CN114577865B - Multi-channel thermal conductivity type sensor array based on MEMS chip and analysis method - Google Patents

Multi-channel thermal conductivity type sensor array based on MEMS chip and analysis method Download PDF

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CN114577865B
CN114577865B CN202210495683.7A CN202210495683A CN114577865B CN 114577865 B CN114577865 B CN 114577865B CN 202210495683 A CN202210495683 A CN 202210495683A CN 114577865 B CN114577865 B CN 114577865B
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gas
heating wire
thermal conductivity
sensor
resistance
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CN114577865A (en
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徐凯
欧建臻
周辉
任光辉
夏南
欧睿
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Guangzhou Kebo Enterprise Management Co.,Ltd.
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Sichuan Zhilifang Bodao Science And Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
    • G01N27/14—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of an electrically-heated body in dependence upon change of temperature
    • G01N27/18—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of an electrically-heated body in dependence upon change of temperature caused by changes in the thermal conductivity of a surrounding material to be tested
    • G01N27/185—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of an electrically-heated body in dependence upon change of temperature caused by changes in the thermal conductivity of a surrounding material to be tested using a catharometer

Abstract

The invention discloses a multichannel thermal conductivity sensor array based on an MEMS chip and an analysis method, relating to the technical field of thermal conductivity type gas-sensitive sensing, comprising an MEMS substrate, wherein N groups of sensor units are arranged on the MEMS substrate in an array manner; the sensor unit comprises a pair of metal electrodes which are connected through a heating wire; a cavity is arranged right below the heating wire; the invention has the characteristics of simple structure, ultralow power consumption, small temperature drift, high sensitivity and the like, and the performance of the invention is far superior to that of the traditional thermal conductivity sensor; the materials or the structures of the sensor units can be different, so that the gas description variables are enriched; the obtained data is used for PCA modeling and can be used for accurately and rapidly analyzing a plurality of gas components in a complex environment.

Description

Multi-channel thermal conductivity type sensor array based on MEMS chip and analysis method
Technical Field
The invention relates to the technical field of thermal conductivity type gas-sensitive sensing, in particular to a multi-channel thermal conductivity type sensor array based on an MEMS chip and an analysis method.
Background
The thermal conductivity sensor heats gas molecules in the surrounding environment, and converts information such as the type and concentration of the gas into electric signal changes for measurement through different heat capacity characteristics of the gas molecules. Because of the use of a purely physical gas-sensitive mechanism, thermal conductivity sensors are more stable and reliable and have a longer service life than mainstream semiconductor or electrochemical sensors, and can be used to detect almost any gas component. Because of its non-dependence on oxygen and extremely strong toxic resistance, thermal conductivity sensors are mainly used in complex gas environments of various non-toxic or toxic harmful gases. However, since the heating element requires a high quality constant current source drive to achieve a stable high operating temperature. The conventional low-end thermal conductivity sensor generally has the defects of large power consumption, poor measurement accuracy, low sensitivity, large temperature drift and the like. Although the detection accuracy can be improved to some extent by adding wheatstone bridge compensation to reduce temperature drift interference, it will further increase hardware cost and power consumption and cannot make a great improvement in detection sensitivity.
Disclosure of Invention
The invention provides a multi-channel thermal conductivity type sensor array based on an MEMS chip and an analysis method aiming at the problems in the prior art.
The technical scheme adopted by the invention is as follows: a multichannel thermal conductivity sensor array based on an MEMS chip comprises an MEMS substrate, wherein N groups of sensor units are arranged on the MEMS substrate in an array manner; the sensor unit comprises a pair of metal electrodes which are connected through a heating wire; a cavity is arranged under the heating wire.
Further, an insulating layer is arranged on the MEMS substrate.
Furthermore, the depth of the cavity is 100-300 μm, and the thickness of the insulating layer is 0.2-2 μm.
