CN114527173A - 一种阵列式气体传感器及智能气体检测方法 - Google Patents

一种阵列式气体传感器及智能气体检测方法 Download PDF

Info

Publication number
CN114527173A
CN114527173A CN202111573969.4A CN202111573969A CN114527173A CN 114527173 A CN114527173 A CN 114527173A CN 202111573969 A CN202111573969 A CN 202111573969A CN 114527173 A CN114527173 A CN 114527173A
Authority
CN
China
Prior art keywords
magnetron sputtering
sputtering
hole
mask
sensor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111573969.4A
Other languages
English (en)
Inventor
彭小燕
陈家正
王顺
褚金
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest University
Original Assignee
Southwest University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest University filed Critical Southwest University
Priority to CN202111573969.4A priority Critical patent/CN114527173A/zh
Publication of CN114527173A publication Critical patent/CN114527173A/zh
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/26Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/28Electrolytic cell components
    • G01N27/30Electrodes, e.g. test electrodes; Half-cells
    • G01N27/304Gas permeable electrodes
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/04Coating on selected surface areas, e.g. using masks
    • C23C14/042Coating on selected surface areas, e.g. using masks using masks
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/0605Carbon
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/0623Sulfides, selenides or tellurides
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/08Oxides
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/08Oxides
    • C23C14/083Oxides of refractory metals or yttrium
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/08Oxides
    • C23C14/086Oxides of zinc, germanium, cadmium, indium, tin, thallium or bismuth
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/22Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
    • C23C14/34Sputtering
    • C23C14/35Sputtering by application of a magnetic field, e.g. magnetron sputtering
    • CCHEMISTRY; METALLURGY
    • C23COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23CCOATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/58After-treatment
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/26Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/416Systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning

Landscapes

  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Materials Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Electrochemistry (AREA)
  • Immunology (AREA)
  • Molecular Biology (AREA)
  • Theoretical Computer Science (AREA)
  • Pathology (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Investigating Or Analyzing Materials By The Use Of Fluid Adsorption Or Reactions (AREA)

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

一种阵列式气体传感器及智能气体检测方法
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。
CN202111573969.4A 2021-12-21 2021-12-21 一种阵列式气体传感器及智能气体检测方法 Pending CN114527173A (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111573969.4A CN114527173A (zh) 2021-12-21 2021-12-21 一种阵列式气体传感器及智能气体检测方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111573969.4A CN114527173A (zh) 2021-12-21 2021-12-21 一种阵列式气体传感器及智能气体检测方法

Publications (1)

Publication Number Publication Date
CN114527173A true CN114527173A (zh) 2022-05-24

Family

ID=81618568

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111573969.4A Pending CN114527173A (zh) 2021-12-21 2021-12-21 一种阵列式气体传感器及智能气体检测方法

Country Status (1)

Country Link
CN (1) CN114527173A (zh)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1975397A (zh) * 2006-12-21 2007-06-06 天津大学 三氧化钨薄膜气敏传感器的表面改性方法
CN101299031A (zh) * 2008-06-27 2008-11-05 中国科学院合肥物质科学研究院 一种基于气体传感器阵列的汽车尾气检测方法
CN101458220A (zh) * 2008-12-22 2009-06-17 中国航天科技集团公司第五研究院第五一○研究所 利用铂钯掺杂的so2薄膜的多传感器及其制备方法
CN103558253A (zh) * 2013-11-11 2014-02-05 中国石油大学(华东) 基于钯/二氧化钛/二氧化硅/硅异质结的氢气探测器
US8702962B1 (en) * 2007-05-25 2014-04-22 The United States Of America As Represented By The Administrator Of National Aeronautics And Space Administration Carbon dioxide gas sensors and method of manufacturing and using same
CN108956714A (zh) * 2018-06-29 2018-12-07 五邑大学 ZnO/Si纳米/微米柱阵列敏感材料及其制备方法和传感器
CN110726758A (zh) * 2019-09-26 2020-01-24 华南理工大学 一种气敏探测模块、制造方法及系统
CN210775316U (zh) * 2019-06-20 2020-06-16 南京云创大数据科技股份有限公司 一种基于wo3纳米薄膜的可燃气体传感器
WO2021067756A1 (en) * 2019-10-04 2021-04-08 North Carolina State University Monolithically integrated and densely packed array sensor platform for ultra-low power gas sensing applications
CN113358701A (zh) * 2021-06-04 2021-09-07 华中科技大学 一种大规模阵列气体传感器及其制备方法

