CN118602958A - Film thickness online measurement method - Google Patents
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Abstract
The invention discloses a film thickness online measurement method, which comprises the following steps: calibrating a film thickness measuring device; selecting a substrate of a sample to be tested and the material type of the sample; placing a substrate of a sample to be detected on a sample placing table, and detecting a reflected bright light field spectrum and a reflected dark light field spectrum of the substrate; placing a substrate attached with a film to be detected on a sample placing table, and subtracting the difference between the reflection light field spectrum of the substrate and the reflection dark field spectrum of the substrate from the detected reflection spectrum to obtain the reflection spectrum of the film; after the acquisition of the reflection spectrum of the film is completed, the microcomputer automatically analyzes the reflection spectrum of the film and outputs the measurement result of the thickness of the film; the position of the sample placing table is changed, and multi-point measurement of the film thickness is realized. According to the method, the spectrometer is adopted to collect the film reflection spectrum, and the film thickness can be measured on line through the film thickness measurement network model, so that the measuring speed is high, the result is accurate, and the cost is low.
Description
Technical Field
The invention relates to the technical field of film thickness measurement, in particular to an online film thickness measurement method.
Background
Optoelectronic devices generally consist of thin films, the thickness of which is a critical factor affecting the optoelectronic device during its fabrication, and thus it is desirable to accurately detect the thickness of the thin film as it is being fabricated.
Currently, thin film thickness measurement techniques are largely classified into mechanical scanning methods and optical measurement methods. Although the mechanical scanning method using the step meter as an example has higher precision and measurement range, the mechanical scanning method has a plurality of limitations in practical production due to the low measurement speed, the fact that only a single-point film thickness can be measured, the film needs to be damaged, and the like. At present, an optical measurement method has become a mainstream method for measuring the thickness of a film, and can be divided into an ellipsometer, a structure illumination microscope and the like according to an experimental principle.
When the film thickness is measured by an ellipsometer based on incident light with a certain polarization state and reflected on the surface of a sample, the amplitude attenuation ratio and the phase difference of s-wave and p-wave of the reflected light are changed, the polarization state of the reflected light is finally changed, then the incident light vectors are point-by-point multiplied by a Jones matrix, so that electromagnetic field vectors of the s-wave and the p-wave are obtained, and corresponding reflection coefficients are calculated. According to the Fresnel formula, the theoretical expression of the phase difference of the amplitude attenuation coefficients of the s wave and the p wave can be inversely solved by the ratio of the two reflection coefficients, wherein the variables are the thickness and the refractive index of the film. Thus, the thickness may be calculated at a known refractive index, or the film refractive index may be calculated at a known thickness.
A range of film thickness profiles can be obtained for thin films using structured light illumination microscopy (MSIM) to measure film thickness. The method projects a sinusoidal stripe pattern generated by a digital micromirror device onto a film, acquires film substrate and surface reflection images by using a CCD, moves a sample to a Z-direction position, judges a focus position according to interference stripe contrast ratio change, compares theoretical light intensity distribution taking film optical thickness as a variable at the position, and can realize film thickness measurement.
However, the optical measurement method and the mechanical scanning method have the disadvantages of low measurement speed, few measurement points, high cost and the like. It is difficult to meet the actual production needs.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the invention aims to provide the film thickness online measurement method, which adopts a spectrometer to collect the film reflection spectrum, and can realize online measurement of the film thickness through a film thickness measurement network model, and has the advantages of high measurement speed, accurate result and low cost.
The invention further aims to provide a method for generating the film thickness measurement network model, which has the advantages of high iterative convergence speed and high measurement accuracy in the training process.
Still another object of the present invention is to provide the apparatus for generating a film thickness measurement network model.