A method for analyzing a multi-channel thermal conductivity sensor array based on a MEMS chip comprises the following steps:
step 1: placing an operating multi-channel thermal conductivity type sensor array based on an MEMS chip in a stable air environment, and obtaining resistance values of N groups of sensor units in the current gas environment;
step 2: setting the gas environment as measuring gas, and acquiring the resistance values of the N groups of sensor units in the current gas environment;
and step 3: repeating the step 2 to obtain the relative resistance change values, the heating wire conductivity and the cavity depth under M groups of different measurement gases to obtain a 3 MXN gas data matrix;
and 4, step 4: substituting the data matrix obtained in the step 3 into PCA training modeling to obtain a gas detection model; and analyzing by using the obtained gas detection model.
Further, the relative resistance change value, the heating wire conductivity and the cavity depth are used as characteristic variables to form a gas information function of the sensor:
Figure GDA0003702479220000021
wherein, C v Is the molar heat storage rate of gas molecules, rho is the density of the gas,
Figure GDA0003702479220000022
in terms of the relative rate of change of resistance, Δ x is the mean free path distance of the gas, and k is the thermal conductivity of the heater wire.
Further, the gas information function derivation process is as follows:
the relationship between the surface temperature of the heating wire and the resistance value is as follows:
Figure GDA0003702479220000023
in the formula: r is the current resistance value of the sensor unit, T is the absolute temperature of the surface of the heating wire, L is a Lorentz constant, and k is the thermal conductivity of the heating wire;
the temperature change and the resistance value change are related as follows:
Figure GDA0003702479220000024
then:
Figure GDA0003702479220000025
in the formula: delta T is the surface temperature change value of the heating wire, and delta R is the resistance change value;
the temperature change can be expressed as a conduction process of thermal energy from the surface of the heating wire to the surrounding gas molecules:
Figure GDA0003702479220000026
in the formula: q is the conducted heat energy, A is the contact area between the heating wire and the gas molecules, and deltax is the mean free path distance of the gas;
equation (4) can also be expressed as the process by which the gas molecules gain heat from the environment is as follows:
Q＝C v ·ρ·A·Δx·T (4)
derived from equations (3) and (4):
k·ΔT＝C v ·ρ·A·Δx 2 ·T
Figure GDA0003702479220000027
Figure GDA0003702479220000028
Figure GDA0003702479220000029
Figure GDA00037024792200000210
thereby, a gas information function of the sensor can be obtained:
Figure GDA00037024792200000211
the invention has the beneficial effects that:
(1) the invention has the characteristics of simple structure, ultralow power consumption, small temperature drift, high sensitivity and the like, and the performance of the invention is far superior to that of the traditional thermal conductivity sensor;
(2) the materials or the structures of all the sensor units in the multi-channel heat conduction type sensor array obtained by the invention can be different, and the gas description variables are enriched;
(3) the data obtained by the method is used for PCA modeling, and can be used for accurately and rapidly analyzing various gas components in a complex environment.
Drawings
Fig. 1 is a schematic structural diagram of a sensor array in embodiment 1 of the present invention.
FIG. 2 is a sectional view taken along line A-A in the schematic structural diagram of the present invention.
FIG. 3 is a cross-sectional view taken along line B-B of the schematic structural diagram of the present invention.
In the figure: 1-metal electrode, 2-insulating layer, 3-cavity, 4-MEMS substrate.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
As shown in fig. 1-3, a multi-channel thermal conductivity sensor array based on a MEMS chip includes a MEMS substrate 4, where N groups of sensor units are arranged in an array on the MEMS substrate 4; the sensor unit comprises a pair of metal electrodes 1, and the metal electrodes 1 are connected through a heating wire; a cavity 3 is arranged right below the heating wire. The MEMS substrate 4 is provided with an insulating layer 2. The depth of the cavity 3 is 100 to 300 μm, and the thickness of the insulating layer 2 is 0.2 to 2 μm.
The MEMS substrate 4 is a conventional silicon substrate with a thickness of about 0.5 mm, and the total area of the device depends on the number of processed sensors. The surface of the substrate is covered with an insulating thin layer, the material can be silicon dioxide, silicon nitride or aluminum oxide, and the like, and N groups of thermal conductivity sensor units can be processed on the same MEMS substrate.
