All‐Flexible Artificial Reflex Arc Based on Threshold‐Switching Memristor

Wang, Depeng; Zhao, Shufang; Li, Linlin; Wang, Lili; Cui, Shaowei; Wang, Shuo; Lou, Zheng; Shen, Guozhen

doi:10.1002/adfm.202200241

Cited by 42 publications

(31 citation statements)

References 38 publications

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“…These results provide us with a visualized understanding of the inhomogeneity in layered CIPS induced by Cu ion migration, though it is difficult to resolve the exact locations of Cu ions under an electric field in the lattice microscopically. The widely distributed inhomogeneity inspired an ionic conducting-based resistive-switching device relying on the large nonresponsive areas rather than the randomly formed conducting filament as demonstrated in traditional oxide memory devices. − In this way, we expect to enhance the stability of the resistive-switching device.…”

Section: Results and Discussionmentioning

confidence: 99%

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

Zhong

et al. 2023

ACS Nano

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The two-dimensional layered material CuInP 2 S 6 (CIPS) has attracted significant research attention due to its nontrivial physical properties, including room-temperature ferroelectricity at the ultrathin limit and substantial ionic conductivity. Despite many efforts to control its ionic conductance and develop electronic devices, such as memristors, improving the stability of these devices remains a challenge. This work presents a highly stable threshold-switching device based on the Cu/CIPS/graphene heterostructure, achieved after a comprehensive investigation of the activation of Cu's ionic conductivity. The device exhibits exceptional threshold-switching performance, including good cycling endurance, a high on/off ratio of up to 10 4 , low operation voltages, and an ultrasmall subthreshold swing of less than 1.8 mV/decade for the resistance-switching process. Through temperature-dependent electrical and Raman spectroscopy measurements, the stable resistive-switching mechanism is interpreted with a drifting and diffusion model of Cu ions under the electric field, rather than the conventional conducting filament mechanism. These results make the layered ferroionic CIPS material a promising candidate for information storage devices, demonstrating a compelling approach to achieving high-performance threshold-switching memristor devices.

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Section: Results and Discussionmentioning

confidence: 99%

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

Zhong

et al. 2023

ACS Nano

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“…The whole reflex arc exploits the bionic characteristics of memristors and provides a potential prospect of applications in soft robots and health monitoring. [ 214 ] It is expected that the sensing part of the memristor will be combined with the actuator in the future to provide more practical value.…”

Section: Biological and Memristive Central Nervous Systemmentioning

confidence: 99%

Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

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“…It is undeniable that flexible electronic devices have attracted much attention due to their bendability, stretchability, and application potential in wearable electronics. , In recent years, flexible electronic products have become more and more important in various scenes of modern life. Actually, the data storage/processing systems are the key link of intelligent flexible electronics.…”

Section: Introductionmentioning

confidence: 99%

In-Depth Physical Mechanism Analysis and Wearable Applications of HfO_x-Based Flexible Memristors

Zhu

Sun

Zhou

et al. 2023

ACS Appl. Mater. Interfaces

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Since memristors as an emerging nonlinear electronic component have been considered the most promising candidate for integrating nonvolatile memory and advanced computing technology, the in-depth reveal of the memristive mechanism and the realization of hardware fabrication have facilitated their wide applications in next-generation artificial intelligence. Flexible memristors have shown great promising prospects in wearable electronics and artificial electronic skin (e-skin), but in-depth research on the physical mechanism is still lacking. Here, a flexible memristive device with a Ag/HfO x /Ti/PET crossbar structure was fabricated, and a remarkable analog switching characteristic similar to synaptic behavior was observed. Through detailed data fitting and in-depth physical mechanism analysis, it is confirmed that the analog switching characteristics of the device are mainly caused by carrier tunneling. Furthermore, the memristive properties of the Ag/HfO x /Ag/PET device can be attributed to the conductive filaments formed by the redox reaction of the active metal Ag. Finally, the interfacial barrier is extracted by the Arrhenius diagram and the energy band diagram, which is drawn to clearly demonstrate the conduction mechanism of charge trapping in the device. Therefore, the HfO x -based flexible memristor with analog switching behavior and stable memory performance lays the foundation for cutting-edge applications in wearable electronics and smart e-skin.

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All‐Flexible Artificial Reflex Arc Based on Threshold‐Switching Memristor

Cited by 42 publications

References 38 publications

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

Memristor‐Based Intelligent Human‐Like Neural Computing

In-Depth Physical Mechanism Analysis and Wearable Applications of HfO_x-Based Flexible Memristors

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All‐Flexible Artificial Reflex Arc Based on Threshold‐Switching Memristor

Cited by 42 publications

References 38 publications

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP2S6 Heterostructure

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP2S6 Heterostructure

Memristor‐Based Intelligent Human‐Like Neural Computing

In-Depth Physical Mechanism Analysis and Wearable Applications of HfOx-Based Flexible Memristors

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Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

Robust Threshold-Switching Behavior Assisted by Cu Migration in a Ferroionic CuInP₂S₆ Heterostructure

In-Depth Physical Mechanism Analysis and Wearable Applications of HfO_x-Based Flexible Memristors