Realize ultralow-energy-consumption photo-synaptic device based on a single (Al,Ga)N nanowire for neuromorphic computing

Gu, Xiushuo; Zhou, Min; Zhao, Yukun; Zhang, Qianyi; Zhang, Jianya; Huang, Yonglin; Lu, Shulong

doi:10.1007/s12274-023-6069-0

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…Compared to other materials (e.g. metal oxide [47][48][49], organic material [50][51][52], graphene [53] and gallium arsenide [54][55][56]), perovskites and TMDs exhibit adjustable band gap and high quantum efficiency [57][58][59][60][61][62]. In addition, by changing the halogen species in perovskites and constructing the TMD based heterostructures, the photoelectric properties of materials and devices can be modulated and optimized [63].…”

Section: Optoelectronic Materialsmentioning

confidence: 99%

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

Li,

Zhou

et al. 2024

J. Phys. Mater.

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With the advancements in Web of Things, Artificial Intelligence, and other emerging technologies, there is an increasing demand for artificial visual systems to perceive and learn about external environments. However, traditional sensing and computing systems are limited by the physical separation of sense, processing, and memory units that results in the challenges such as high energy consumption, large additional hardware costs, and long latency time. Integrating neuromorphic computing functions into the sensing unit is an effective way to overcome these challenges. Therefore, it is extremely important to design neuromorphic devices with sensing ability and the properties of low power consumption and high switching speed for exploring in-sensor computing devices and systems. In this review, we provide an elementary introduction to the structures and properties of two common optoelectronic materials, perovskites and transition metal dichalcogenides (TMDs). Subsequently, we discuss the fundamental concepts of neuromorphic devices, including device structures and working mechanisms. Furthermore, we summarize and extensively discuss the applications of perovskites and TMDs in in-sensor computing. Finally, we propose potential strategies to address challenges and offer a brief outlook on the application of optoelectronic materials in term of in-sensor computing.

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Section: Optoelectronic Materialsmentioning

confidence: 99%

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

Li,

Zhou

et al. 2024

J. Phys. Mater.

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“…On the other hand, stable persistent photoconductivity (PPC) effects can be observed in GaN-based materials, which have the advantages of low-energy consumption, long lifetime and small volume, etc [11,12]. In the previous works, photo-stimulated synaptic devices are fabricated based on GaN-based materials successfully [13,14]. However, GaNbased materials are normally grown on the rigid substrates, such as silicon (Si) and sapphire, which cannot be used for flexible devices [6].…”

Section: Introductionmentioning

confidence: 99%

Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing

Zhang,

Hou,

Zhang

et al. 2024

Nanotechnology

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Because of wide range of applications, the flexible artificial synapse is an indispensable part for next-generation neural morphology computing. In this work, we demonstrate a flexible synaptic device based on a lift-off (In,Ga)N thin film successfully. The synaptic device can mimic the learning, forgetting, and relearning functions of biological synapses at both flat and bent states. Furthermore, the synaptic device can simulate the transition from short-term memory to long-term memory successfully under different bending conditions. With the high flexibility, the excitatory post-synaptic current of the bent device only shows a slight decrease, leading to the high stability. Based on the experimental conductance for long-term potentiation and depression, the simulated three-layer neural network can achieve a high recognition rate up to 90.2%, indicating that the system comprising of flexible synaptic devices could have a strong learning-memory capability. Therefore, this work has a great potential for the development of wearable intelligence devices and flexible neuromorphic systems.

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“…Flexible electronic devices with low power consumption and high performance are crucial for realizing edge devices, and flexible synaptic electronics are suitable since they exhibit these characteristics. 22,23 Various materials such as metal oxides, 24,25 quantum dots, 9,26 organic materials, 27,28 perovskites, 29,30 nanowires, 31,32 and twodimensional (2D) nanomaterials [33][34][35] have been used to satisfy these requirements. In particular, 2D nanomaterials are highly promising owing to their unique characteristics, such as excellent flexibility, thermal and mechanical stability, strong electrostatic tunability, low power consumption, monolithic integration, and high scalability.…”

Section: Introductionmentioning

confidence: 99%

An extremely low-power-consumption reconfigurable two-dimensional tellurene artificial synapse for bio-inspired wearable edge computing

You,

Yoon,

Kim

et al. 2024

J. Mater. Chem. C

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Realize ultralow-energy-consumption photo-synaptic device based on a single (Al,Ga)N nanowire for neuromorphic computing

Cited by 9 publications

References 52 publications

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing

An extremely low-power-consumption reconfigurable two-dimensional tellurene artificial synapse for bio-inspired wearable edge computing

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