CN117089817A - Optical film hybrid monitoring method based on Kalman filtering data fusion - Google Patents
Optical film hybrid monitoring method based on Kalman filtering data fusion Download PDFInfo
- Publication number
- CN117089817A CN117089817A CN202310781874.4A CN202310781874A CN117089817A CN 117089817 A CN117089817 A CN 117089817A CN 202310781874 A CN202310781874 A CN 202310781874A CN 117089817 A CN117089817 A CN 117089817A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- thickness
- film
- covariance matrix
- quartz crystal
- 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.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 155
- 238000000034 method Methods 0.000 title claims abstract description 92
- 239000012788 optical film Substances 0.000 title claims abstract description 37
- 230000004927 fusion Effects 0.000 title claims abstract description 30
- 238000001914 filtration Methods 0.000 title claims abstract description 23
- 239000010408 film Substances 0.000 claims abstract description 80
- 239000013078 crystal Substances 0.000 claims abstract description 46
- 239000010453 quartz Substances 0.000 claims abstract description 45
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims abstract description 45
- 230000008021 deposition Effects 0.000 claims abstract description 44
- 238000004364 calculation method Methods 0.000 claims abstract description 16
- 239000011159 matrix material Substances 0.000 claims description 62
- 238000005259 measurement Methods 0.000 claims description 22
- 238000001228 spectrum Methods 0.000 claims description 18
- 230000003287 optical effect Effects 0.000 claims description 12
- 230000010355 oscillation Effects 0.000 claims description 8
- 238000005137 deposition process Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000007747 plating Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 239000010409 thin film Substances 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 7
- 238000007500 overflow downdraw method Methods 0.000 abstract 1
- 238000000151 deposition Methods 0.000 description 30
- 230000000694 effects Effects 0.000 description 7
- 238000002360 preparation method Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 3
- 229910004298 SiO 2 Inorganic materials 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000007888 film coating Substances 0.000 description 1
- 238000009501 film coating Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C23—COATING 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
- C23C—COATING 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
- C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
- C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
- C23C14/54—Controlling or regulating the coating process
- C23C14/542—Controlling the film thickness or evaporation rate
- C23C14/545—Controlling the film thickness or evaporation rate using measurement on deposited material
- C23C14/546—Controlling the film thickness or evaporation rate using measurement on deposited material using crystal oscillators
-
- C—CHEMISTRY; METALLURGY
- C23—COATING 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
- C23C—COATING 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
- C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
- C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
- C23C16/52—Controlling or regulating the coating process
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Organic Chemistry (AREA)
- Metallurgy (AREA)
- Materials Engineering (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Software Systems (AREA)
- General Chemical & Material Sciences (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Operations Research (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses an optical film hybrid monitoring method based on Kalman filtering data fusion, which belongs to the technical field of optical film monitoring, wherein estimated values of film thickness and deposition speed are obtained through quartz crystal oscillator monitoring and a broad spectrum monitoring method in the optical film monitoring process, and then an optimal estimated value of film thickness is obtained through a data fusion method, and the optical film hybrid monitoring method comprises the following steps: 1) Setting a target thickness of a current film layer; 2) Carrying out data fusion by using a Kalman filter to obtain an optimal estimated value; 3) When the optimal estimated value reaches the target thickness, the deposition of the layer film is stopped. Compared with the calculation and manufacturing thickness errors under a single monitoring mode and a mixed monitoring method, the method can effectively improve the accuracy of monitoring the thickness of the optical film, and has important significance for preparing the high-performance optical film.
Description
Technical Field
The invention belongs to the technical field of optical film monitoring, and particularly relates to an optical film hybrid monitoring method based on Kalman filtering data fusion.
Background
The optical film is an optical device based on optical interference effect, has strong light intensity and phase modulation capability, and has wide application in the fields of laser devices, national defense industry, medical instruments, aerospace and the like. In the actual preparation process of the film, the optical performance of the film can be seriously affected by the film thickness error caused by the monitoring error, so that an accurate film monitoring method is a key for preparing the high-performance optical film. A number of high-precision thin film monitoring methods have been developed so far, with the mainstream methods including non-optical monitoring methods and optical monitoring methods.
The quartz crystal oscillator monitoring is a classical non-optical monitoring method, obvious uncorrelated random errors can occur in the monitoring process, and the magnitude of the errors increases along with the increase of the thickness of the film. The optical monitoring method mainly comprises single-wavelength monitoring and broad-spectrum monitoring. The single-wavelength monitoring method has the technical characteristics of simplicity and easiness, and has a certain error compensation effect in the monitoring process, but the monitoring accuracy is general. The wide spectrum monitoring method has higher monitoring precision and obvious error self-compensation effect, and is widely applied to the accurate preparation of high-performance optical films. However, the monitoring error in the wide spectrum monitoring process has strong correlation, obvious accumulation effect can appear in the monitoring error in the film plating process, and the monitoring precision is gradually reduced.
The existing main stream monitoring methods have different defects, and the different monitoring methods have certain complementarity. In view of the above-mentioned problems, there is an urgent need for a hybrid monitoring method that can combine the respective excellent characteristics of the non-optical monitoring method and the optical monitoring method in the accurate preparation of high-performance optical films. Therefore, the method performs data fusion on different monitoring methods based on Kalman filtering, and provides an accurate optical film hybrid monitoring method.
Disclosure of Invention
The invention aims to provide an optical film hybrid monitoring method based on Kalman filtering data fusion, which solves the problems of low monitoring precision and over-strong monitoring error correlation in the technology.
