与传统用于构建人工神经元的刚性硅基电路相比, 具有阈值电阻转变特性的忆阻器因结构简单、材料选择广泛和动力学特性丰富等优点, 已成为构建柔性人工神经元的理想选择[8⇓⇓-11]。例如, Xu等[12]在柔性聚对苯二甲酸乙二醇酯(PET)衬底上制备了最大弯曲半径为2.14 cm的Ag/Nafion/Au阈值型忆阻器, 并基于此器件模仿了神经元的泄露-整合-发放(Leaky integrate-and-fire, LIF)功能; 朱佳雪等[13]在柔性聚酰亚胺(PI)衬底上制备了最大弯折半径为2.5 mm的NbOx基阈值型忆阻器, 并结合电路设计模拟了LIF神经元。尽管在柔性衬底上沉积刚性功能材料可使阈值型忆阻器具有一定弯折能力, 但因刚性材料缺少本征可拉伸性以及与柔性衬底之间弹性模量不匹配, 会导致器件在拉伸等大形变过程中出现开裂失效问题, 难以满足穿戴场景的要求[14⇓-16]。因此, 如何研制可在拉伸应变下保持结构和电学性能稳定的阈值型忆阻人工神经元器件成为重要挑战。
近期研究表明, 使用本征柔性的电极和介质层制备可拉伸忆阻器为解决上述问题提供了重要途径[14,16]。液态金属是一种本征弹性导电材料, 由镓(Ga)、铟(In)等低熔点金属构成。它在室温下具有良好的导电性和延展性, 是制备柔性电极的理想选择[17]。例如, 由掺杂Cu微粒的GaIn合金(Cu@GaIn)制成的液态金属电极, 在30%的拉伸应变下仍可保持出色的导电能力[18]。另一方面, 银纳米线-弹性聚合物复合材料是以弹性聚合物基体为连续相, 银纳米线为分散相的一种复合材料。它不仅具有弹性聚合物的柔韧性, 而且在电场作用下还具有阈值转变特性[19⇓⇓-22]。其中, 以聚氨酯(Polyurethane, PU)为代表的弹性聚合物拉伸性能优异, 是理想的弹性聚合物基体材料[23-24]。因此将液态金属作为电极, 银纳米线-聚氨酯复合薄膜作为介质层, 有望获得具有本征可拉伸能力的新型阈值型忆阻器及人工神经元。
本工作以液态金属(Cu@GaIn)作为电极和银纳米线-聚氨酯复合薄膜(AgNWs-PU)作为介质层, 采用水溶性牺牲层法制备了可拉伸阈值型忆阻器。研究了该器件的阈值电阻转变特性, 分析了其工作机制, 并模拟了神经元的脉冲整合发放等信息功能。进一步评估该器件在拉伸应变下的工作稳定性, 并展示其作为可拉伸人工神经元在智能可穿戴系统领域的应用前景。
1 实验方法
1.1 材料准备
NaCl基片(10 mm×10 mm×2 mm)由自贡三川晶片有限公司提供; 银纳米线(AgNWs, 长度10 μm, 直径30 nm)由中诺新材科技有限公司提供; 882型水性聚氨酯由广东三七化工有限公司提供; 聚二甲基硅氧烷(Polydimethylsiloxane, PDMS)由美国道康宁公司提供, 将基本组分与固化剂按10 : 1混合并在常温下搅拌5 min备用。
液态金属(Cu@GaIn)制备: 首先将金属Ga(99.99%)和In(99.995%)按质量比3 : 1混合, 在80 ℃下搅拌45 min制得GaIn合金。为了降低GaIn的表面能, 提高与聚合物介质层薄膜的浸润性[25], 在GaIn合金中添加质量分数为3.5%的Cu微粒(平均粒径5 μm)后，研磨40 min得到样品。
银纳米线-聚氨酯复合溶液制备: 取适量水性聚氨酯原液, 使用去离子水稀释10倍, 向其中添加水性聚氨酯质量分数1.4%的银纳米线并混合均匀制得银纳米线-聚氨酯复合溶液。
1.2 制备方法
可拉伸阈值型忆阻器的制备流程如图1所示。实验使用NaCl基片作为旋涂银纳米线-聚氨酯复合溶液的衬底。旋涂前使用氧等离子体(150 W, 5 min)对NaCl基片表面进行亲水性处理。匀胶过程第一步使用900 r/min的转速旋涂8 s, 第二步使用2200 r/min的转速旋涂45 s, 结束后在65 ℃加热台上烘干2.5 h, 制备出厚度约为1 μm的银纳米线-聚氨酯复合薄膜。再次, 使用金属掩模板在介质层上溅射一层厚度为16 nm的Au膜, 随后在Au膜上刷涂400 μm宽的液态金属底电极, 并使用PDMS对器件进行封装。最后, 使用去离子水溶解NaCl衬底并翻转器件, 刷涂垂直于底电极的400 μm宽液态金属顶电极, 并用PDMS对器件整体封装, 得到图1插图中单元结构为PDMS/Cu@GaIn/AgNWs-PU/Cu@GaIn/PDMS的器件。
图1
图1
液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的制备流程图
Fig. 1
Flow chart of Cu@GaIn/AgNWs-PU/Cu@GaIn device fabrication
1.3 样品表征
使用日立SU8200冷场发射扫描电子显微镜观察样品的介质层; 使用Agilent B1500A型半导体参数分析仪对器件的I-V性能进行测试; 使用Keithley 4225脉冲测试单元进行神经元特性测试; 如图1所示, 测试过程中在器件顶电极施加电压并将底电极接地。
2 结果与讨论
2.1 器件I-V特性研究
