Error analysis for 
                
                  
                
                
                  
                    
                      l
                      q
                    
                  
                
                $l^{q}$
              -coefficient regularized moving least-square regression

Guo, Qin; Ye, Peixin

doi:10.1186/s13660-018-1856-y

Cited by 14 publications

(17 citation statements)

References 22 publications

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“…[50,53] Thus, based on the EDL mechanism, short-term synaptic plasticity can be mimicked on EDLTs. [54][55][56][57][58] Meantime, electrochemical doping/de-doping can also cause ionic species to trap in the channel and lead to the long-term changes in channel conductances. Thus, the electrochemical doping/dedoping mechanism in ECTs is similar to the long-term changes in the synaptic weights.…”

Section: Synaptic Plasticity Mimicked On Ionotronic Transistorsmentioning

confidence: 99%

“…Recently, biocompatible transparent starch-gated oxide neuromorphic transistors were proposed to simulate the PTP behavior. [57] Figure 4d shows the simulated PTP behavior triggered by five repeated presynaptic spikes (0.5 V, 10 ms) at 10 Hz with the V ds of 0.3 V. The PPF gain (A 2 /A 1 ) is lower than the PTP gain (A 5 /A 1 ), i.e., 1.36 vs 1.81. The behavior is similar to the biological PTP phenomenon.…”

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

“…Both PPF behavior and PPD behavior can also be mimicked on a single iontronic neuromorphic transistor and can be modulated by altering spiking conditions. [57] Before applying main presynaptic spikes, initial spikes were applied. When adjusting the amplitude of initial spikes, the transition between PPF and PPD were observed.…”

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

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Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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The von Neumann bottleneck constrains developments of conventional artificial intelligences (AIs) toward portable, energy efficient, and truly brain‐like systems. Biological synapses connecting neurons deliver neural action potentials by regulating neurotransmitters between presynaptic and postsynaptic membranes. In a similar way, ionotronic transistors and memristors transmit electronic signals through the migration of ionic species in dielectric and resistive switching electrolyte under external electrical field. Thus, utilizing ionotronic devices to emulate synapses and building bionic neural networks opens up a brand‐new path to realize hardware‐based AI. Moreover, it would allow the fabrication of an artificial perception learning system to mimic the human perception system with multi‐sensory learning abilities. This review presents ionotronic devices for neuromorphic engineering applications. The operation mechanisms and brain‐inspired synaptic responses and neural functions are discussed. At last, outlooks for ionotronic devices to construct bionic neural networks are provided.

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Section: Synaptic Plasticity Mimicked On Ionotronic Transistorsmentioning

confidence: 99%

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

Section: Short-term Synaptic Plasticitymentioning

confidence: 99%

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Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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“…Conventional methods to make white light-emitting diodes (WLEDs) consist of the combination of a blue emitting InGaN chip with the Y 3 Al 5 O 12 :Ce 3+ phosphor. [5,6] In recent years, trivalent and divalent rare earth ion-doped inorganic phosphors have been paid significant attention because of their potential application in environmentally friendly light-emitting diodes. [7][8][9] White light-emitting diodes are considered to be the third generation of solid-state lighting technology due to their advantages over traditional light technology such as superior colour uniformity, low energy consumption (less CO 2 exhaustion), environmental friendliness, long lifetime and high colour rendering index (CRI) values.…”

mentioning

confidence: 99%

Photoluminescence properties of Ca₂Al₂O₅:RE³⁺ (RE = Eu, Dy and Tb) phosphors for solid state lighting

Yerpude¹,

Panse

Dhoble

et al. 2017

Luminescence

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Ca Al O :Eu , Ca Al O :Dy and Ca Al O :Tb phosphors were synthesized using a combustion synthesis method. The prepared phosphors were characterized by X-ray powder diffraction for phase purity, by scanning electron microscopy for morphology, and by photoluminescence for emission and excitation measurements. The Ca Al O :Eu phosphors could be efficiently excited at 396 nm and showed red emission at 594 nm and 616 nm due to D → F and D → F transitions. Dy -doped phosphors showed blue emission at 482 nm and yellow emission at 573 nm. Ca Al O :Tb phosphors showed emission at 545 nm when excited at 352 nm. Concentration quenching occurred in both Eu and Dy phosphors at 0.5 mol%. Photoluminescence results suggested that the aluminate-based phosphor could be a potential candidate for application in environmentally friendly based lighting technologies.

