Dual-Functional Long-Term Plasticity Emulated in IGZO-Based Photoelectric Neuromorphic Transistors

He, Yongli; Nie, Sha; Li, Rui; Jiang, Shanshan; Shi, Yi; Wan, Qing

doi:10.1109/led.2019.2908727

Cited by 35 publications

(26 citation statements)

References 35 publications

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“…As one kind of conventional materials applied for synaptic devices, binary oxides are extensively used in memristors. Among various kinds of binary oxides, ZnO, HfO 2 , and AlO x have been paid growing attention in photonic synaptic devices due to their peculiar performance, such as their ease of fabrication, low cost, and great possibility in achieving a simple device structure …”

Section: Emerging Materials‐based Synaptic Devicesmentioning

confidence: 99%

Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang

Dai

Zhao

et al. 2020

Advanced Intelligent Systems

152

118

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Implementing synaptic functions with electronic devices is critical to achieve neuromorphic systems on the hardware platform, as synapses play important roles in brain computing and memory. Synapses modulated by light signals, which are also referred as photonic synapses, can not only make effective use of the outstanding properties of light to provide devices with ultrahigh propagation speed, high bandwidth and low crosstalk but also provide a noncontact writing method, which can facilitate the evolution of optical wireless communication and operation. More importantly, real‐time image processing can also be performed by photonic synapses which possess temporary memory. Thus far, tremendous efforts have been taken to design and fabricate photonic synapses. Herein, a summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems. Finally, some current challenges and future study directions are discussed.

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Section: Emerging Materials‐based Synaptic Devicesmentioning

confidence: 99%

Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang

Dai

Zhao

et al. 2020

Advanced Intelligent Systems

152

118

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“…For an ideal optoelectronic neuromorphic device, e.g., optoelectronic synapse, [ 22,23 ] its weight is represented by conductance that should be all‐optically tunable. Various optoelectronic synapses, [ 24–36 ] such as memristive synapses, [ 34–36 ] have recently been developed; however, the device conductance could be reversibly tuned only through a combination of optical and electrical signals. Such an operation scheme makes optoelectronic NC far less attractive.…”

Section: Introductionmentioning

confidence: 99%

All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Yang

Wang

et al. 2020

Adv Funct Materials

175

157

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Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh‐density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all‐optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light‐induced electron trapping and detrapping. It is found that the light‐induced multiple memconductance states are nonvolatile. Furthermore, spike‐timing‐dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.

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“…InGaZnO (IGZO) has been widely used in display and flexible electronics thanks to the advantages of high mobility, uniformity, transparency, and low-temperature processing [34][35][36][37][38][39][40]. In particular, IGZO devices have been recently studied in applications such as wearable healthcare and biosensor, where energy-efficient and flexible logic and memory technologies are needed to process a huge amount of bio-electrical information [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Digital and Analog Switching Characteristics of InGaZnO Memristor Depending on Top Electrode Material for Neuromorphic System

et al. 2020

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In this study, we demonstrate both of digital and analog memory operations in InGaZnO (IGZO) memristor devices by controlling the electrode materials for neuromorphic application. The switching properties of the devices are determined by the initial energy barrier characteristics between the metal electrodes and the IGZO switching layer. Digital switching characteristics are obtained after the forming process when Schottky junction occurs at both of top and bottom electrodes. On the other hands, analog resistive switching is achieved when Schottky and Ohmic junctions exist at each side because the applied voltage modulates the Schottky barrier height through the Ohmic contact. In addition, the weightupdate properties of the devices are verified depending on identical and incremental pulse schemes. The incremental pulse trains improve the linearity and variation of weight modulation, leading to the stable learning characteristics of neuromorphic system in terms of pattern recognition with MNIST handwritten digit images.

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Dual-Functional Long-Term Plasticity Emulated in IGZO-Based Photoelectric Neuromorphic Transistors

Cited by 35 publications

References 35 publications

Recent Progress in Photonic Synapses for Neuromorphic Systems

Recent Progress in Photonic Synapses for Neuromorphic Systems

All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Digital and Analog Switching Characteristics of InGaZnO Memristor Depending on Top Electrode Material for Neuromorphic System

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