Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks

Yang, Jiaqin; Wang, Ruopeng; Wang, Zhan‐Peng; Ma, Qinyuan; Mao, Jing‐Yu; Ren, Yi; Yang, Xiaoyang; Zhou, Ye; Han, Su‐Ting

doi:10.1016/j.nanoen.2020.104828

Cited by 126 publications

(112 citation statements)

References 67 publications

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“…The leaky-integrate-and-fire model has been applied to SNN-based neuromorphic systems because its low complexity facilitates practical application. Also, the leaky-integrate-and-fire model is considered for the neuromorphic systems because it can simultaneously emulate the integration/fire functions of spikes with spatiotemporal correlation ( Yang et al., 2020 ). Application of SNNs for neuromorphic computing requires learning rules different from those used in DNNs.…”

Section: Neural Networkmentioning

confidence: 99%

Emerging Materials for Neuromorphic Devices and Systems

et al. 2020

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Section: Neural Networkmentioning

confidence: 99%

Emerging Materials for Neuromorphic Devices and Systems

et al. 2020

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“…The typical response time will be in nanosecond range, as is widely ascertained in perovskite-based ionic memristor devices. [61,[73][74][75] Optoelectronically mimicking the integrated functionalities of pupil and retina to control the biological visual systems' responsiveness to light signals, the organometal halide perovskite based all-in-one machine vision therefore enables true-color, high-fidelity imaging of the examined object with a maximum 263% enhancement of the recognition accuracy in complex lighting environments.…”

Section: Resultsmentioning

confidence: 99%

Switchable Perovskite Photovoltaic Sensors for Bioinspired Adaptive Machine Vision

Chen

Zhang

Liu

et al. 2020

Advanced Intelligent Systems

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Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.

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“…can be roughly grouped into oxides (such as SiO 2 , [17,21] HfO 2 , [18,23] TiO 2 , [19] ZnO, [20] CuO, [14] CoO, [22] ZrO 2 , [24] NbO x , [25] and VO 2 [26] ), 2D materials (e.g., h-BN, [13] WSe 2 , [16] MoS 2 , [27] and MoS 2 / graphene [35] ), perovskites (e.g., Cs 3 Sb 2 Br 9 [32] and MAPbI 3 [33] ), chalcogenides (such as AsTeSi, [29] GeTe, [30] and GeTe 8 [28] ), and others (e.g., PEDOT:PSS, [34] ferritin [31] ).…”

Section: Methodsmentioning

confidence: 99%

“…So far, large amounts of materials have been reported as electrodes for volatile memristors. We grouped the common electrode materials into four categories based on composition of electrodes, including pure metal electrodes such as Au, [32] Ag, [16] Cu, [21] Pt, [18] W, [34] and Ti; [20] nitride-based electrodes such as TiN; [23,30] conductive oxide-based electrodes such as ITO; [22,33] and semiconductor electrodes such as Si [25] and so on.…”

Section: Methodsmentioning

confidence: 99%

“…With the increased applied voltage, the ions or vacancies in the interlayer would be drifted and accumulated near the interface of material and electrodes, resulting in self n-doping and self p-doping, respectively (as shown in Figure 3i). [33] Because of the self-doping effects, the surface work functions of the perovskite would change, which decreased the Schottky barrier and improved the carrier injection efficiency. When the electric field vanished, the ions would return back to their initial position and the device switched off.…”

Section: Ionic Effectsmentioning

confidence: 99%

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Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

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119

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks

Cited by 126 publications

References 67 publications

Emerging Materials for Neuromorphic Devices and Systems

Emerging Materials for Neuromorphic Devices and Systems

Switchable Perovskite Photovoltaic Sensors for Bioinspired Adaptive Machine Vision

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

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