The aim of the invention is achieved by the following technical scheme:
one embodiment of the invention provides a film thickness online measurement method, which is based on a film thickness measurement device; the film thickness measuring device comprises a sample unit, a light path unit and a data processing unit;
The sample unit comprises a sample placing and platform moving motor; the platform moving motor is used for driving the sample placing table to move on a two-dimensional plane;
The light path unit comprises a halogen tungsten lamp light source, an optical fiber probe, a spectrometer, a first optical fiber and a second optical fiber; the halogen tungsten lamp light source is connected with the optical fiber probe through a first optical fiber, and the optical fiber probe is connected with the spectrometer through a second optical fiber; the optical fiber probe is positioned above the sample placing table;
When the film is measured, the emergent light of the halogen tungsten light source is output to the optical fiber probe through the first optical fiber, is incident into the film and is subjected to multi-beam interference at the upper interface and the lower interface of the film, the reflected light carrying the thickness information of the film is received by the optical fiber probe and is transmitted to the spectrometer through the second optical fiber, the spectrometer collects the reflected spectrum data, and the data processing unit analyzes and processes the data to output the measurement result of the thickness of the film;
the data processing unit is internally provided with a film thickness measurement network model;
the film thickness measurement network model is obtained by the following method:
s11, generating a data set:
s111, according to the refractive indexes of materials of the film and the substrate, obtaining a reflectivity fitting function of the refractive indexes of the film and the substrate along with the change of wavelength through polynomial nonlinear fitting of mathematical analysis software;
s112, selecting a wave band with an extinction coefficient close to zero, and generating a film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m according to a reflectivity fitting function of the refractive indexes of the film and the substrate in the wave band, which is changed along with the wavelength, and a set film thickness training step length;
S113, adopting a Python built-in function, introducing normal distribution noise into a film theoretical reflectivity spectrum, generating a plurality of samples, and dividing the plurality of samples into a training set and a verification set;
s114, changing the materials of the film and the substrate, and repeating the steps S111-S114 to obtain data sets of the films and the substrates of different materials;
S12, building a convolutional neural network model:
The convolutional neural network model comprises a first convolutional activation layer, a first pooling layer, a second convolutional activation layer, a second pooling layer, an unfolding layer and a full-connection layer which are sequentially connected; the first convolution activation layer comprises a one-dimensional convolution operation module and a nonlinear activation function module; the first convolution activation layer comprises a first one-dimensional convolution operation module and a first nonlinear activation function module; the second convolution activation layer comprises a second one-dimensional convolution operation module and a second nonlinear activation function module; the convolution neural network model takes the reflectivity spectrum of the film as input and the thickness of the film as output;
s13, training a convolutional neural network model;
S14, convolutional neural network model verification;
The film thickness online measurement method comprises the following steps:
s1, calibrating the film thickness measuring device by using a standard sample;
s2, selecting a substrate of a sample to be tested and the material type of the sample;
S3, placing a substrate of a sample to be detected on a sample placing table, and moving a motor through a control platform to enable the motor to be positioned right below the optical fiber probe, and detecting a reflected bright light field spectrum and a reflected dark light field spectrum of the substrate;
S4, placing the substrate attached with the film to be tested on a sample placing table, moving a motor through a control platform to enable the motor to be located under the optical fiber probe, and subtracting the difference between the reflection light field spectrum of the substrate and the reflection dark light field spectrum of the substrate from the detected reflection spectrum to obtain the reflection spectrum of the film;
s5, after the acquisition of the reflection spectrum of the film is completed, the data processing unit automatically analyzes the reflection spectrum of the film and outputs a measurement result of the thickness of the film;
S6, controlling a platform moving motor to change the position of the sample placing table, and realizing multipoint measurement of the film thickness.
In one embodiment of the present invention, the convolutional neural network model training in step S13 is specifically implemented in the following manner:
By adopting an Adam optimization mode, taking 0.005 as a learning rate and cross entropy as a loss function, if the loss value is reduced in a plurality of iteration cycles, the learning rate is reduced by 10%.
In one embodiment of the present invention, in the step of constructing the convolutional neural network model in step S12, the input is set to 50 matrices of 200×1, the first one-dimensional convolutional operation module is set to 64 convolutional kernels, the second one-dimensional convolutional operation module is set to 128 convolutional kernels, flattening is performed after the convolutional operation is completed, a matrix of 6400×1 is obtained, and finally the matrix is fully connected to the film thickness label.
In one embodiment, the convolution kernel is set to 3×1, the stride is set to 1, and the edge zero padding is set to 1; the pooling core in the first pooling layer and the second pooling layer is set to 3×1, and the stride is set to 2.
In one embodiment, the film thickness training step is 50nm.
