Artificial Synapse Consisted of TiSbTe/SiCx:H Memristor with Ultra-high Uniformity for Neuromorphic Computing

Chen, Liangliang; Ma, Zhongyuan; Leng, Kangmin; Chen, Tong; Hu, Hongsheng; Yang, Yang; Li, Wei; Xu, Jun; Xu, Liang; Chen, Kunji

doi:10.3390/nano12122110

Cited by 3 publications

(2 citation statements)

References 30 publications

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Section: Introductionmentioning

confidence: 99%

“…Memristor is one such technology that aims to develop the next generation of highdensity and high-speed digital technology through its powerful analog computing capabilities. [6][7][8][9][10] In recent years, considerable progress was made in the development of large-scale and efficient parallel analog computing capabilities based on transition metal oxide memristors. For instance, a Ta/HfO2 128 × 64 1T1R crossbar array was constructed and used for analog signal processing and machine learning; [11][12][13][14] and a 32 × 32 WOxbased memristor crossbar array was used for image sparse processing.…”

Section: Introductionmentioning

confidence: 99%

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Memristor-Based Signal Processing for Compressed Sensing

et al. 2023

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With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Memristor-Based Signal Processing for Compressed Sensing

et al. 2023

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Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications

et al. 2023

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Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO4)]2/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

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Flexible Artificial Ag NPs:a–SiC0.11:H Synapse on Al Foil with High Uniformity and On/Off Ratio for Neuromorphic Computing

Zuo,

Zhou,

et al. 2024

Nanomaterials

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A neuromorphic computing network based on SiCx memristor paves the way for a next-generation brain-like chip in the AI era. Up to date, the SiCx–based memristor devices are faced with the challenge of obtaining flexibility and uniformity, which can push forward the application of memristors in flexible electronics. For the first time, we report that a flexible artificial synaptic device based on a Ag NPs:a–SiC0.11:H memristor can be constructed by utilizing aluminum foil as the substrate. The device exhibits stable bipolar resistive switching characteristic even after bending 1000 times, displaying excellent flexibility and uniformity. Furthermore, an on/off ratio of approximately 107 can be obtained. It is found that the incorporation of silver nanoparticles significantly enhances the device’s set and reset voltage uniformity by 76.2% and 69.7%, respectively, which is attributed to the contribution of the Ag nanoparticles. The local electric field of Ag nanoparticles can direct the formation and rupture of conductive filaments. The fitting results of I–V curves show that the carrier transport mechanism agrees with Poole–Frenkel (P–F) model in the high-resistance state, while the carrier transport follows Ohm’s law in the low-resistance state. Based on the multilevel storage characteristics of the Al/Ag NPs:a–SiC0.11:H/Al foil resistive switching device, we successfully observed the biological synaptic characteristics, including the long–term potentiation (LTP), long–term depression (LTD), and spike–timing–dependent plasticity (STDP). The flexible artificial Ag NPs:a–SiC0.11:H/Al foil synapse possesses excellent conductance modulation capabilities and visual learning function, demonstrating the promise of application in flexible electronics technology for high-efficiency neuromorphic computing in the AI period.

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Artificial Synapse Consisted of TiSbTe/SiCx:H Memristor with Ultra-high Uniformity for Neuromorphic Computing

Cited by 3 publications

References 30 publications

Memristor-Based Signal Processing for Compressed Sensing

Memristor-Based Signal Processing for Compressed Sensing

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications

Flexible Artificial Ag NPs:a–SiC0.11:H Synapse on Al Foil with High Uniformity and On/Off Ratio for Neuromorphic Computing

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