Ref. [45]. (d) The diagram depicts the structure of the memristor with graphene insertion. (e) The switching characteristics with different sized nanopores. (d, e) Reproduced from Ref. [46]. (f) Schematic illustration of the 4 × 4 crossbar array based on Ag/MoS2/Pt structure [47]. (g) Schematic illustration of the memristor with WS₂ layer [48]. (h) TEM and FFT images of the WS2 device. (i) TEM image of the different area of the WS₂ device. (h, i) Reproduced from Ref. [48].

Fig.3 (a) Schematic diagram of the Au/MAPbI₃/ITO memristor, cross-sectional SEM image of the memristor and molecular structure. (b) Schematic illustration of a biological neuron and its experimental circuit. (a, b) Reproduced from Ref. [61]. (c) The structure of the Pd/LBFO/LSMO/STO/Si device. (d) XRD patterns. (e) AFM morphology diagram. (f) PFM phase diagram. (g) PFM hysteresis loop. (h) XPS full spectrum. (c?h) Reproduced from Ref. [62].

Fig.4 (a) Cross-sectional HADDF TEM image of the Au/BM-SFO/SRO device. (b?g) The corresponding EDS mapping image of elements. First-principles calculation for band structures. (h?j) Band structure and PDOS plots of SrFeO_x. (k?q) Schematic RS process in the SFO memristor. (a?q) Reproduced from Ref. [67].

Fig.5 (a) Schematics illustration of a synapse how to work. (b) Device structure based on APP. (c) Chemical structure of APP. (d) The cross-section of APP-based device. (a?d) Reproduced from Ref. [77]. (e) Schematics diagram of the ITO/F₁₆CuPc/Al device. (f) The switching characteristics of the F₁₆CuPc-based device with multiple sweep voltage. (e, f) Reproduced from Ref. [78]. (g) Lignin is widely found in plants. (h) Schematics diagram of Au/lignin/ITO/PET device. (i) Schematics diagram of the flexible memristor. (j) Schematics diagram of chemical structure of lignin. (g?j) Reproduced from Ref. [79].

Fig.6 (a) The process of emoticon recognition. (b) The pixel image is converted into pulses of current of different amplitudes. (c) Current response to 16 pulse streams and 14 states can be separated efficiently. (d) Relation between classification accuracy and training epochs. (e) False color confusion matrix. (a?e) Reproduced from Ref. [83]. (f) The process of handwritten digit recognition. (g) Random input signals u test(k) are used to solve a second-order nonlinear dynamic problem. (h) The forecasting results from the memristor-based RC system. (f?h) Reproduced from Ref. [84].

Fig.7 (a) Schematics illustration of dynamic memristor-based RC system with virtual nodes. (b) The input and classification outcome of sinusoidal and square waveforms. (c) The result of Hénon map prediction. (d) The prediction error varies with the two test parameters M and V_max. (a?d) Reproduced from Ref. [91]. (e) Schematics diagram of the DM-RC hardware system. (f) Memristor-based RC system used to realize dynamic gesture recognition task. (e, f) Reproduced from Ref. [92].

Fig.8 (a) The delayed feedback RC system with virtual nodes for neural activity analysis. (b) False color confusion map. (c) Experimental results vs. ground truth in real-time analysis of neural firing pattern evolution. (d) Classification results in real-time analysis of neural synchronization. (a?d) Reproduced from Ref. [93]. (e) Schematic illustration of handwritten digit recognition. (f) False color confusion map, which is the result of handwritten digit recognition task. (g) Audio waveform corresponding to the digit nine pronounced by a speaker. (h) Results of spoken-digit recognition tasks. (e?h) Reproduced from Ref. [94].

Fig.9 (a) NG-RC, which is equivalent to nonlinear vector autoregression. (b) Structure of the NG-RC reservoir for three-dimensional timing signals predicting. (a, b) Reproduced from Ref. [100].

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