The metal electrode 1 is used for an external circuit to provide a driving constant current and read the current resistance value. A pair of metal electrodes 1 are connected by a narrow metal wire of n μm width as a heating wire of a thermal conductivity type sensor. The electrode units (including the heating wires) of different thermal conductivity sensor units on the same MEMS can respectively adopt metals with different thermal conductivities, such as gold, silver, titanium, nickel, platinum, aluminum, copper and the like. An adhesive layer of 5-10 nm is grown between the metal electrode 1 and the insulating layer 2 by using metal chromium or titanium.
Cavity 3 a micro gas cavity is machined using KOH or TMAH wet etching. So that the heating wire is suspended right above the cavity 3 to form a micron transverse bridge. The gas cavities 3 of different thermal conductivity sensor units on the same MEMS may be of different dimensions of depth.
A method for analyzing a multi-channel thermal conductivity sensor array based on a MEMS chip comprises the following steps:
step 1: to be operated based on MPlacing a multi-channel thermal conductivity type sensor array of the EMS chip in a stable air environment, and waiting for 5-20 minutes until the gas components of the environment reach a dynamic balance point; obtaining resistance value R of N groups of sensor units in current gas environment base ；
And 2, step: setting a gas environment as a measurement gas, and waiting for 5-20 minutes until the gas components of the environment reach a dynamic balance point; obtaining resistance value R of N groups of sensor units in current gas environment gas ；
And step 3: repeating the step 2 to obtain the relative resistance change values of M groups of different measurement gases
Figure GDA0003702479220000041
Heating the conductivity and the cavity depth of the wire to obtain a 3 MxN gas data matrix;
wherein:
Figure GDA0003702479220000042
wherein
Figure GDA0003702479220000043
The relative resistance change value under the current measurement gas is obtained.
And 4, step 4: substituting the data matrix obtained in the step 3 into PCA training modeling to obtain a gas detection model; and analyzing by using the obtained gas detection model.
The data rows in the data matrix are used as input variables of the model, and the data columns are corresponding sample values of the input variables under the current gas condition. After PCA modeling is finished, the principal components PC1 and PC2 are extracted to express the types and concentration information of different gases, and a model basis is provided for rapid gas detection in the future.
Figure GDA0003702479220000044
In the formula: g1, g2, … gM respectively represent the parameter values corresponding to the first group gas, the second group gas, … Mth group gas test; s1, s2, … sN respectively indicate that the first sensor unit, the second sensor unit, and the … Nth sensor unit test the corresponding parameters.
The relative resistance change value, the heating wire conductivity and the cavity depth are used as characteristic variables to form a gas information function of the sensor:
Figure GDA0003702479220000045
wherein, C v Is the molar heat storage rate of gas molecules, rho is the density of the gas,
Figure GDA0003702479220000046
in terms of the relative rate of change of resistance, Δ x is the mean free path distance of the gas, and k is the thermal conductivity of the heater wire.
The gas information function derivation process is as follows:
due to the design of micro-nano processing, the heat loss of the sensor heating wire can be almost ignored, and according to the Wildman-Franz quantification, the surface temperature of the heating wire and the resistance value thereof are in a direct proportion relation: the relationship between the surface temperature of the heating wire and the resistance value is as follows:
Figure GDA0003702479220000051
in the formula: r is the current resistance value of the sensor unit, T is the absolute temperature of the surface of the heating wire, L is a Lorentz constant, and k is the thermal conductivity of the heating wire;
it follows that the resistance of the device depends on the thermal conductivity of the metal material of the heating filament at the current temperature, and that when the heating filament is fully exposed to gas molecules across the gas cavity and reaches the point of energy equilibrium, its surface temperature will change as a result of the energy transfer process between the heating filament and the ambient gas molecular environment.
The temperature change and the resistance value change are related as follows:
Figure GDA0003702479220000052
then:
Figure GDA0003702479220000053
in the formula: delta T is the surface temperature change value of the heating wire, and delta R is the resistance change value;
according to the fourier law, the above temperature variations can be expressed as a conduction process of thermal energy from the surface of the heating wire to the surrounding gas molecules:
Figure GDA0003702479220000054
in the formula: q is the conducted heat energy, A is the contact area between the heating wire and the gas molecules, and deltax is the mean free path distance of the gas;
when energy transfer is interpreted as a problem of the total energy required by the surrounding gas molecules to reach the current temperature from room temperature, its energy formula can be expressed as a function of the species, density and mean free path of one gas molecule.