图2(a)显示了基于银纳米线-聚氨酯复合薄膜的阈值器件典型I-V曲线。向顶电极施加扫描电压, 方向为0 V→5V→0 V→-5 V→0 V, 研究发现器件具有典型的双向阈值转变特性。以正向为例, 当电压从0 V逐渐增加到阈值电压~2 V (Vth)时, 器件电流从~10-9 A跃迁到限流(10-5 A), 此时器件从高阻态(High resistance state, HRS)切换到低阻态(Low resistance state, LRS)。当电压降低至保持电压0.45 V (Vhold)后, 电流突然减小, 此时器件从LRS返回到HRS。图2(b)显示了20个I-V循环中的工作电压分布情况。如图2(c)所示, 器件可在10 nA~10 μA的限流下稳定工作, 开关比随限流增加而逐渐增加。
图2
图2
液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的I-V特性
Fig. 2
I-V characteristics of the Cu@GaIn/AgNWs-PU/Cu@GaIn device
(a) I-V curve of the Cu@GaIn/AgNWs-PU/Cu@GaIn device; (b) Cumulative distribution function of the operation voltages; (c) I-V curves of the device under different compliance currents; (d) Dependence of the operation voltage on the thickness of the AgNWs-PU film
2.2 器件工作机理分析
实验研究了该器件产生阈值电阻转变特性的物理机制。根据Wang等[22]的工作, 在强电场作用下, 分散在聚合物基体中的银纳米线可通过氧化、迁移和还原形成银纳米导电细丝, 连通银纳米线和器件电极, 进而引起HRS到LRS的转变。而在弱电场下, 银导电细丝在表面张力和原子自发驰豫驱动下自发断裂, 是器件从LRS转变为HRS的主要原因。鉴于介质材料成分的相似性, 可以推断银纳米线间形成银导电细丝的通断是诱导器件电阻转变的主要原因。
图3
图3
液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的工作机制
Fig. 3
Working mechanism of the Cu@GaIn/AgNWs-PU/Cu@GaIn device
(a) Dependence of the device resistance at the LRS on the compliance currents; (b) Illustration of the dynamic Ag filament formation/rupture between AgNWs during threshold switching process
基于以上分析, 本课题组提出了器件的工作机理模型。如图3(b-左)所示的纳米接触结构(银纳米线/聚氨酯/银纳米线), 当施加正向电压时, 顶部银纳米线中部分Ag原子会被氧化为Ag+(Ag-e-→Ag+)。随后Ag+在电场作用下通过PU迁移到底部银纳米线, 并被还原为Ag原子(Ag++e-→Ag)。随着Ag原子在底部堆积, 银导电细丝在银纳米线之间逐渐生长[28]。当V > Vth时, 银导电细丝会连通银纳米线, 此时隧穿电流急剧增大，达到限流时, 导电细丝停止生长, 形成具有间隙的银导电细丝, 如图3(b-中)所示。当V < Vhold时, 该导电细丝在表面张力作用下自发断裂, 使器件从LRS自发过渡到HRS, 如图3(b-右)所示。这是由于银导电细丝的表面能较高, 在没有足够电场维持的情况下, 其会自发断裂以减小表面能[29]。
2.3 器件神经元特性研究
在生物神经元中, 信息整合功能是通过膜电位的变化来实现[30]。神经元膜类似于电容器, 整合来自其它神经元的脉冲信号后, 膜电位得到提升。当膜电位达到阈值时, 神经元发放动作电位, 并将膜电位重置[31]。同时, 神经元具有泄露特性[32], 当膜电位未达阈值停止输入电脉冲, 膜电位会自发衰减, 如图4(a)所示。为了研究阈值型忆阻器在模仿神经元功能方面的潜力, 对器件施加电脉冲, 研究其电流响应特性, 如图4(b)所示。图4(c)展示了向器件施加幅值为6 V, 宽度为10 ms, 间隔为30 ms的电脉冲序列时的测试结果, 可以发现, 器件对前五个脉冲进行整合, 在第六个脉冲时到达阈值并发放了电流尖峰, 模仿了神经元中的脉冲整合-发放功能。
图4
图4
液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件模拟生物神经元的整合发放功能
Fig. 4
Emulation of the integrate-and-fire behaviors of biological neurons with the Cu@GaIn/AgNWs-PU/Cu@GaIn device
(a, b) Schematic diagram for (a) biological neuron and (b) artificial neuron; (c) Typical integrate-and-fire behavior of the memristor based artificial neuron; Colorful figures are available on website
图5
图5
输入脉冲幅值、间隔对阈值型忆阻人工神经元整合发放功能的影响
Fig. 5
