CN114527173A - 一种阵列式气体传感器及智能气体检测方法 - Google Patents
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Abstract
本发明的目的是提供一种阵列式气体传感器及智能气体检测方法。其制备方法是在传感器衬底上制备出n列×m行，共nm个磁控溅射孔位和电极；采用掩模版B1～掩模版Bn依次覆盖于处理过的传感器衬底上，并依次采用n种溅射材料，通过磁控溅射将n种不同溅射材料分别生长到n列磁控溅射孔位上；再采用掩模版C1～掩模版Cm‑1依次覆盖于处理过的传感器衬底上，并依次采用m‑1种溅射材料，通过磁控溅射分别生长到磁控溅射孔位上，值得具有nm个阵列单元的阵列式气体传感器。
Description
技术领域
本发明涉及气体传感器。
背景技术
构建基于新原理和新材料的智能感知系统是传感领域的研发趋势。传统的基于气敏材料的传感器具有室温恢复能力和选择 性等还达不到应用要求的问题。而在阵列传感器中，普遍采用温度调制技术或敏感元负载调控的方法，结合算法的概率统计模型来达到信息多样化的要求。但是温度组件和大量负载的引入不可避免地带来体积大、兼容性差、功耗高等问题。
发明内容
本发明的目的是提供一种阵列式气体传感器，其特征在于，其制备方法包括以下步骤:
1〕在传感器衬底上制备出n列×m行，共nm个磁控溅射孔位和电极；制备时，需要采用掩模版A覆盖于衬底上，进行磁控溅射孔位的制备；其中掩模版A具有n列×m行，共nm个矩形阵列状排列的通孔；每个磁控溅射孔位均连接两个电极； n和m为大于2的自然数；
2〕采用掩模版B1～掩模版Bn依次覆盖于步骤1〕处理过的传感器衬底上，并依次采用n种溅射材料，通过磁控溅射将n种不同溅射材料分别生长到n列磁控溅射孔位上；其中，掩模版B1具有与第1列磁控溅射孔位对应的通孔、掩模版B2具有与第2列磁控溅射孔位对应的通孔、……掩模版Bn具有与第n列磁控溅射孔位对应的通孔；
3〕采用掩模版C1～掩模版Cm-1依次覆盖于步骤2〕处理过的传感器衬底上，并依次采用m-1种溅射材料，通过磁控溅射分别生长到磁控溅射孔位上； m-1种溅射材料，依次被编号为i，i＝1、2……m-1；这m-1种溅射材料与步骤2〕中所述n种溅射材料均不同；其中，第i种溅射材料生长到第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位上，n为奇数时，n/2舍位取整；其中，掩模版C1具有与第1行和第2行磁控溅射孔位对应的n/2个通孔、掩模版C2具有与第2行和第3行磁控溅射孔位对应的n/2个通孔……掩模版C1具有与第1行磁控溅射孔位对应的n/2个通孔。
进一步，磁控溅射材料选自MoS2、ZnO、WO3、TiO2、SnO2、C、HfO2、Ta2O5、Ga2O3。
进一步，步骤2〕和/或步骤3〕中，在完成材料溅射后，对溅射材料进行参杂；参杂选用的金属为Cu、Al、W、Pt、Cr、Mn、Ag、Ce、Sb。
进一步，步骤3〕中，第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位错位分布。
本发明还公开一种基于上述传感器的智能气体检测方法，其特征在于：将所述传感器的电极接入测量电路，采集传感器阵列中，n×m个阵列单元对不同气体的动态实时响应曲线；对响应曲线提取稳态响应特征和动态响应特征，通过机器学习，建立和训练用于识别气体种类的模型；在利用所述传感器检测待测气体时，通过测量电路获取测量参数，并输入所述用于识别气体种类的模型，实现待测气体种类的识别。即通过对信号进行特征提取和分析，以及模式识别算法，得到传感器特性参数与被测气体间的内在联系机制，实现选择性和探测精度等气敏性能的提升。
附图说明
图1为实施例3的传感器实物图；
图2为阵列单元示意图；
图3为实施例3步骤1的掩模版A示意图；
图4为实施例3步骤2的掩模版B1~B4示意图；
图5为实施例3步骤3的掩模版C1~C3示意图；
图6为实施例4对应的实物图。
具体实施方式
下面结合实施例对本发明作进一步说明，但不应该理解为本发明上述主题范围仅限于下述实施例。在不脱离本发明上述技术思想的情况下，根据本领域普通技术知识和惯用手段，做出各种替换和变更，均应包括在本发明的保护范围内。