In one embodiment, the film thickness measuring device further comprises a display and control unit; the display and control unit comprises a display screen and control buttons; the display screen is used for displaying the reflection spectrum of the sample and the measurement result of the film thickness; the control unit is used for setting sample parameters and measurement parameters.
In one embodiment, the sample placement table is further provided with an adjusting knob, which is used for adjusting the angle between the sample placement table and the horizontal plane, so that the sample placement table is horizontally placed; the halogen tungsten lamp light source is also connected with a light source adjusting knob; the light source adjusting knob is used for adjusting the light intensity of the light source of the halogen tungsten lamp.
In one embodiment, the data processing unit is a microcomputer; the film thickness measuring network model is written by Python, and is imported into a microcomputer after training and verification are completed.
One embodiment of the present invention further provides a method for generating a film thickness measurement network model, including the following steps: s11, generating a data set:
s111, according to the refractive indexes of materials of the film and the substrate, obtaining a reflectivity fitting function of the refractive indexes of the film and the substrate along with the change of wavelength through polynomial nonlinear fitting of mathematical analysis software;
s112, selecting a wave band with an extinction coefficient close to zero, and generating a film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m according to a reflectivity fitting function of the refractive indexes of the film and the substrate in the wave band, which is changed along with the wavelength, and a set film thickness training step length;
S113, adopting a Python built-in function, introducing normal distribution noise into a film theoretical reflectivity spectrum, generating a plurality of samples, and dividing the samples into a training set and a verification set;
s114, changing the materials of the film and the substrate, and repeating the steps S111-S113 to obtain data sets of the films and the substrates of different materials;
S12, building a convolutional neural network model:
The convolutional neural network model comprises a first convolutional activation layer, a first pooling layer, a second convolutional activation layer, a second pooling layer, a full-connection layer and a spreading layer which are sequentially connected; the first convolution activation layer comprises a first one-dimensional convolution operation module and a first nonlinear activation function module; the second convolution activation layer comprises a second one-dimensional convolution operation module and a second nonlinear activation function module; the convolution neural network model takes the reflectivity spectrum of the film as input and the thickness of the film as output;
S13, training a convolutional neural network model: adopting an Adam optimization mode, taking 0.005 as a learning rate, taking cross entropy as a loss function, and if the loss value is reduced in a plurality of iteration cycles, reducing the learning rate by 10%;
s14, convolutional neural network model verification.
One embodiment of the present invention also provides an apparatus for generating a film thickness measurement network model, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor being configured as steps of the method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The film thickness online measurement method adopts the spectrometer to collect the film reflection spectrum and generate the film thickness measurement network model which takes the film reflection spectrum as input, can realize the online measurement of the film thickness, and has the advantages of high measurement speed, accurate result and low cost.
(2) According to the film thickness measurement network model generation method, in the data set generation step, the film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m is generated, normal distribution noise is introduced into the film theoretical reflectivity spectrum, a plurality of samples are generated, the diversity of data is improved, the occurrence of the over-fitting phenomenon is prevented, a large number of training samples can be generated under the same film thickness, and the accuracy of the measurement network model is guaranteed.
(3) According to the film thickness measurement network model generation method, a neural network consisting of two convolution active layers, two pooling layers, one flattening layer and one full-connection layer is built, and after 20 iterations of parameter optimization and training optimization of the neural network are carried out, the classification accuracy of the neural network can reach 99%.
Drawings
Fig. 1 is a schematic diagram showing the composition of a thin film thickness measuring apparatus according to an embodiment of the present invention.
Fig. 2 is a schematic cross-sectional view of a control cabinet of a film thickness measuring apparatus according to an embodiment of the present invention.
FIG. 3 is a flow chart of a film thickness measurement network model according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a spectrum generation process according to an embodiment of the present invention.
FIG. 5 is a plot of the reflectance fit as a function of wavelength for films and substrates obtained in accordance with an embodiment of the present invention.
Fig. 6 is a graph of theoretical reflectance spectra before and after noise is introduced in an embodiment of the present invention.
FIG. 7 is a schematic diagram of a convolutional neural network model of an embodiment of the present invention.
Fig. 8 shows the change in the loss values of the training set and the test set in an embodiment of the present invention.