Influences of the voltage pulse amplitude and interval on the integrate-and-fire behaviors of the memristor based artificial neuron
(a) Integrate-and-fire behaviors of the device as a function of the voltage pulse amplitude with pulse interval and width at 30 ms and 10 ms, respectively; (b) Relationship between the required pulse number for device firing (NFire) and the pulse amplitude; (c) Integrate-and-fire behaviors of the device as a function of the voltage pulse interval with pulse amplitude and width at 6 V and 10 ms, respectively; (d) Relationship between the required pulse number for device firing (NFire) and the pulse interval; Colorful figures are available on website
2.4 器件拉伸能力测试
为了评估阈值器件在可穿戴系统中应用的可行性, 本工作对不同程度拉伸应变下的器件进行了测试。如图6(a)所示, 定义平行于底电极和顶电极的方向分别为拉伸应变的x和y方向。将器件粘贴在丝杆滑台上, 沿x或y轴方向依次拉伸至20%, 并做I-V测试。图6(b)为器件沿x方向拉伸20%与未拉伸时的对比实拍图。对测试所得工作电压进行统计分析, 结果如图6(c)所示。可以看出, 器件沿x方向的拉伸应变在20%以内时, 工作电压幅值对拉伸不敏感。以V+th为例, 拉伸为(2.5±0.6) V, 拉伸20%时为(2.7±0.6) V, 平均电压值波动<7.09%。对器件沿y方向进行拉伸20%时可得到类似结果, 器件的平均V+th相比拉伸前波动约5%。结果表明, 该器件在20%的拉伸应变内具有稳定的结构和阈值转变特性。分析认为, 优异的拉伸稳定性源于该器件采用高延展性液态金属和银纳米线-聚氨酯复合薄膜作为电极和介质层, 有效避免了传统忆阻器在大应变条件下产生裂纹和界面脱落导致器件失效的问题[34]。在拉伸过程中, 聚氨酯中的银纳米线产生水平方向上的相对滑动, 其之间的绝对距离并未发生显著改变, 从而确保了器件的稳定工作。
图6
图6
拉伸应变条件下液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的阈值转变电压研究
Fig. 6
Threshold switching voltages of the Cu@GaIn/AgNWs-PU/Cu@GaIn device under different tensile strain conditions
(a) Schematics of the device stretching under tensile strain; (b) Optical images of the device before and after being stretched by 20%; (c, d) Evolution of the operation voltage for the device with tensile strain in (c) x and (d) y directions
图7
图7
拉伸应变条件下人工神经元的整合发放功能测试
Fig. 7
Integrate-and-fire function test of artificial neuron under tensile strain conditions
(a) Control pulse interval (30 ms), width (10 ms) and amplitude (6 V) being unchanged, and the NFire change of the device by changing tensile strain ratio of the device in the x direction; (b) Evolution of NFire of the device with tensile strain ratio
3 结论
研究采用液态金属(Cu@GaIn)作为电极, 银纳米线-聚氨酯的复合薄膜(AgNWs-PU)作为介质层, 结合水溶性牺牲层法制备了可拉伸阈值型忆阻器。该器件呈现稳定的阈值电阻转变特性, 归因于电场下银纳米线间非连续银纳米导电细丝的可逆通断。在外加电压脉冲激励下, 器件可模拟神经元的整合-发放特性, 并实现脉冲强度、间隔调制的尖峰放电功能。该器件具有优良的拉伸稳定性, 在20%拉伸应变下, 器件平均驱动电压变化小于8%。本工作为构建下一代柔性智能仿生电子芯片及可穿戴电子设备提供了重要思路。
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