实施例1：
本实施例提供一种阵列式气体传感器，其特征在于，其制备方法包括以下步骤:
1〕在传感器衬底上制备出n列×m行，共nm个磁控溅射孔位和电极；进而在后续步骤中，使得每一个磁控溅射孔位形成如图2所示的阵列单元。
本步骤中，制备时，需要采用掩模版A覆盖于衬底上，进行磁控溅射孔位的制备；其中掩模版A具有n列×m行，共nm个矩形阵列状排列的通孔；每个磁控溅射孔位中的阵列单元均连接两个电极； n和m为大于2的自然数；如图1所示，在一个实施例中，电极为Au电极。每一个阵列单元均具有两条电极。每一个电极连接器件边缘处的触点。
在一个实施例中，衬底材料为硅。需要在硅材料上方形成氧化层后，再进行磁控溅射孔位和电极的制备。
2〕采用掩模版B1～掩模版Bn依次覆盖于步骤1〕处理过的传感器衬底上，并依次采用n种溅射材料，通过磁控溅射将n种不同溅射材料分别生长到n列磁控溅射孔位上；其中，掩模版B1具有与第1列磁控溅射孔位对应的通孔、掩模版B2具有与第2列磁控溅射孔位对应的通孔、……掩模版Bn具有与第n列磁控溅射孔位对应的通孔；
3〕采用掩模版C1～掩模版Cm-1依次覆盖于步骤2〕处理过的传感器衬底上，并依次采用m-1种溅射材料，通过磁控溅射分别生长到磁控溅射孔位上； m-1种溅射材料，依次被编号为i，i＝1、2……m-1；这m-1种溅射材料与步骤2〕中所述n种溅射材料均不同；其中，第i种溅射材料生长到第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位上，n为奇数时，n/2舍位取整；其中，掩模版C1具有与第1行和第2行磁控溅射孔位对应的n/2个通孔、掩模版C2具有与第2行和第3行磁控溅射孔位对应的n/2个通孔……掩模版C1具有与第1行磁控溅射孔位对应的n/2个通孔。
步骤2和3中，磁控溅射材料选自MoS2、ZnO、WO3、TiO2、SnO2、C，HfO2、Ta2O5、Ga2O3。作为优选地，步骤3〕中，第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位错位分布。
实施例2：
本实施例提供一种类似于实施例1的阵列式气体传感器，进一步，步骤2〕和/或步骤3〕中，在全部或部分磁控溅射孔位完成材料溅射后，需要对溅射材料进行参杂；参杂选用的金属为Cu、Al、W、Pt、Cr、Mn、Ag、Ce、Sb。
实施例3：
本实施例提供一种如图1所示的阵列式气体传感器，其特征在于，其制备方法包括以下步骤:
1〕选用硅材料衬底，在硅材料上方形成氧化层后，在传感器衬底上制备出4列×4行，共16个磁控溅射孔位。制备磁控溅射孔位时，需要用到如图3所示的掩模版A。如图2所示，每一个磁控溅射孔位连接两个Au电极，每一个电极均连接到器件边缘处的触点。
2〕采用如图4所示的掩模版B1～B4依次覆盖于步骤1〕处理过的传感器衬底上，并依次采用4种溅射材料，通过磁控溅射将4种不同溅射材料分别生长到4列磁控溅射孔位上。
具体地：
掩模版B1覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料MoS2生长到第1列磁控溅射孔位对应的阵列单元中。
掩模版B2覆盖于步骤1〕处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料ZnO生长到第2列磁控溅射孔位对应的阵列单元中。
掩模版B3覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料WO3生长到第3列磁控溅射孔位对应的阵列单元中。
掩模版B4覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料SnO2生长到第4列磁控溅射孔位对应的阵列单元中。
3〕采用如图5所示的掩模版C1～3依次覆盖于步骤2〕处理过的传感器衬底上，并依次采用3种溅射材料，通过磁控溅射分别生长到磁控溅射孔位上；
具体地：
掩模版C1覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料HfO2生长到（行，列）编号为(1,1)、(1,3)、(2,2)、(2,4) 的磁控溅射孔位对应的阵列单元中。
