Bioinspired bio-voltage memristors

Fu, Tianda; Liu, Xiaomeng; Gao, Hongyan; Ward, Joy E.; Yin, Bing; Wang, Zhongrui; Ye, Zhuo; Walker, David; Yang, June‐Mo; Chen, Jianhan; Lovley, Derek R.; Yao, Jianing

doi:10.1038/s41467-020-15759-y

Cited by 163 publications

(205 citation statements)

References 60 publications

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“…We conclude that for optical nanosources with ∼0.01 μm 2 mode volume, intensity modulated optical spiking with sub-nanosecond refractory time responses can be achieved upon receiving exceptionally low (sub-10 mV) incoming synaptic-like activation signals, close to the shot-noise limit. This is lower than the amplitude of 100 mV in biological counterparts and much smaller than the typical switching voltages (0.2-2 V) of memristive devices [42]. Notably, we conclude the optical energy per spike is roughly constant and therefore, at some extent, almost independent of the incoming modulating frequency signal, a situation markedly different from standard current modulation methods of light sources [31,37,43].…”

Section: Introductioncontrasting

confidence: 53%

“…This corresponds to a sub-fJ electrical pulse activation with a value close to the typical shotnoise receivers (∼0.13 fJ/bit) [68]. Remarkably, this enables activation of the spiking response using sub-10 mV pulses, Figure 5, which is lower than the amplitude of 50-120 mV in biological counterparts and even much smaller than the typical switching voltages (0.2-2 V) of memristive devices which are promising candidates to emulate biological computing [42]. We note the energy of the incoming stimulus for triggering the all-or-nothing (excitable) spiking response depends on the selected bias quiescent voltage.…”

Section: Optical Spiking Dynamic Propertiesmentioning

confidence: 99%

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NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

2020

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AbstractEvent-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub-λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub-λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.

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Section: Introductioncontrasting

confidence: 53%

Section: Optical Spiking Dynamic Propertiesmentioning

confidence: 99%

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

2020

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“…Meanwhile it can be observed that the reported response time scatters in a large scale in Table 2. Actually, the programming voltage and electrical stimulation history have tremendous Graphene/h-BN/ Ag (part of the self-selective memory) [105] Bidirectional 100 µA [40] Ag/Protein nanowires/Ag [86] Bidirectional >100 µA 10 6 <100 mV 0.4 mV dec −1 13 ms 29 ms >10 4 Nanowire Artificial synapses and neurons Ag/Al 2 O3/ SiO x /W [107] Unidirectional 100 µA…”

Section: Selectorsmentioning

confidence: 99%

“…The device shows four specific nociceptive behaviors: threshold, relaxation, allodynia, and hyperalgesia, according to the strength, duration, and repetition rate of the external stimuli. Fu et al [86] reported a bioinspired memristors based on an ultra-low-voltage RDTSM. The catalysis effect of the protein nanowires on metallization was utilized to achieve sub 100 mV threshold voltages, which are rarely seen in other TSM systems.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Design of a Controllable Redox‐Diffusive Threshold Switching Memristor

Sun

Song

Yin

et al. 2020

Adv Elect Materials

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In most cases, the current flows uniformly through the device in the HRS and is restricted to a local region with high conductance known as a conducting filament (CF) in the LRS. [3] Among them, a specific memory device is termed as conductivebridge RRAM (CBRAM), where the formation/rupture of metallic conductive filaments are dominated by cation migration and redox processes. [4] Meanwhile, volatile resistance switching behaviors are commonly observed in CBRAM as threshold switching (TS). Analogous to the nonvolatile electrochemical metallization mechanism in terms of materials and structures, [5] the electrical resistance of such a threshold switching memristor (TSM) could decrease by orders of magnitude when an electric field is applied due to the formation of CFs with active metal (such as Ag or Cu) atoms. [6] Differently, the resistance returns back spontaneously after the termination of the external bias, yielding a superior I-V nonlinearity and unique temporal conductance evolution dynamics. [7] Recent years, the threshold switching memristors based on active metals are also called "diffusive memristors" to emphasize the diffusion dynamics of the metal species. [8] To be more comprehensively, redox-diffusive threshold switching memristor (RDTSM) might be the best choice to demonstrate the overall switching behavior. [1a] RDTSM has gained significant attention due to its similar advantages to RRAM, such as the simple structure, great fabricability and integrability, and compatibility with conventional CMOS technology. More importantly, it has great potential in many vital applications, such as two-terminal selectors with high nonlinearity, [9] high-powerefficient synaptic or neuronal devices with novel functions. [8,10] A specific application requires a correspondingly proper device. Figure 1 displays the measurement schematic of the key parameters of RDTSM, including selectivity (or nonlinearity), compliance currents, switching voltages (including threshold voltages and hold voltages),TS mode (unidirectional and bidirectional, as shown in Figure 1a,b, respectively), switching slopes (Figure 1c), response time (including delay and relaxation, as shown in Figure 1d) and endurance, which are all up to the material composition and device structure. [11] Therefore, With the rapid development of information technique in the big-data era, there is an extremely urgent demand for new circuit building blocks, represented by resistive switching memristors with high speed, high-density integration, and power-efficiency, to overcome the limitations of electronic device scaling and thus achieve non-von-Neumann neuromorphic computing. Redox-diffusive threshold switching memristors, based on the volatile formation/rupture of metallic conductive filaments, are attracting great attention for many novel applications, ranging from selectors to synaptic and neuronal devices. Here, how to design a proper redox-diffusive threshold switching memristor is comprehensively introduced, with particular focus on the effect of the devic...

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“…Based on the mechanism of filament formation, CBRAM can be also called programmable metallization cells [ 93,94 ] or electrochemical metallization. [ 95 ] With the structure in Figure 3, the top electrode is commonly made of Ag or Cu serving as the fast diffusing metal layer; [ 96 ] the bottom electrode is made of Pt, Au, or W which serves as a relatively inert layer; [ 97 ] and the dielectric layer is made of amorphous silicon, [ 98 ] oxide, [ 99 ] polymer, [ 100 ] or amorphous carbon. [ 101 ] Because the resistive switching behavior of CBRAM is based on metal‐ion migration between two electrodes, the conductive filament is composed of metal oxidation.…”

Section: Memristive Synaptic Devicesmentioning

confidence: 99%

Neuromorphic Engineering for Hardware Computational Acceleration and Biomimetic Perception Motion Integration

Wang

Chen

Huang

et al. 2020

Advanced Intelligent Systems

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Figure 4. CBRAM-based artificial synapses. a) A conceptual schematic of Ag-SiGe-Si CBRAM during switching. Reproduced with permission. [107] Copyright 2018, Springer Nature. b) Resistive switching characteristics of Ag/CrPS 4 /Au CBRAM. The inset is the schematic diagram of the device. c) The experiment data of LRS retention and the fitting curve of the result with exponential decay function. Reproduced under the terms of the Creative Commons Attribution 4.

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Bioinspired bio-voltage memristors

Cited by 163 publications

References 60 publications

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Design of a Controllable Redox‐Diffusive Threshold Switching Memristor

Neuromorphic Engineering for Hardware Computational Acceleration and Biomimetic Perception Motion Integration

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