Flexible neuromorphic transistors and their biomimetric sensing application

Jiang Zi-Han; Ke Shuo; Zhu Ying; Zhu Yi-Xin; Zhu Li; Wan Chang-Jin; Wan Qing

doi:10.7498/aps.71.20220308

Abstract

Biological perception system has the unique advantages of high parallelism, high error tolerance, self-adaptation and low power consumption. Using neuromorphic devices to emulate biological perceptual system can effectively promote the development of brain-computer interfaces, intelligent perception, biological prosthesis and so on. Compared with other neuromorphic devices, multi-terminal neuromorphic transistors can not only realize signal transmission and training learning at the same time, but also carry out nonlinear spatio-temporal integration and collaborative regulation of multi-channel signals. However, the traditional rigid neuromorphic transistor is difficult to achieve bending deformation and close fit with the human body, which limits the application range of neuromorphic devices. Therefore, the research of flexible neuromorphic transistor with good bending characteristics has become the focus of recent research. Firstly, this review introduces the research progress of many kinds of flexible neuromorphic transistors, including device structure, working principle and basic functions. In addition, the application of the flexible neuromorphic transistor in the field of bionic perception is also introduced. Finally, this review also gives a summary and simple prospect of the above research fields.

Keywords:

Authors and contacts

School of Electronic Science & Engineering, Nanjing University, Nanjing 210093, China

Corresponding author: Wan Chang-Jin, cjwan@nju.edu.cn ; Wan Qing, wanqing@nju.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62074075, 62174082).

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图 1 生物神经元(a)和生物突触(b)的结构示意图^[66]

Figure 1. Schematic diagram of biological neuron (a) and biological synapse (b)^[66].

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图 2 (a)基于多栅 IZO 神经形态晶体管的柔性 pH 传感器的示意图^[86]; (b) 神经纤维-OECT 的装置结构示意图和 OECT-神经纤维的照片, 插图: 离子在可渗透半导体中的掺杂机制示意图; (c) P3CT-神经纤维的 PSC 作为施加电压尖峰之间的时间间隔(Δt)的函数 (V_GS = –0.7 V, 100 ms); (d) P3CT-和 P3HT-神经纤维中超过 45 个周期的LTP和LTD循环测试; (e) 生物神经网络和神经纤维晶体管网络示意图(左), 10 × 10 P3CT-神经纤维阵列的照片(右)^[46]

Figure 2. (a) Schematic illustration of the flexible pH sensor based on an IZO neuromorphic transistor with multiple gate electrodes^[86]; (b) schematic of the device architecture for neurofiber-OECT and photograph of OECT-neurofiber, inset: schematic of the doping mechanism by ions in a permeable semiconductor; (c) PSC of a P3CT-neurofiber as a function of the time interval (Δt) between applied voltage spikes (V_GS = –0.7 V, 100 ms); (d) cycle test of LTP and LTD in P3CT- and P3HT-neurofibers over 45 cycles; (e) schematic of biological neural network and neurofiber transistor network (left), photograph of a 10 × 10 array of P3CT-neurofibers (right)^[46].

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图 3 (a) LiClO₄溶解在PEO中作为栅极电解质的突触晶体管的结构示意图; (b) 双脉冲易化, 插图: PPF指数被绘制为两个脉冲之间时间间隔的函数; (c)由40个突触前脉冲触发的EPSC; (d) LTP和LTD的可重复性^[105]

Figure 3. (a) Schematic of synaptic transistors with LiClO₄ dissolved in PEO as gate electrolyte; (b) paired-pulse facilitation, inset: PPF index is plotted as a function of time interval between the two pulses; (c) EPSC triggered by 40 presynaptic pluses; (d) repeatability of LTP and LTD^[105].

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图 4 (a) 自支撑光电神经形态晶体管示意图; (b) 光刺激角膜伤害感受器示意图; (c) IGZO 晶体管中光学响应的能带图; (d) PCN“受伤”前后实验测量的光电流; (e) 利用V_G = 0.1 V模拟的中枢敏化, P_T降至 4.98 nW/µm²; (f) 利用V_G = –0.1 V 模拟的镇痛作用, P_T 增大到 17.62 nW/µm^2[97]

Figure 4. (a) Schematic diagram of the freestanding photoelectric neuromorphic transistor; (b) schematic illustration of photoexcited corneal nociceptor; (c) energy-band diagrams of optical responses in IGZO-based transistor; (d) experimentally measured photocurrents of the PCN before and after “wounded”; (e) central sensitization simulated by V_G = 0.1 V with P_T reduced to 4.98 nW/µm² ; (f) analgesic effect simulated by V_G = –0.1 V with P_T increased to 17.62 nW/µm^2[97].

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图 5 (a) 全无机柔性FeFET示意图; (b) 不同脉宽的突触前脉冲电压触发的EPSC; 不同弯曲半径(c)、不同弯曲循环次数(d)、不同弯曲时间(e)下的LTP和LTD; (f)—(h)对应的MNIST数字识别准确率^[54]

Figure 5. (a) Schematic of the all-inorganic flexible FeFET; (b) EPSC triggered by presynaptic voltage pulse with different spike widths; LTP and LTD with the different bending radius, (c) different bending cycles (d), and different bending durations (e); (f)–(h) the corresponding MNIST digit recognition accuracy^[54].