掩模版C2覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料Ta2O5生长到（行，列）编号为(1,3)、(2,4)、(3,3)、(4,4)的磁控溅射孔位对应的阵列单元中。
掩模版C3覆盖于处理过的传感器衬底上，以Ar作为溅射气体，溅射时溅射功率保持在100W，衬底温度300℃，通过磁控溅射将溅射材料Ga2O3生长到（行，列）编号为(1,4)、(2,3)、(3,4)、(4,3)的磁控溅射孔位对应的阵列单元中。
实施例4：
本实施例将实施例3所述传感器的电极接入测量电路（如图6的照片所示），通过实验，采集传感器阵列中，16个阵列单元对不同气体的动态实时响应曲线；对采集到的数据提取稳态响应特征和动态响应特征，通过机器学习，建立和训练用于识别气体种类的模型；
在利用所述传感器检测待测气体时，通过测量电路获取测量参数，并输入所述用于识别气体种类的模型，实现待测气体种类的识别。
实施例5：
本实施例主要技术方案同实施例4，进一步，对传感器阵列响应曲线提取稳态响应特征和动态响应特征，包括：基于原始响应曲线的特征（峰值、面积、斜率等）、基于曲线拟合参数的特征、基于变换域的特征。由各特征组成特征矩阵，并通过特征评价算法，降低特征冗余度、选出最优特征子集。把超限学习机（ELM）神经网络作为分类器，对最优特征子集进行定量/定性识别，并利用量子粒子群优化算法优化分类器参数。
Claims (5)
1.一种阵列式气体传感器，其特征在于，其制备方法包括以下步骤:
1〕在所述传感器衬底上制备出n列×m行，共nm个磁控溅射孔位和电极；制备时，需要采用掩模版A覆盖于衬底上，进行磁控溅射孔位的制备；其中掩模版A具有n列×m行，共nm个矩形阵列状排列的通孔；每个磁控溅射孔位均连接两个电极； n和m为大于2的自然数；
2〕采用掩模版B1～掩模版Bn依次覆盖于步骤1〕处理过的传感器衬底上，并依次采用n种溅射材料，通过磁控溅射将n种不同溅射材料分别生长到n列磁控溅射孔位上；其中，掩模版B1具有与第1列磁控溅射孔位对应的通孔、掩模版B2具有与第2列磁控溅射孔位对应的通孔、……掩模版Bn具有与第n列磁控溅射孔位对应的通孔；
3〕采用掩模版C1～掩模版Cm-1依次覆盖于步骤2〕处理过的传感器衬底上，并依次采用m-1种溅射材料，通过磁控溅射分别生长到磁控溅射孔位上； m-1种溅射材料，依次被编号为i，i＝1、2……m-1；这m-1种溅射材料与步骤2〕中所述n种溅射材料均不同；其中，第i种溅射材料生长到第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位上，n为奇数时，n/2舍位取整；其中，掩模版C1具有与第1行和第2行磁控溅射孔位对应的n/2个通孔、掩模版C2具有与第2行和第3行磁控溅射孔位对应的n/2个通孔……掩模版C1具有与第1行磁控溅射孔位对应的n/2个通孔。
2.根据权利要求1所述的一种阵列式气体传感器，其特征在于：磁控溅射材料选自MoS2、ZnO、WO3、TiO2、SnO2、C、HfO2、Ta2O5、Ga2O3。
3.根据权利要求1所述的一种阵列式气体传感器，其特征在于：步骤2〕和/或步骤3〕中，在完成材料溅射后，对溅射材料进行参杂；参杂选用的金属为Cu、Al、W、Pt、Cr、Mn、Ag、Ce、Sb。
4.根据权利要求1所述的一种阵列式气体传感器，其特征在于：步骤3〕中，第i行的n/2个磁控溅射孔位上和第i+1行的n/2个磁控溅射孔位错位分布。
5.基于1～4任意一项权利要求1所述传感器的智能气体检测方法，其特征在于：将所述传感器的电极接入测量电路，采集传感器阵列中，n×m个阵列单元对不同气体的动态实时响应曲线；对响应曲线提取稳态响应特征和动态响应特征，通过机器学习，建立和训练用于识别气体种类的模型；
在利用所述传感器检测待测气体时，通过测量电路获取测量参数，并输入所述用于识别气体种类的模型，实现待测气体种类的识别。
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