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Interface Modification in Three‐Terminal Organic Memory and Synaptic Device

Zheng

Liao

Han

et al. 2020

Adv Elect Materials

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including floating-gate memory devices, [13] polymer memory devices, [14] and ferroelectric memory devices. [9] Generally, the structure of three-terminal organic memory devices is similar to the OFET except that a floating gate layer is added to the former between the semiconductor layer and the dielectric layer, through which the charges can be trapped or the polarization can be changed when the operation voltage is applied on the electrodes. Subsequently it leads to threshold voltage shift. It is reported that the electrical properties of OFET can be improved by modifying the interface between semiconductor layer and dielectric layer [15-17] since the charge transport is correlated to the interfaces. In view of this, interface engineering is in high demand to obtain excellent electrical characteristics. For three-terminal organic memory devices, the characteristics of the interface such as interfacial adhesion, [18] morphology, [19] surface diploes, energy level alignment, [20] hydrophilic/hydrophobic property [21] affect the charge transport of device. To control the interfacial property, some methods of interface modification are proposed which includes 1) inserting SAMs or polymer layer as the interfacial layer between dielectric layer and semiconductor layer and 2) pretreating the dielectric layer with ultraviolet-ozone (UVO), plasma, [22] or oxygen content technology. [23,24] Among these methods, SAMs are the most common and effective way to modify the interface because the interfacial properties such as morphology, hydrophilic/hydrophobic, and so on are adjustable by designing specific self-assembled molecules. [25] Nowadays, due to the physical separation of the memory unit from the central processor, the data shuttling between them consumes a large amount of energy with lower work efficiency, inducing memory wall effect. [26] Neuromorphic computing, by contrast, can break the "Von Neumann bottleneck" since these computers can simultaneously mimic the spatiotemporal learning and inference of the synapse in the human brain. [27] Three-terminal organic memory devices can be used to simulate excitatory post-synaptic current/potential (EPSC/ EPSP), synaptic weight, short-term plasticity (STP), long-term plasticity (LTP), and other functions of the artificial synapses, making artificial synapses a very popular research topic. [28,29] Similar to the mechanism of three-terminal organic memory devices, the properties of artificial synaptic devices based on three-terminal structure are also related to the interface between dielectric layer/semiconductor layer and source-drain Interface is a valuable research area for the three-terminal organic memory device, which is an important member of memory family, as the transport and trapping operation of charge carriers are related to the interfaces between semiconductor/dielectric layers and source-drain electrodes/semiconductor layer. This progress report highlights the developments of the interface effect for three-terminal organic memory devices. First, the goals of...

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Error analysis for l q $l^{q}$ -coefficient regularized moving least-square regression

Cited by 14 publications

References 22 publications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Photoluminescence properties of Ca₂Al₂O₅:RE³⁺ (RE = Eu, Dy and Tb) phosphors for solid state lighting

Interface Modification in Three‐Terminal Organic Memory and Synaptic Device

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Error analysis for l q $l^{q}$ -coefficient regularized moving least-square regression

Cited by 14 publications

References 22 publications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Photoluminescence properties of Ca2Al2O5:RE3+ (RE = Eu, Dy and Tb) phosphors for solid state lighting

Interface Modification in Three‐Terminal Organic Memory and Synaptic Device

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Product

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Photoluminescence properties of Ca₂Al₂O₅:RE³⁺ (RE = Eu, Dy and Tb) phosphors for solid state lighting