Metal oxide semiconductor nanowires enabled air-stable ultraviolet-driven synaptic transistors for artificial vision

Zhou, Ruifu; Zhang, Wenxin; Cong, Haofei; Chang, Yu; Wang, Fengyun; Liu, Xuhai

doi:10.1016/j.mssp.2023.107344

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…The photogenerated electrons move toward c-In 2 O 3 and the photogenerated holes move toward rh-In 2 O 3 . 27 When UV light is removed, prolonged LTP is achieved due to the presence of a potential barrier between c-In 2 O 3 and rh-In 2 O 3 , which prevents electron–hole recombination with subsequent slow conductance decay. Table 1 compares the LTPs of different synaptic transistors, which can clearly illustrate the memory capability of IZTO-6 nanowire synaptic transistors.…”

Section: Resultsmentioning

confidence: 99%

“…Secondly, we have applied various multi-cation MOSs in UV-driven synapses, such as InZnO and zinc tin oxide (ZnSnO) nanowires, to obtain different positive characteristics of artificial synaptic transistors, such as long-memory retention, high environmental operating stability as well as the capability for versatile applications. 26,27 However, we have not implemented the asymmetric device configuration to explore the self-powering characteristics of optically driven synapses based on MOS nanowires, in an attempt to further simplify the design of artificial synapse based circuits.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the fact that the homojunction formed by the c-In 2 O 3 and rh-In 2 O 3 grains at the interface can generate a built-in electric field,36 which can effectively promote the separation and transfer of photogenerated charge carriers, as illustrated in Fig.4b. The photogenerated electrons move toward c-In 2 O 3 and the photogenerated holes move toward rh-In 2 O 3 27. When UV light is removed, prolonged LTP is achieved due to the presence of a potential barrier between c-In 2 O 3 and rh-In 2 O 3 , which prevents electron-hole recombination with subsequent slow conductance decay.…”

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confidence: 99%

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Long-memory retention and self-powered ultraviolet artificial synapses realized by multi-cation metal oxide semiconductors

et al. 2023

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-memory retention and self-powered ultraviolet artificial synapses realized by multi-cation metal oxide semiconductors

et al. 2023

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“…Various synaptic functions, such as PPF and LTP, could be mimicked in this structure. Many synaptic devices have been investigated substantially and shown to mimic synaptic functions based on similar mechanisms [65][66][67][68]. It should be mentioned that charge-trap-flash-based synaptic transistors were successfully fabricated based on a silicon nitride film as the charge-trapping layer and a silicon film as the channel layer in transistor devices [69,70].…”

Section: Capture and Release Of Carriersmentioning

confidence: 99%

Transistor-Based Synaptic Devices for Neuromorphic Computing

Huang,

Zhang,

Lin

et al. 2024

Crystals

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Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic control capabilities on synaptic weight changes. Various low-dimensional inorganic materials such as silicon nanomembranes, carbon nanotubes, nanoscale metal oxides, and two-dimensional materials are employed to fabricate transistor-based synaptic devices. Although these transistor-based synaptic devices have progressed in terms of mimicking synaptic functions, their application in neuromorphic computing is still in its early stage. In this review, transistor-based synaptic devices are analyzed by categorizing them into different working mechanisms, and the device fabrication processes and synaptic properties are discussed. Future efforts that could be beneficial to the development of transistor-based synaptic devices in neuromorphic computing are proposed.

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An Artificial Neuromuscular System for Bimodal Human–Machine Interaction

Geng

Fan

et al. 2023

Adv Funct Materials

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Neuromuscular system enabled muscle functions are critical for body movements, such as rhythmic motions and other complexed movements. Imparting artificial neuromuscular system to advanced robots and interactive systems can potentially improve their sensorimotor coordination and interactivity.Here, an artificial neuromuscular system is reported to mimic the sensing, processing, and manipulation of neuromuscular information, which consists of a triboelectric nano-generator (TENG), SnErO x neuromorphic transistors (SENTs), and the signal-converting system. The synaptic performance of the SENT is optimized to implement multiple operation modes of muscle upon receiving signals from TENG, including muscle contraction, fast/slow muscle fiber shift, conscious/unconscious muscle movements, and transformation. As a proof-of-concept demonstration, the artificial neuromuscular system is used to develop contact human-machine interaction (HMI) by decoding the surface electromyogram (sEMG) and non-contact HMI based on supercapacitive iontronic effect. Importantly, both HMI demonstrate real-time gesture recognition and robotic manipulation, indicating the potential of developing next-generation smart electronics that desire multiple interaction patterns.

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Metal oxide semiconductor nanowires enabled air-stable ultraviolet-driven synaptic transistors for artificial vision

Cited by 7 publications

References 43 publications

Long-memory retention and self-powered ultraviolet artificial synapses realized by multi-cation metal oxide semiconductors

Long-memory retention and self-powered ultraviolet artificial synapses realized by multi-cation metal oxide semiconductors

Transistor-Based Synaptic Devices for Neuromorphic Computing

An Artificial Neuromuscular System for Bimodal Human–Machine Interaction

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