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1975397A (zh) * 2006-12-21 2007-06-06 天津大学 三氧化钨薄膜气敏传感器的表面改性方法
US8702962B1 (en) * 2007-05-25 2014-04-22 The United States Of America As Represented By The Administrator Of National Aeronautics And Space Administration Carbon dioxide gas sensors and method of manufacturing and using same
CN101299031A (zh) * 2008-06-27 2008-11-05 中国科学院合肥物质科学研究院 一种基于气体传感器阵列的汽车尾气检测方法
CN101458220A (zh) * 2008-12-22 2009-06-17 中国航天科技集团公司第五研究院第五一○研究所 利用铂钯掺杂的so2薄膜的多传感器及其制备方法
CN103558253A (zh) * 2013-11-11 2014-02-05 中国石油大学(华东) 基于钯/二氧化钛/二氧化硅/硅异质结的氢气探测器
CN108956714A (zh) * 2018-06-29 2018-12-07 五邑大学 ZnO/Si纳米/微米柱阵列敏感材料及其制备方法和传感器
CN210775316U (zh) * 2019-06-20 2020-06-16 南京云创大数据科技股份有限公司 一种基于wo3纳米薄膜的可燃气体传感器
CN110726758A (zh) * 2019-09-26 2020-01-24 华南理工大学 一种气敏探测模块、制造方法及系统
WO2021067756A1 (en) * 2019-10-04 2021-04-08 North Carolina State University Monolithically integrated and densely packed array sensor platform for ultra-low power gas sensing applications
CN113358701A (zh) * 2021-06-04 2021-09-07 华中科技大学 一种大规模阵列气体传感器及其制备方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
郑耀添: "基于单片机的低功耗甲烷检测系统设计", 单片机开发与应用, vol. 24, no. 2, 15 February 2008 (2008-02-15) *

Similar Documents

Publication Publication Date Title
Toda et al. Training instance segmentation neural network with synthetic datasets for crop seed phenotyping
Chitwood et al. Latent developmental and evolutionary shapes embedded within the grapevine leaf
Lin et al. Lightweight residual convolutional neural network for soybean classification combined with electronic nose
CN110213788A (zh) 基于数据流时空特征的wsn异常检测及类型识别方法
CN112163703A (zh) 考虑气象因子不确定性的农田参考作物蒸散量预测方法
CN106295801B (zh) 一种基于果蝇算法优化广义回归神经网络算法的茶叶储存时间分类方法
CN110321774B (zh) 农作物灾情评估方法、装置、设备及计算机可读存储介质
JP2021509212A (ja) 病害の予測・コントロール方法、及びそのシステム
CN110414366B (zh) 一种基于动态信号的压阻阵列及压力分布匹配方法
CN111896495A (zh) 基于深度学习与近红外光谱太平猴魁产地甄别方法及系统
CN114399719A (zh) 一种变电站火灾视频监测方法
CN114527173A (zh) 一种阵列式气体传感器及智能气体检测方法
WO2022188425A1 (zh) 一种融入先验知识的深度学习故障诊断方法
Cui et al. Deep learning methods for atmospheric PM2. 5 prediction: A comparative study of transformer and CNN-LSTM-attention
Lu et al. Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network
Shukla et al. Early Detection of Potato Leaf Diseases using Convolutional Neural Network with Web Application
Singh et al. Deep transfer learning-based automated detection of blast disease in paddy crop
CN111210081A (zh) 一种基于Bi-GRU的PM2.5数据处理与预测方法
CN111242369A (zh) 基于多重融合卷积gru的pm2.5数据预测方法
CN107169407A (zh) 基于联合双边滤波与极限学习机的高光谱图像分类方法
CN115965875A (zh) 一种农作物病虫害智能监控方法及系统
CN116681929A (zh) 一种麦类作物病害图像识别方法
Chang et al. Recognition of wheat rusts in a field environment based on improved DenseNet
CN112099438B (zh) 一种基于电流信号的机床节能控制方法及装置
CN112819343A (zh) 基于特征识别和大数据分析的智慧农业农作物区域种植监测分析方法

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination