All-oxide-based and metallic electrode-free artificial synapses for transparent neuromorphic computing

Kumar, Naveen; Patel, Malkeshkumar; Nguyen, Thanh Tai; Bhatnagar, P. K.

doi:10.1016/j.mtchem.2021.100681

Cited by 18 publications

(15 citation statements)

References 53 publications

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“…Neuromorphic computing systems provide novel computing models with parallel processing, high efficiency, and low power consumption as a significant way of building next-generation computer architectures. , However, not only are robust analog memristors essential for high-performance neuromorphic computing systems but also the assistance of digital memristors is necessary. Analog memristors provide the basis for the construction of neural networks, while digital memristors provide access to on-chip logic operations, and in most cases, an initialization procedure is required to operate memristor arrays, which can be effectively accomplished by a digital memristor.…”

Section: Introductionmentioning

confidence: 99%

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

Wang

Zhang

et al. 2022

ACS Appl. Electron. Mater.

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As an emerging electronic device, a memristor facilitates the realization of neuromorphic computing systems. However, high-performance neuromorphic computing systems require not only robust analog memristors but also digital memristors with complementary functions. Here, a digital–analog integrated memristor based on Al/ZnO NPs/CuO NWs/Cu is proposed and demonstrated. First, the electrical properties of the CuO NWs based device are investigated, which exhibit a write-once-read-many-times (WORM) performance. Then, typical bipolar resistive switching behaviors are realized by spin-coating the ZnO NPs to form an Al/ZnO NPs/CuO NWs/Cu based memristor. Through tuning of the applied voltage, an abrupt-to-gradient transition of conductance is achieved in situ. Several analog behaviors, such as long-term potentiation/depression (LTP/LTD), and paired-pulse facilitation/depression (PPF/PPD) are effectively emulated by applying a series of specified electrical measurements to the memristor, which proves the achievement of a digital–analog integrated memristor. Furthermore, on the basis of the LTD and LTP behaviors of the memristor, neural network simulations for handwritten recognition are implemented. The results reveal high recognition accuracies of up to 93%, which are close to the performances for ideal memristors.

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Section: Introductionmentioning

confidence: 99%

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

Wang

Zhang

et al. 2022

ACS Appl. Electron. Mater.

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“…[5][6][7][8] Thus, reproducing biological synaptic plasticity is highly desired for realizing brain-inspired neuromorphic and hardware-based artificial intelligence. [9][10][11][12] Over the past ten years, some essential synaptic functions, such as neurotransmitter release, excitatory postsynaptic current (EPSC), long-term depression (LTD) or potentiation (LTP) of postsynaptic currents, spike-timing-dependent plasticity (STDP), have been successfully reproduced in various nanoscale electronic devices, including memristors, [13][14][15] fieldeffect transistors, [16][17][18] and ferroelectric tunnel junctions. [19][20][21] In particular, photoelectronic synapses have been rapidly developed, such as phototransistors and photomemristors, which enable flexible implementation of synaptic plasticity due to more modulation channels.…”

Section: Equalizing Excitation-inhibition Via the Ambipolar Photoresp...mentioning

confidence: 99%

Equalizing Excitation‐Inhibition via the Ambipolar Photoresponse of Al₂O₃/graphene Hybrid Neuromorphic Devices

Jiang

Wen

Zhong

et al. 2022

Adv Elect Materials

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Intrigued by the artificial intelligence and brain‐like neural networks, the photoelectronic neuromorphic devices that can simulate synaptic functions have been attracting wide attention. However, equalizing excitation‐inhibition has been seldom artificially realized despite its fundamental feature in a biological system. In this work, an artificial synapse is proposed based on a sandwiched Ag/Al2O3/graphene/ITO photodetector. The device exhibits a special ambipolar photoresponse at the millivoltage as a result of the interfacial trapping effects. Under the coupled light and electrical stimulus, the synaptic excitatory, inhibitory signals and their recovery are emulated on a single device via the positive and negative photoresponse, respectively. Further, the biologically dynamic balance of excitation and inhibition is reproduced on a simple analogous system integrating multiple photoelectronic synaptic cells. These remarkable results provide a potential foundation for building hardware units with neuromorphic architecture to mimic the complex human brain functionalities.

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“…Among optoelectronic synapses, transparent devices are drawing great attention due to their potential applications in “invisible” electronic products. − Until now, a few studies about transparent optoelectronic synapses have been reported. − In the design of transparent optoelectronic synapses, electrodes play an important role in the device performance. Transparent indium-tin oxide (ITO) and fluorine-doped tin oxide (FTO) electrodes are usually adopted no matter their relatively high cost and high requirement for equipment. , Ag-nanowires and Ga-doped ZnO as electrodes have also been reported. − However, the transparent electrode materials for efficient transparent optoelectronic synapses are still rare.…”

Section: Introductionmentioning

confidence: 99%

Transparent Optoelectronic Synapse Based on a CuI Electrode for Arithmetic Operation

Zhao

et al. 2022

ACS Appl. Electron. Mater.

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Transparent optoelectronic synapses are attracting great attention due to their great potentials in constructing "invisible" electronic products. In this work, CuI, a p-type semiconductor with good conductivity and transparency, is adopted as the electrode material. A transparent optoelectronic synapse is fabricated with a structure of CuI/copper-phthalocyanine (CuPc)/indium-tin oxide. The device shows a sensitive and linear response to the light stimulation with a wavelength of 660 nm. Some basic functions of biological synapses, such as pairedpulse facilitation, spike-rate-dependent plasticity, spike-numberdependent plasticity, and so forth, are successfully simulated by this transparent optoelectronic synapse. Furthermore, based on the high linear relationship between the device response and the number of light pulses, basic arithmetic operations of addition, subtraction, multiplication, and division within 20 are realized. The experimental results not only show the good performance of the device as an artificial synapse but also illustrate the great application potential of CuI in transparent and metallic electrode-free optoelectronics.

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All-oxide-based and metallic electrode-free artificial synapses for transparent neuromorphic computing

Cited by 18 publications

References 53 publications

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

Equalizing Excitation‐Inhibition via the Ambipolar Photoresponse of Al₂O₃/graphene Hybrid Neuromorphic Devices

Transparent Optoelectronic Synapse Based on a CuI Electrode for Arithmetic Operation

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All-oxide-based and metallic electrode-free artificial synapses for transparent neuromorphic computing

Cited by 18 publications

References 53 publications

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

Equalizing Excitation‐Inhibition via the Ambipolar Photoresponse of Al2O3/graphene Hybrid Neuromorphic Devices

Transparent Optoelectronic Synapse Based on a CuI Electrode for Arithmetic Operation

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Equalizing Excitation‐Inhibition via the Ambipolar Photoresponse of Al₂O₃/graphene Hybrid Neuromorphic Devices