Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang, Junyao; Dai, Shilei; Zhao, Yiwei; Zhang, Jianhua; Huang, Jia

doi:10.1002/aisy.201900136

Cited by 165 publications

(125 citation statements)

References 135 publications

(183 reference statements)

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“…[25] In spite of such good performance, the filamentary RS devices suffer severely from large cycle-tocycle and cell-to-cell variability due to its defect-based mechanism, [26] and thus the mass industrial production is greatly hindered. Besides memristive oxides, other materials and devices such as Li-ion battery electrolytes, [27] 2D materials, [28][29][30][31] perovskites, [32] ionic liquid, [33] ferroelectric materials, [34,35] magnetic devices, [36,37] and even organic polymers [38,39] were reported to have the ability to carry out the neuromorphic computing tasks.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Mai

Lin

et al. 2020

Adv Funct Materials

182

120

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Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance and energy efficiency, calling for emerging neuromorphic electronic and optical devices and systems which can mimic the human brain to shift this paradigm. Material-level innovation has become the key component to this revolution of information technology. Chalcogenide phase-change material (PCM) as a well-acknowledged data-storage medium is a promising candidate to tackle this challenge. In this review, the use of PCMs to implement artificial neurons and synapses from both the electronic and optical respects is discussed, and in particular, the structure-property physics and transition dynamics that enable such brain-inspired and in-memory computing applications are emphasized. Recent advances on the atomic-level amorphous and crystalline structures, transition mechanisms, materials optimization and design, neural and synaptic devices, brain-inspired chips, and computing systems, as well as the future opportunities of PCMs, are summarized and discussed.

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Section: Introductionmentioning

confidence: 99%

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Mai

Lin

et al. 2020

Adv Funct Materials

182

120

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“…In this section, we mainly discuss the principles of related devices based on optical memristors for synapse simulation. These principles mainly include the ionization and dissociation of oxygen vacancies [92], capture and release of carriers by traps [94,108,109], and light-induced phase transition [17,110].…”

Section: Optically Stimulated Synaptic Devicesmentioning

confidence: 99%

Memristive Artificial Synapses for Neuromorphic Computing

et al. 2021

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Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

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“…In the past years, many circuits based on CMOS technology have been used to mimic synapses. [64][65][66][67][68][69][70] However, because of the finite similarity to the biological synapses, the three terminal devices need complex circuit design to imitate the synaptic behavior. As a better choice, two terminal devices (such as memristors), with less footprint and lower power consumption, are widely utilized to mimic synapses.…”

Section: Volatile Memristor As Artificial Synapsementioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

153

113

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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Recent Progress in Photonic Synapses for Neuromorphic Systems

Cited by 165 publications

References 135 publications

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Memristive Artificial Synapses for Neuromorphic Computing

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

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