FIG. 9 is a flow chart of a film thickness measurement method according to an embodiment of the invention.
FIG. 10 is a graph showing the comparison of the actual spectrum and the predicted spectrum in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples, but embodiments of the present invention are not limited thereto.
Examples
Referring to fig. 1 and 2, one embodiment of the present invention provides a thin film thickness measuring apparatus, which includes a data processing unit, an optical path unit, and a sample unit.
The data processing unit includes a microcomputer 2; the sample unit comprises a sample placing table 3, a platform moving motor 4 and an adjusting knob 5, wherein the platform moving motor 4 is used for driving the sample placing table 3 to move on a two-dimensional plane; the adjusting knob 5 is used for adjusting the angle between the sample placing table 3 and the horizontal plane, so that the sample placing table 3 is horizontally placed. The light path unit comprises a halogen tungsten lamp light source 6, an optical fiber probe 7, a first optical fiber 8, a spectrometer 1 and a second optical fiber 9; the halogen tungsten lamp light source 6 is connected with the optical fiber probe 7 through a first optical fiber 8, and the optical fiber probe 7 is connected with the spectrometer 1 through a second optical fiber 9; the fiber optic probe 7 is located above the sample placement stage 3. Wherein the halogen tungsten lamp light source 6, the spectrometer 1 and the microcomputer 2 are positioned in the control cabinet 10. The control cabinet 10 is provided with a display screen and control buttons connected to the microcomputer 2. The halogen tungsten lamp light source 6 is provided with a light source adjusting knob for adjusting the intensity of the halogen tungsten lamp light source.
In the above embodiment, when the film thickness measuring device measures the film, the outgoing light of the halogen tungsten light source is output to the optical fiber probe through the first optical fiber, enters the film and generates multi-beam interference at the upper and lower interfaces of the film, the reflected light carrying the film thickness information is received by the optical fiber probe, and is transmitted to the spectrometer through the second optical fiber, the spectrometer collects the reflected spectrum data, and after analysis and processing by the data processing unit, the measurement result of the film thickness is output.
In one embodiment of the invention, a film thickness measuring network model is built in the microcomputer, the film thickness measuring network model is written by Python, and the film thickness measuring network model is imported into the microcomputer after training and verification are completed.
Referring to fig. 3, in one embodiment of the present invention, the film thickness measurement network model generation method is as follows:
s11, generating a data set:
s111, according to the refractive indexes of materials of the film and the substrate, obtaining a reflectivity fitting function of the refractive indexes of the film and the substrate along with the change of wavelength through polynomial nonlinear fitting of mathematical analysis software;
S112, selecting a wave band with an extinction coefficient close to zero, and generating a film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m by adopting a spectrum generation module according to a reflectivity fitting function of the refractive indexes of the film and the substrate in the wave band, which is changed along with the wavelength, and the set film thickness training step length;
S113, adopting a Python built-in function, introducing normal distribution noise into a film theoretical reflectivity spectrum, generating a plurality of samples, and dividing the plurality of samples into a training set and a verification set;
s114, changing the materials of the film and the substrate, and repeating the steps S111-S113 to obtain data sets of the films and the substrates of different materials;
S12, building a convolutional neural network model:
In one embodiment, the convolutional neural network model comprises a first convolutional activation layer, a first pooling layer, a second convolutional activation layer, a second pooling layer, an unfolding layer and a full-connection layer which are sequentially connected; the first convolution activation layer comprises a first one-dimensional convolution operation module and a first nonlinear activation function module; the second convolution activation layer comprises a second one-dimensional convolution operation module and a second nonlinear activation function module; the convolution neural network model takes the reflectivity spectrum of the film as input and the thickness of the film as output;
s13, training a convolutional neural network model;
s14, convolutional neural network model verification.
In the above embodiment, the principle of the spectrum generation module is as follows:
as shown in fig. 4, the incident angle of light in the panel is θ 0, and the optical path difference of the two successive light beams is d=2n 1dcosθ0. If θ 0 =0 at normal incidence, the corresponding phase difference Where n 1 d is the optical thickness of the film and λ is the wavelength of light in vacuo. At this time, according to the fresnel formula, the reflection coefficients on the upper and lower surfaces of the film are respectively:
The first order reflected beam and the other order reflected beams can be described as:
the resultant intensity of the reflected light thus obtained is:
the film reflectivity can be expressed as:
it can be seen that there are two unknowns in this reflectance-generating model, the wavelength of light and the thickness of the film, respectively.