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图 6 (a) 以P(VDF-TrFE)薄膜为栅介质的自支撑有机神经形态晶体管的结构示意图; (b) 贴合在大脑形状模型(上图)和弯曲半径为50 µm的FONTs(下图)照片; (c) 在6000次突触前脉冲期间, 折叠FONTs的LTP和LTD的重复转换, 上左、上右图分别代表最初和最后的10个LTP, LTD循环^[53]

Figure 6. (a) Schematic diagram of freestanding ferroelectric organic neuromorphic transistors with a P(VDF-TrFE) film as the dielectric layer; (b) photo images of the FONTs on the brain-shaped mold and folded FONTs with a bending radius of 50 µm (lower panel); (c) repetitive transition between the LTP and LTD in the folded FONTs during 6000 spikes of presynaptic pulses (±30 V for 500 ms), the left and the right in upper graph shows the LTP and LTD during the initial and final 10 cycles, respectively^[53].

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图 7 (a) 生物触觉感知系统示意图(左)和人工触觉学习铁电皮肤的器件结构示意图(右); (b)三种不同手写风格(N₁, N₂ 和 N₃)的“N”图案示意图(左)和用于识别手写图案的单层神经网络的组成部分(右)^[132]

Figure 7. (a) Schematic of the biological tactile perception system (left) and schematic device structure of the artificial tactile learning ferroelectric skin (right); (b) schematic illustrations of “N” patterns with three different handwriting styles (N₁, N₂, and N₃) (left) and constituents of a single-layer neural network used to recognize handwriting pattern (right)^[132].

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图 8 (a) 光电双调控的柔性人工异突触示意图; (b) 由两个连续光脉冲序列模拟的学习-遗忘-再学习行为; (c)光照条件下, 电脉冲产生的LTP和LTD, 并且通过单独的电脉冲获得进一步的抑制; (d) 在平坦状态和弯曲状态 (R = 10 mm) 下, PSC作为突触前脉冲数的函数^[57]; (e) C₆₀ 浮栅突触晶体管的示意图(左)和横截面 SEM 形貌图像(右)^[55]

Figure 8. (a) Schematic diagram of flexible artificial heterosynapse with photoelectric dual modulation; (b) learning, forgetting and relearning behaviors emulated by two sequences of consecutive light pulses; (c ) electrical pulses induced the LTP and LTD under illumination of light, further depression was obtained by electrical pulse independently; (d) PSC as a function of pre-synaptic pulse number in a flat states and curved state (R = 10 mm)^[57]; (e) schematic representation (left) and cross-sectional SEM topography image (right) of a C₆₀ floating gate synaptic transistor^[55].

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图 9 (a) 以CNTs/CsPbBr3-QDs为沟道的光电晶体管示意图; (b) 当观察到陌生和熟悉的面孔时, 人类视觉系统印象的示意图; (c) 不同训练脉冲数的训练权重结果; (d) 模拟人脸的学习过程^[153]

Figure 9. (a) Schematic diagram of the phototransistor with a CNTs/CsPbBr3-QDs channel; (b) schematics illustration of the impression of human visual systems when unfamiliar and familiar faces are observed; (c) training weight results with different number of training pulses; (d) simulation of the learning process of a human face^[153].

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图 10 (a) 视觉-触觉融合的双模人工感觉神经元示意图; (b) 用于肌肉和机械手驱动的视觉-触觉融合示意图; (c)视觉(顶部, 粉红色)和触觉(底部, 蓝色)反馈分别用于在z轴和y轴上推断“是”或“否”; (d) 单感觉模式和双感觉模式各自的识别率^[158]

Figure 10. (a) Schematic illustration of bimodal artificial sensory neuron with visual-haptic fusion; (b) schematic diagram of visual-haptic fusion for muscle and robotic hand actuation; (c) visual (top, pink) and haptic (bottom, blue) feedback used to infer “YES” or “NO” in z-axis and y-axis respectively; (d) the recognition rates of unimodal and bimodal modes, respectively^[158].

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表 1 不同类型柔性突触晶体管比较

Table 1. Comparison of different types of flexible synaptic transistors.

器件类型	衬底/栅介质/沟道	弯曲半径/mm	弯曲次数	生物相容性	神经功能	文献
电解质栅晶体管	PET/Al₂O₃/IWO	20	—	—	EPSC、PPF、高通滤波	[44]
	PET/LiClO₄/MoSSe	3	1000	—	图像识别、存储和处理	[45]
	—/离子凝胶/P3 CT	—	—	—	LIF、语音识别	[46]
	PEN/LiClO₄+PEO/SnO₂	8	1000	—	学习规则、痛觉感知	[47]
	PEN/SiO₂+PVA/CNTs	3	1000	—	STP、LTP、LTD	[48]
	PI/壳聚糖/ITO	20	—	是	STDP、学习规则	[49]
	PET/蛋白质/ITO	10	3500	是	神经递质释放动力学	[50]
	PDMS/离子凝胶/P3 HT	—	—	—	学习规则	[51]
铁电晶体管	—/P(VDF-TrFE)/并五苯	2.5	100	—	SRDP、STDP、图像识别	[52]
	—/P(VDF-TrFE)/并五苯	5×10^–5	—	—	STDP	[53]
	云母/PZT/IGZO	4	400	—	数字识别	[54]
浮栅晶体管	PET/Al₂O₃+PMMA:C₆₀/并五苯	10	500	—	STP、LTP	[55]
	纸/Au+SiO_x/CNTs	—	—	—	STDP、图像识别	[56]
	PET/ZrO₂+Al₂O₃/MoS₂	10	—	—	学习规则	[57]
	PET/黑磷-QD+Al₂O₃/MoSSe	5	1000	—	学习规则	[58]

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