无机材料学报 ›› 2023, Vol. 38 ›› Issue (4): 367-377.DOI: 10.15541/jim20220700
• 专栏:神经形态材料与器件(特邀编辑:万青) • 上一篇 下一篇
收稿日期:
2022-11-24
修回日期:
2022-12-13
出版日期:
2023-04-20
网络出版日期:
2022-12-28
通讯作者:
万昌锦, 副教授. E-mail: cjwan@nju.edu.cn;作者简介:
杨洋(1994-), 女, 博士研究生. E-mail: 1294596086@qq.com
基金资助:
YANG Yang(), CUI Hangyuan, ZHU Ying, WAN Changjin(), WAN Qing()
Received:
2022-11-24
Revised:
2022-12-13
Published:
2023-04-20
Online:
2022-12-28
Contact:
WAN Changjin, associate professor. E-mail: cjwan@nju.edu.cn;About author:
YANG Yang (1994-), female, PhD candidate. E-mail: 1294596086@qq.com
Supported by:
摘要:
近年来, 受人脑独特工作模式的启发, 利用人工神经形态器件模拟突触和神经元的感知与计算功能吸引了广泛关注。到目前为止, 已经有很多关于神经形态晶体管的报道, 但绝大多数器件是在刚性衬底上加工的。柔性神经形态晶体管不仅可以同时实现信号传输和训练学习, 对多路信号进行非线性的时空整合与协同调控, 而且能密切贴合柔软的人体皮肤, 承受器官和组织的高生理应变。更重要的是, 柔性神经形态晶体管具有可设计的灵活性和优异的生物兼容性, 在检测生物环境中生理相关时间尺度的低幅信号方面具备独特的优势和应用潜力。柔性神经形态晶体管已经广泛应用于电子皮肤、人工视觉系统、智能可穿戴系统等领域。目前, 研制低功耗、高密度集成的柔性神经形态晶体管是研究的首要任务之一。本文综述了基于不同柔性衬底的神经形态晶体管的研究进展, 并展望了柔性神经形态晶体管的未来应用前景,这将为未来柔性神经晶体管的研制以及智能计算和感知应用提供比较详实的参考。
中图分类号:
杨洋, 崔航源, 祝影, 万昌锦, 万青. 柔性神经形态晶体管研究进展[J]. 无机材料学报, 2023, 38(4): 367-377.
YANG Yang, CUI Hangyuan, ZHU Ying, WAN Changjin, WAN Qing. Research Progress of Flexible Neuromorphic Transistors[J]. Journal of Inorganic Materials, 2023, 38(4): 367-377.
图2 基于PI衬底的突触晶体管的研究工作
Fig. 2 Research on synaptic transistor on PI substrate (a) Image of In2O3 nanofiber synaptic transistor on PI substrate[29]; (b) Optical response of EPSC triggered by different light intensities[30]; (c) Relationship between amplitude of EPSC response to presynaptic peak (2.0 V, 25 ms) and gate pressure[31]; (d) Accuracy of simulated neural network for pattern recognition of MNIST small digit (8×8 pixels) database[29]; Colorful figures are available on website
图3 基于PEN衬底的突触晶体管的研究工作[36]
Fig. 3 Research on synaptic transistor on PEN substrate[36] (a) Schematic diagram of a synaptic transistor constructed on a PEN substrate; (b) Variation trend of the postsynaptic current with the number of bending, and curvature radius at 0.8 cm during the test; (c) Schematic diagram of a pain perception model caused by UV stimulation; (d) Pain facilitation index increased with the increase of UV intensity; (e) Change of pain de-facilitation index with ΔT
图4 基于PET衬底的突触晶体管的研究工作
Fig. 4 Research on synaptic transistor on PET substrate (a) Physical photograph of the construction of synaptic transistors on a PET substrate; (b) Change of current with time after 20 (4.0 V, 20 ms) pulses being applied to the gate of the synaptic transistor when VDS at 0.5 V [39]; (c) Schematic diagram of spatio-temporal signal integration and memory performed by heterogeneous synapses (with the introduction of noise signals during the transmission of the original signal); (d) Comparison of classification accuracy of two-gate heterogeneous synaptic transistor and single-gate homogeneous synaptic transistor networks[41]
图5 基于云母衬底的突触晶体管的研究工作
Fig. 5 Research on synaptic transistor on mica substrate (a) Synaptic plasticity of synaptic transistors constructed on mica substrate[45]; (b) Optical photographs of flexible VO2 transistors constructed on mica substrate; (c) EPSC count chart to assess pain intensity; (d) Simulation identification accuracy of ideal devices and VO2 transistor devices[49]; EPSC: Excitatory post-synaptic current
图6 基于纸衬底的突触晶体管的研究工作[52]
Fig. 6 Research on synaptic transistor on paper substrate[52] (a) Schematic diagram of the chitosan /IZO synaptic transistor constructed on paper substrate; (b) Optical photographs of bent synaptic transistors on a paper substrate; (c, d) Simulated biological synaptic function on paper synaptic transistors
图7 基于其他自支撑柔性衬底的突触晶体管的研究工作
Fig. 7 Research on synaptic transistor on other self-support substrate (a) Organic synaptic transistor based on PDMS substrate and its excellent mechanical stability[54]; (b) Pain thresholds of independent artificial nociceptors at different modulation voltages; (c) Pain response currents of independent artificial nociceptors at different modulation voltages[56]
图8 柔性神经元晶体管相关工作
Fig. 8 Research on flexible neuron transistor (a) Schematic diagram of IZO-based neuron transistor measurement; (b) pH sensitivity of the device with different VG1[61]; (c) Change of the estimated recognition rate of pattern recognition with the number of training under three conditions according to different exposure time; (d) Classification test confusion matrix under three different conditions ("Good", "Yeah" and "OK") after different exposure time and number of iterations[62]
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