In one embodiment of the present invention, taking a silicon substrate and a Pi film as an example, the following are specifically set in the film thickness measurement network model generation process:
Generating a data set: after obtaining the reflectance fitting function of the refractive indexes of the film and the substrate along with the change of the wavelength (as shown in fig. 5), selecting a part with an extinction coefficient close to zero (such as less than 0.05), namely a 600-800nm wave band, generating a theoretical reflectance spectrum of the film with the thickness between 500nm and 10 mu m and the interval of 50nm, introducing normal distribution noise according to a Python built-in function, and then taking the normal distribution noise as a model training set, wherein the theoretical reflectance spectrums before and after the noise is introduced are respectively shown in (a) and (b) in fig. 6. A total of 190 film thickness labels, 30 training samples under each label, 20 test samples, and 9500 sample lead-in models for training and verification.
Building a convolutional neural network model: the convolutional neural network model is shown in fig. 7, and is composed of two convolutional active layers (Conv and ReLU), two pooling layers (Pooling), one flattening layer (RESHAPING) and one full-connection layer (FC), the input is set to 50 matrices of 200×1, the first convolutional operation is set to 64 convolutional kernels, the second convolutional operation is set to 128 convolutional kernels, flattening is performed after the convolutional operation is completed, a matrix of 6400×1 is obtained, and finally the matrix is fully connected to 190 film thickness labels. It is proved by multiple practices that the convolution kernel is set to 3×1, the stride is set to 1, the padding is set to 3×1, the stride is set to 2, and the best classification effect can be obtained in the minimum iteration times.
Convolutional neural network model training: adopts Adam optimization mode and takes 0.005 as learning rate. If the loss value is reduced in a plurality of iteration cycles, the learning rate is reduced by 10%, and the model is ensured to be high-efficiency and approaching to the optimal solution. After the model is iterated for 20 times by taking the cross entropy as a loss function, the loss values of the training set and the test set are obviously reduced (as shown in fig. 8). The accuracy of the classification theory spectrum data reaches 99%, and the classification capability of the model on spectrum data of different film thicknesses is fully proved.
After training the film thickness measurement network model, film thickness measurement is performed as follows (as shown in fig. 9):
s1, calibrating the film thickness measuring device by using a standard sample;
s2, selecting a substrate of a sample to be tested and the material type of the sample;
S3, placing a substrate of a sample to be detected on a sample placing table, and moving a motor through a control platform to enable the motor to be positioned right below the optical fiber probe, and detecting a reflected bright light field spectrum and a reflected dark light field spectrum of the substrate;
S4, placing the substrate attached with the film to be tested on a sample placing table, moving a motor through a control platform to enable the motor to be located under the optical fiber probe, and subtracting the difference between the reflection light field spectrum of the substrate and the reflection dark light field spectrum of the substrate from the detected reflection spectrum to obtain the reflection spectrum of the film;
S5, after the acquisition of the reflection spectrum of the film is completed, the microcomputer automatically analyzes the reflection spectrum of the film and outputs a measurement result of the thickness of the film;
S6, controlling a platform moving motor to change the position of the sample placing table, and realizing multipoint measurement of the film thickness.
Embodiments of the present invention also perform the following verification: firstly, comparing a predicted theoretical spectrum data value with actual spectrum data; and secondly, measuring the actual film thickness by using a step meter and comparing the actual film thickness with the predicted value of the invention.
And (3) verifying:
Measuring a film sample (taking a silicon substrate and a Pi film as an example) through the steps S1-S4, wherein four sampling points are used in the measuring process to obtain actual spectrum data; the actual spectrum is compared with the theoretical spectrum (generated by steps S111 to S112), and the results are shown in fig. 10 (a) to (d). As can be seen from fig. 10, the actual spectrum substantially matches the predicted spectrum in the 600-800nm range. The thickness of the film is dependent on the magnitude of the peak-to-peak separation of the reflectance spectrum, with the refractive index unchanged.