Q＝C v ·ρ·A·Δx·T (4)
Derived from equations (3) and (4):
k·ΔT＝C v ·ρ·A·Δx 2 ·T
Figure GDA0003702479220000055
Figure GDA0003702479220000056
Figure GDA0003702479220000057
relative rate of change of resistance of thermal conductivity sensor
Figure GDA0003702479220000058
Can be equated to a function of gas molecular species and concentration at the present temperature conditions during the present period. Wherein C is v Depending primarily on the molecular structure of the gas, it can be considered a signature of the gas species. ρ and Δ x contain information on the concentration of gas molecules and the gas hole capacity, respectively. 1/k is the reciprocal of the metal thermal conductivity constant of the heating wire.
Figure GDA0003702479220000061
Thereby, a gas information function of the sensor can be obtained:
Figure GDA0003702479220000062
the gas information detected by the sensor is in a functional relationship with the heating wire material k and the sensor structure deltax.
Example 1
A multichannel thermal conductivity sensor array based on MEMS chip comprises an MEMS substrate 4, wherein the substrate is a conventional silicon substrate with the thickness of about 0.5 mm; the total area of the device is 90 multiplied by 20mm, and a silicon dioxide insulating layer with the thickness of 0.3 micron is covered on the surface of the silicon substrate. As shown in fig. 1, a total of 6 sets of thermal conductivity sensor cells were fabricated on the substrate. Each sensor unit has a pair of metal electrodes 1, and the pair of metal electrodes 1 are connected by a narrow metal wire with a width of 5 microns as a heating wire of the thermal conductivity type sensor. The three groups of sensor units on the upper half use gold electrodes, and the lower half use platinum electrodes. An adhesive layer of 5 nm thickness is provided between the electrode and the silicon dioxide insulating layer of the substrate to enhance the stability of the electrode and prevent peeling. A micro gas cavity 3 is machined in the center of each sensor unit just above the heater wire. The sensor heater wire is suspended right above the cavity 3 to form a micron transverse bridge. As shown in fig. 2, the depth of the left-most evacuation cavity in the substrate is 100 microns, the depth of the middle row is 200 microns, and the depth of the right-most row is 300 microns.
A method for analyzing a multi-channel thermal conductivity sensor array based on a MEMS chip comprises the following steps:
step 1: and placing the operated 6-channel thermal conductivity type sensor array chip in a stable air environment, waiting for 15 minutes until the ambient gas components reach a dynamic balance point, and acquiring the resistance values of the 6 groups of sensor units in the current gas environment.
And 2, step: setting the gas environment as measuring gas, waiting for 15 minutes until the gas components of the environment reach a dynamic balance point, and acquiring the resistance value of 6 groups of sensor units in the current gas environment;
and step 3: repeating the step 2 to obtain the relative resistance change value, the heater wire conductivity and the cavity depth under 20 groups of different measurement gases to obtain a 3 multiplied by 20 multiplied by 6 gas data matrix; after 20 groups of readings of different gases are obtained, the relative resistance change value of the sensor unit under the specific gas condition is obtained through processing according to the resistance values obtained in the step 1 and the step 2
Figure GDA0003702479220000063
And 4, step 4: substituting the data matrix obtained in the step 3 into PCA training modeling to obtain a gas detection model; and analyzing by using the obtained gas detection model. The data rows serve as input variables for the model and the data columns are corresponding sample values for each input variable under the current gas conditions. After PCA modeling is finished, the principal components PC1 and PC2 are extracted to express the types and concentration information of different gases, and a model basis is provided for rapid gas detection in the future.
The data matrix is as follows:
Figure GDA0003702479220000071
in the formula: gas1, gas2 and … gas20 respectively represent 20 test gases, and g1, g2 and … g20 respectively represent corresponding parameters obtained by testing the test gases in 20; 1. 2, … 6, respectively, indicate the parameters corresponding to the 6 sensor unit tests.
