Artificial Synaptic Performance with Learning Behavior for Memristor Fabricated with Stacked Solution-Processed Switching Layers

Shen, Zongjie; Zhao, Chun; Zhao, Tianshi; Xu, Wangying; Liu, Yina; Qi, Yanyuan; Mitrović, Ivona Z.; Yang, Li; Zhao, Ce Zhou

doi:10.1021/acsaelm.0c01094

Cited by 25 publications

(16 citation statements)

References 69 publications

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“…Current IMC synaptic devices (three-terminal transistor, twoterminal memristor, and atomic switch) represented by the popular nonvolatile memory (NVM) are significantly affected by the purely electronic control, which has an important influence on electrical input/output, delay of resistance-capacitance (RC) interconnection, and circuit crosstalk. [8][9][10][11]19,[21][22][23][24][25][26][27] 1.2. Artificial Optical In-Memory Computing Sensor Synapses Biologically, the normal life of human beings cannot be separated from vision, and the content of vision depends on the light reflected on the surface of things.…”

Section: Artificial Imc Synapsesmentioning

confidence: 99%

“…[8,13] Compared with the electrical synapse, the chemical synapse relies on the terminal of the presynaptic neuron to release special chemicals (neurotransmitters) as media for transmitting information to affect the postsynaptic neuron, which is closely related to the high-level neural activities, such as memorizing and learning. [19][20][21][22]31]…”

Section: Biological Synapsementioning

confidence: 99%

“…In general, STP behaviors are always reflected by paired-pulse facilitation (PPF) and paired-pulse depression (PPD). [19,21,22] PPF or PPD is demonstrated by the response of the synaptic device to two consecutive external stimuli. [8][9][10][11] After the first stimuli A 1 , the synaptic device may exhibit an increased (decreased) current to respond to the second stimuli A 2 , which results in PPF (PPD).…”

Section: Biomimetic Synaptic Behaviors Of the Synaptic Devicementioning

confidence: 99%

“…Apart from pattern/image recognition, some other researchers also developed the application of mathematic calculation/logic operation of optical IMCS synapses. [9,22] Pi et al emulated the logic calculation of optical synapses fabricated with 0D Si-NC. [147] Before the mathematic calculation, the device demonstrated a linear increment of EPSC with the increase of optical pulse number.…”

Section: Mathematic Calculation/logic Operationmentioning

confidence: 99%

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Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

Shen

Zhao

Kang

et al. 2022

Advanced Intelligent Systems

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Emerging optical synapses with in‐memory computing sensor (IMCS) performance are considered to be one of the most effective candidates to circumvent the bottleneck of the current Von Neumann structure while developing neuromorphic systems with higher effectiveness and lower energy consumption. Biomimetic properties of optical IMCS synapses in function and form indicate the higher requirements for utilized functional materials, such as stronger optical sensitivity and lower energy dissipation. Because of properties with high optical‐sensitivity efficiency and excellent electrical conductivity, low‐dimensional nanomaterials have received tremendous interest in modulating optical‐induced synaptic plasticity and emulating optical‐triggered neuromorphic activity of optical IMCS synapses. Herein, a comprehensive summary of optical IMCS synapses based on low‐dimensional nanomaterials is introduced systematically for the first time, including 0D, 1D, and 2D materials. In addition, the content of biomimetic synaptic characteristics, materials classification, operation mechanism, and neuromorphic applications of optical IMCS synapses based on low‐dimensional nanomaterials are also summarized in this work. At last, the challenges and outlook related to artificial optical IMCS synapses with low‐dimensional nanomaterials are provided.