And II, verification:
As a film thickness verification instrument, a American Ambios technology company XP-200 step gauge was used, and the parameters of the step gauge were as follows: slide table diameter size: 6 inches; scanning length: 50 μm to 55mm; vertical measurement range: 524 μm; three-dimensional scanning function module: the device comprises measurement software, a color variable-focus camera, a precise vibration isolation platform and a micrometer standard calibration module.
Firstly, the film thickness measurement network model of the embodiment is adopted to predict the thickness of four sampling points on a film sample (taking a silicon substrate and a Pi film as an example), then a step meter measurement method is adopted to measure the vertical distance between the surface of the film and the surface of the substrate for three times at the position of the sampling points respectively, the average is taken, the film thickness of four points is calculated, the accuracy of the measurement result of the invention is verified, and the result is shown in the table 1.
The data in Table 1 shows that the error is between 1.1 and 3.6 percent, which proves that the invention has good film thickness measurement level and high reliability and repeatability.
Table 1 sample data statistics
The embodiments described above are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the embodiments described above, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principles of the present invention should be made in the equivalent manner, and are included in the scope of the present invention.
Claims (10)
1. The online film thickness measuring method is characterized by being based on a film thickness measuring device; the film thickness measuring device comprises a sample unit, a light path unit and a data processing unit;
The sample unit comprises a sample placing and platform moving motor; the platform moving motor is used for driving the sample placing table to move on a two-dimensional plane;
The light path unit comprises a halogen tungsten lamp light source, an optical fiber probe, a spectrometer, a first optical fiber and a second optical fiber; the halogen tungsten lamp light source is connected with the optical fiber probe through a first optical fiber, and the optical fiber probe is connected with the spectrometer through a second optical fiber; the optical fiber probe is positioned above the sample placing table;
When the film is measured, the emergent light of the halogen tungsten light source is output to the optical fiber probe through the first optical fiber, is incident into the film and is subjected to multi-beam interference at the upper interface and the lower interface of the film, the reflected light carrying the thickness information of the film is received by the optical fiber probe and is transmitted to the spectrometer through the second optical fiber, the spectrometer collects the reflected spectrum data, and the data processing unit analyzes and processes the data to output the measurement result of the thickness of the film;
the data processing unit is internally provided with a film thickness measurement network model;
the film thickness measurement network model is obtained by the following method:
s11, generating a data set:
s111, according to the refractive indexes of materials of the film and the substrate, obtaining a reflectivity fitting function of the refractive indexes of the film and the substrate along with the change of wavelength through polynomial nonlinear fitting of mathematical analysis software;
s112, selecting a wave band with an extinction coefficient close to zero, and generating a film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m according to a reflectivity fitting function of the refractive indexes of the film and the substrate in the wave band, which is changed along with the wavelength, and a set film thickness training step length;
S113, adopting a Python built-in function, introducing normal distribution noise into a film theoretical reflectivity spectrum, generating a plurality of samples, and dividing the plurality of samples into a training set and a verification set;
s114, changing the materials of the film and the substrate, and repeating the steps S111-S114 to obtain data sets of the films and the substrates of different materials;
S12, building a convolutional neural network model:
the convolutional neural network model comprises a first convolutional activation layer, a first pooling layer, a second convolutional activation layer, a second pooling layer, an unfolding layer and a full-connection layer which are sequentially connected; the first convolution activation layer comprises a first one-dimensional convolution operation module and a first nonlinear activation function module; the second convolution activation layer comprises a second one-dimensional convolution operation module and a second nonlinear activation function module; the convolution neural network model takes the reflectivity spectrum of the film as input and the thickness of the film as output;
s13, training a convolutional neural network model;
S14, convolutional neural network model verification;
The film thickness online measurement method comprises the following steps:
s1, calibrating the film thickness measuring device by using a standard sample;
s2, selecting a substrate of a sample to be tested and the material type of the sample;
S3, placing a substrate of a sample to be detected on a sample placing table, and moving a motor through a control platform to enable the motor to be positioned right below the optical fiber probe, and detecting a reflected bright light field spectrum and a reflected dark light field spectrum of the substrate;
S4, placing the substrate attached with the film to be tested on a sample placing table, moving a motor through a control platform to enable the motor to be located under the optical fiber probe, and subtracting the difference between the reflection light field spectrum of the substrate and the reflection dark light field spectrum of the substrate from the detected reflection spectrum to obtain the reflection spectrum of the film;
s5, after the acquisition of the reflection spectrum of the film is completed, the data processing unit automatically analyzes the reflection spectrum of the film and outputs a measurement result of the thickness of the film;
S6, controlling a platform moving motor to change the position of the sample placing table, and realizing multipoint measurement of the film thickness.