Each column of data in the matrix is a test sample value obtained by the same gas through different devices, and each row of data is an input variable value of each device under different test gases. And introducing a PCA analysis tool to analyze the data matrix, and performing a machine learning model training process on the sensor array by using the known gas sample information. The PCA modeling is a process of performing linear transformation on input variables and sample data through orthogonal transformation, and by projecting the data to a series of linear uncorrelated variable values, the data component PC1 and the minor component PC2 with the minimum correlation in the data can be extracted separately and projected to different regions. As the main components of the linear irrelevant data in the training data are the type and concentration information of the test gas, the PCA model obtained by the method is suitable for analyzing in a complex gas environment and obtaining specific gas component information.
The multichannel heat conduction sensor array obtained by the invention is based on an MEMS chip, and ten groups of heat conduction sensor units can be simultaneously processed and produced on a millimeter-sized silicon-based chip through a micro-nano processing technology. Because the heating element in each sensor unit is in a micron transverse bridge structure, the sensor unit has the characteristics of simple structure, strong interference resistance, high sensitivity and the like under the use condition of ultralow power consumption. As each sensor unit in the sensor array uses different heating materials or structures, the specific algorithm allows the chip to acquire enough learning data in parallel for PCA modeling, and the method can be applied to the complex gas environment for accurate and efficient gas detection and analysis.

Claims (3)

1. The analysis method of the multichannel thermal conductivity sensor array based on the MEMS chip is characterized in that the multichannel thermal conductivity sensor array based on the MEMS chip comprises an MEMS substrate (4), wherein N groups of sensor units are arranged on the MEMS substrate (4) in an array manner; the sensor unit comprises a pair of metal electrodes (1), and the metal electrodes (1) are connected through a heating wire; a cavity (3) is arranged right below the heating wire;
the analysis method comprises the following steps:
step 1: placing an operating multi-channel thermal conductivity type sensor array based on an MEMS chip in a stable air environment, and obtaining resistance values of N groups of sensor units in the current gas environment;
step 2: setting the gas environment as measuring gas, and acquiring the resistance values of the N groups of sensor units in the current gas environment;
and step 3: repeating the step 2 to obtain the relative resistance change values, the heating wire conductivity and the cavity depth under M groups of different measuring gases to obtain a 3 MxN gas data matrix; the relative resistance change value, the heating wire conductivity and the cavity depth are used as characteristic variables to form a gas information function of the sensor:
Figure FDA0003705174290000011
wherein, C v Is the molar heat storage rate of gas molecules, rho is the density of the gas,
Figure FDA0003705174290000012
the relative change rate of the resistance is shown, deltax is the mean free path distance of the gas, and k is the thermal conductivity of the heating wire;
the gas information function derivation process is as follows:
the relationship between the surface temperature of the heating wire and the resistance value is as follows:
Figure FDA0003705174290000013
in the formula: r is the current resistance value of the sensor unit, T is the absolute temperature of the surface of the heating wire, L is a Lorentz constant, and k is the thermal conductivity of the heating wire;
the temperature change and the resistance value change are related as follows:
Figure FDA0003705174290000014
then:
Figure FDA0003705174290000015
in the formula: delta T is the surface temperature change value of the heating wire, and delta R is the resistance change value;
the temperature change can be expressed as a conduction process of thermal energy from the surface of the heating wire to the surrounding gas molecules:
Figure FDA0003705174290000016
in the formula: q is the conducted heat energy, A is the contact area between the heating wire and the gas molecules, and deltax is the mean free path distance of the gas;
formula (3) can also be expressed as follows:
Q＝C v ·ρ·A·Δx·T (4)
derived from equations (3) and (4):
k·ΔT＝C v ·ρ·Δx 2 ·T
Figure FDA0003705174290000021
Figure FDA0003705174290000022
Figure FDA0003705174290000023
Figure FDA0003705174290000024
thereby, a gas information function of the sensor can be obtained:
Figure FDA0003705174290000025
and 4, step 4: substituting the data matrix obtained in the step 3 into PCA training modeling to obtain a gas detection model; and analyzing by using the obtained gas detection model.
2. The method of analyzing a multi-channel heat conductive sensor array based on a MEMS chip as claimed in claim 1, wherein an insulating layer (2) is disposed on the MEMS substrate (4).
3. The method of claim 2, wherein the depth of the cavity (3) is 100-300 μm, and the thickness of the insulating layer (2) is 0.2-2 μm.
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