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Section: Artificial Imc Synapsesmentioning

confidence: 99%

Section: Biological Synapsementioning

confidence: 99%

Section: Biomimetic Synaptic Behaviors Of the Synaptic Devicementioning

confidence: 99%

Section: Mathematic Calculation/logic Operationmentioning

confidence: 99%

See 2 more Smart Citations

Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

Shen

Zhao

Kang

et al. 2022

Advanced Intelligent Systems

Self Cite

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“…FIGURE 12(d) shows a cross sectional SEM image of the ZnO2 active layer, it can be seen that the thickness is uniform, and the height is almost 100 nm throughout the area. Spin coated memristor was reported Ag/SP-GaOx/SP-AlOx/ITO [126]. The memristor exhibited 2 × 104 s retention time, 1000 endurance cycles, and 2 × 104 ON/OFF ratio.…”

Section: A Spin Coating Techniquementioning

confidence: 99%

Memristor Fabrication Through Printing Technologies: A Review

Ali

Khan

et al. 2021

IEEE Access

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Memristor got the significant attraction to be the next generation memory device due to its small size, simple architecture, high density, and low power consumption. A memristor is a passive twoterminal primary electronic device with a built-in non-volatile memory function that can be fabricated in a crossbar structure. In the literature, independent studies have been reported on memristor manufacturing concerning the spatial resolution of the crossbar electrodes, and thickness of the active layer. However, this paper presents a comprehensive review different from others by comparing all the additive manufacturing technologies and their limitations for the development of a memristor array. A detailed study is performed about the pattern resolution, active layer fabrication, commonly used substrates, and device encapsulation methods. This review will provide a strong basis for the researchers, especially those working in the field of printed memristor devices.INDEX TERMS Memristor, printing technologies, thin films, crossbar electrodes, memristor fabrication.

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Light‐operated On‐chip Autonomous Vision Using Low‐dimensional Material Systems

Walia

2022

Adv Materials Technologies

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we can mimic human vision that employs a highly efficient imaging and recognition process? This will additionally provide a spill over opportunity to design artificial vision devices for the vision-impaired in our society.In this context, low-dimensional materials (LDMs) are a class of material systems that have their one, two, or three dimensions eliminated resulting in 2D, 1D, or 0D structures, respectively. The culling of dimensions brings to the fore exciting quantum confinement driven physics that can be exploited for a range of applications across sectors. The advent of large-area synthesis of LDMs has paved the way for integrated circuits, memory devices, and image sensors. A key feature of these materials is their ability to interact with light in unique ways. Research into 2D materials, 1D structures such as nanowires, and 0D structures such as quantum dots is continuously gaining momentum. A decisive move toward light-driven neuromorphics and machine vision is currently emerging particularly in the last 5 years and is expected to drive artificially intelligent systems to an unprecedented level in the coming years.These developments in new materials and advances in nanofabrication have coincided with strong emerging interest for light-driven cognition in devices. This has meant that there is an impetus toward understanding the physics and chemistry of these materials. The role played by defects in these material systems is not a detrimental one anymore. In fact, defects are now being readily harnessed for exciting functionalities. [25] This perspective is designed to provide a critical and futuristic assessment of optical/optoelectronic technologies that have emerged in the last 5 years to perform the collective function of a human eye, optic nerve, and neurons in the brain with an aim to cater to a broad readership interested in this research field. The author makes the case of why a combination of LDM systems and the new physics that emerges on their interaction with light could give rise to optoelectronic properties that can enable highly-efficient brain-like imaging and computing systems. This perspective will assess the different material and device design configurations that have been implemented tilldate and provide a discussion on future pathways that can overcome current technological limitations by rendering ultrafast processing speed due to high bandwidth, low parasitic crosstalk, and ultralow power consumption in self-learning miniaturized chips.Human-brain inspired machine vision can revolutionise new technologies across sectors. Monolithic devices that are able to achieve image capture, processing, and storage with ultra-low energy requirements can result in smart automation and enhance industrial output (both quality and quantity). This requires tapping into emerging novelties in materials physics, optical materials, and neuromorphic hardware. In this perspective, the author discusses the role of electrophotoactive low-dimensional materials and how their unique intrinsic properties can be...

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Artificial Synaptic Performance with Learning Behavior for Memristor Fabricated with Stacked Solution-Processed Switching Layers

Cited by 25 publications

References 69 publications

Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

Memristor Fabrication Through Printing Technologies: A Review

Light‐operated On‐chip Autonomous Vision Using Low‐dimensional Material Systems

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