2. The method for online measurement of film thickness according to claim 1, wherein the convolutional neural network model training in step S13 is specifically performed by the following method:
By adopting an Adam optimization mode, taking 0.005 as a learning rate and cross entropy as a loss function, if the loss value is reduced in a plurality of iteration cycles, the learning rate is reduced by 10%.
3. The method for online measurement of film thickness according to claim 1, wherein in the step of constructing the convolutional neural network model in step S12, 50 matrices of 200 x 1 are input, the first one-dimensional convolution operation module is provided with 64 convolution kernels, the second one-dimensional convolution operation module is provided with 128 convolution kernels, flattening is performed after the convolution operation is completed, a matrix of 6400 x 1 is obtained, and finally the matrix is fully connected to a film thickness label.
4. The method for online measurement of film thickness according to claim 3, wherein the convolution kernel is set to 3x1, the step is set to 1, and the edge zero padding is set to 1; the pooling core in the first pooling layer and the second pooling layer is set to 3×1, and the stride is set to 2.
5. The method of claim 1, wherein the film thickness training step is 50nm.
6. The thin film thickness on-line measuring method according to claim 1, the thin film thickness measuring device further comprising a display and control unit; the display and control unit comprises a display screen and control buttons; the display screen is used for displaying the reflection spectrum of the sample and the measurement result of the film thickness; the control unit is used for setting sample parameters and measurement parameters.
7. The method for online measurement of film thickness according to claim 1, wherein the sample placement stage is further provided with an adjusting knob for adjusting an angle of the sample placement stage with respect to a horizontal plane so that the sample placement stage is horizontally placed; the halogen tungsten lamp light source is also connected with a light source adjusting knob; the light source adjusting knob is used for adjusting the light intensity of the light source of the halogen tungsten lamp.
8. The method for online measurement of film thickness according to claim 1, wherein the data processing unit is a microcomputer; the film thickness measuring network model is written by Python, and is imported into a microcomputer after training and verification are completed.
9. The method for generating the film thickness measurement network model is characterized by comprising the following steps of:
s11, generating a data set:
s111, according to the refractive indexes of materials of the film and the substrate, obtaining a reflectivity fitting function of the refractive indexes of the film and the substrate along with the change of wavelength through polynomial nonlinear fitting of mathematical analysis software;
s112, selecting a wave band with an extinction coefficient close to zero, and generating a film theoretical reflectivity spectrum with the thickness between 500nm and 10 mu m according to a reflectivity fitting function of the refractive indexes of the film and the substrate in the wave band, which is changed along with the wavelength, and a set film thickness training step length;
S113, adopting a Python built-in function, introducing normal distribution noise into a film theoretical reflectivity spectrum, generating a plurality of samples, and dividing the samples into a training set and a verification set;
s114, changing the materials of the film and the substrate, and repeating the steps S111-S113 to obtain data sets of the films and the substrates of different materials;
S12, building a convolutional neural network model:
The convolutional neural network model comprises a first convolutional activation layer, a first pooling layer, a second convolutional activation layer, a second pooling layer, a full-connection layer and a spreading layer which are sequentially connected; the first convolution activation layer comprises a first one-dimensional convolution operation module and a first nonlinear activation function module; the second convolution activation layer comprises a second one-dimensional convolution operation module and a second nonlinear activation function module; the convolution neural network model takes the reflectivity spectrum of the film as input and the thickness of the film as output;
S13, training a convolutional neural network model: adopting an Adam optimization mode, taking 0.005 as a learning rate, taking cross entropy as a loss function, and if the loss value is reduced in a plurality of iteration cycles, reducing the learning rate by 10%;
s14, convolutional neural network model verification.
10. A device for generating a film thickness measurement network model, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being configured to perform the steps of the method of claim 9.
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