柔性神经形态电子学中基于场效应的可拉伸突触晶体管

Soft science Pub Date : 2023-01-01 DOI:10.20517/ss.2023.06
Xiumei Wang, Longqi Qi, Huihuang Yang, Yuan Rao, Huipeng Chen
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引用次数: 0

摘要

使用模拟生物神经元系统的柔性神经形态电子学是促进下一代人工智能设备实施的创新方法,包括可穿戴计算机、软机器人设备和神经假肢。基于场效应晶体管(fet)的可拉伸突触晶体管具有类似生物突触的功能和结构,具有高柔韧性、良好的生物相容性和易于加工等优点,是柔性神经形态电子学中很有前途的技术器件。然而,获得具有各种突触特性和良好拉伸稳定性的可拉伸突触场效应管是具有挑战性的。在生产可拉伸突触场效应管方面已经做出了重大努力;在材料、制造工艺和应用方面都取得了显著的进步。从这个角度出发,我们讨论了柔性神经形态电子学对神经形态器件的要求以及可拉伸突触场效应管的优势。此外,还介绍了实现可拉伸突触晶体管的典型方法,包括结构设计和内在可拉伸器件的开发。此外,还讨论了可拉伸突触晶体管在光、触觉和多感觉人工神经系统等人工感觉系统中的应用。最后,我们强调了实现和使用可拉伸突触晶体管可能面临的挑战,提出了克服这些器件当前局限性的解决方案,并提出了未来的研究方向。
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Stretchable synaptic transistors based on the field effect for flexible neuromorphic electronics
Using flexible neuromorphic electronics that emulate biological neuronal systems is an innovative approach for facilitating the implementation of next-generation artificial intelligence devices, including wearable computers, soft robotics devices, and neuroprosthetics. Stretchable synaptic transistors based on field-effect transistors (FETs), which have functions and structures resembling those of biological synapses, are promising technological devices in flexible neuromorphic electronics owing to their high flexibility, excellent biocompatibility, and easy processability. However, obtaining stretchable synaptic FETs with various synaptic characteristics and good stretching stabilities is challenging. Significant efforts to produce stretchable synaptic FETs have been undertaken; and remarkable advances in materials, fabrication processes, and applications have been achieved. From this perspective, we discuss the requirements for neuromorphic devices in flexible neuromorphic electronics and the advantages of stretchable synaptic FETs. Moreover, representative methods used to implement stretchable synaptic transistors, including the structural design and development of intrinsically stretchable devices, are introduced. Additionally, the application of stretchable synaptic transistors in artificial sensory systems such as light, tactile, and multisensory artificial nervous systems is also discussed. Finally, we highlight the possible challenges in implementing and using stretchable synaptic transistors, propose solutions to overcome the current limitations of these devices, and suggest future research directions.
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