In order to achieve the above purpose, the invention provides an optical film hybrid monitoring method based on Kalman filtering data fusion, which comprises the following steps:
step 1, setting a target thickness of a current film layer;
step 2, designing a data fusion algorithm to obtain an optimal estimated value of the film thickness;
and 3, judging the moment of stopping deposition according to the optimal estimated value of the film thickness and the target thickness, and repeating the steps until the deposition of all the film layers is completed.
Preferably, the specific process of obtaining the optimal estimated value of the film thickness in the step 2 is as follows:
s21, establishing a state equation and a measurement equation of the optical film deposition process,
x k ＝Ax k-1 +Bu k +ω k-1
z k ＝Hx k +u k
wherein k is a time series, x k As state variable, z k To observe the variable ω k U is process noise k For observing noise, A is a state transition matrix, B is a control input matrix, H is a state observation matrix, where x is k The specific representation of A, B, H is as follows,
wherein d k For the thickness v of the film layer k For the deposition rate, Δt is the data measurement time interval, the observation variable z of quartz crystal oscillation monitoring k,1 The thickness data in the method is obtained from the observation variable z of quartz crystal oscillator monitoring and broad spectrum monitoring k,2 Number of thicknesses in (a)The deposition rate data is obtained from time difference calculation of the film thickness according to the data obtained from broad spectrum monitoring;
s22, designing a Kalman filter to obtain an optimal estimated value of the film thickness.
Preferably, the kalman filter includes a serial kalman filter and a parallel kalman filter;
the calculation process of the serial kalman filter is as follows,
(1) Performing data fusion on the quartz crystal oscillator monitoring and deposition model by using a basic Kalman filtering method, calculating a priori estimated covariance matrix and Kalman gain coefficient as,
in the method, in the process of the invention,prior estimation covariance matrix for quartz crystal oscillator monitoring, K k,1 Kalman gain coefficient, Q for quartz crystal oscillator monitoring 1 Process noise covariance matrix for quartz crystal oscillator monitoring, R 1 The measurement noise covariance matrix for quartz crystal oscillator monitoring is obtained by fusing the quartz crystal oscillator monitoring and the deposition model data, the posterior estimation covariance matrix is calculated,
wherein,optimal estimated value P for monitoring quartz crystal oscillator k,1 Estimating a covariance matrix for a posterior of quartz crystal oscillator monitoring;
(2) Optimal estimation using serial Kalman filterFusion with the data monitored over a broad spectrum,
K k,2 ＝P k,1 H T (HP k,1 H T +R 2 ) -1
P k ＝(I-K k,2 H)P k,1
wherein R is 2 Measurement noise covariance matrix for broad spectrum monitoring, K k,2 Kalman gain factor, P, for broad spectrum monitoring k Estimating a covariance matrix for the posterior;
the parallel Kalman filter is calculated as follows:
(a) A priori state estimation covariance matrix is calculated,
wherein,estimating covariance matrix for a priori state, P k-1 The posterior estimation covariance matrix at the previous moment, Q is a process noise covariance matrix;
(b) Integrating observation variables and measurement noise of quartz crystal oscillator monitoring and broad spectrum monitoring, updating a posterior estimation covariance matrix and a Kalman gain coefficient, fusing data,
in the observation variable z k,1 And z k,2 The thickness data of the film is respectively obtained from quartz crystal oscillation monitoring and broad spectrum monitoring, the deposition rate data is obtained from time difference calculation of the thickness of each film layer, R k,1 And R is k,2 The measurement noise covariance matrix is used for quartz crystal oscillation monitoring and broad spectrum monitoring.
Preferably, the specific process of determining the time of stopping the deposition in the step 3 is as follows: setting the time interval of thickness monitoring as deltat, the optimal estimated value of the film thickness at the moment t is d (t), the deposition speed is v, and the film thickness d (t+deltat) =d (t) +vdeltat at the moment t+deltat is calculated according to the deposition speed, when d (t) < d i,target And d (t+Δt) > d i,target At t 1 ＝(d i,target Stopping deposition after-d (t))/v, d i,target Is the target thickness.
Preferably, the specific process of calculating the target thickness of the current film layer in the step 1 is as follows:
s11, constructing an objective function F,
in the broad spectrum monitoring process, the monitoring function of the ith film is that
Wherein d i,theo And T theo For the theoretical thickness and theoretical spectrum of the ith film, d i,act And T meas For the actual thickness of the i-th layer film and the actual measured spectrum, when Φ reaches the minimum value, the film layer is stopped from being deposited, and the film layer thickness at this time is obtained in advance by numerical calculation, specifically as follows，
Wherein T (d) 1,act ,d 2,act ,...,d i,target Lambda) is the spectrum calculated from the optical film feature matrix;
s12, solving the minimum value of the objective function by using an optimization method, wherein the thickness d of the film layer is at the moment i,target Is the target thickness of the i-th layer.
Preferably, the value of the process noise covariance matrix Q is set according to the magnitude of standard deviation of the thickness of the film layer and the deposition rate in the film plating process.
Preferably, the value of the measurement noise covariance matrix R is set according to the magnitude of the standard deviation of the monitoring inversion film thickness and deposition rate.
Therefore, the optical film hybrid monitoring method based on Kalman filtering data fusion has the following beneficial effects: the method fully combines the error independent characteristic of the non-optical monitoring method and the high-precision and error self-compensation effect of wide spectrum monitoring, realizes the high-precision monitoring of the optical film, and has important significance for the accurate preparation of the high-performance optical film.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a flow chart of an optical film hybrid monitoring method based on Kalman filtering data fusion;
FIG. 2 is a serial Kalman filtering flow chart for data fusion in the present invention;
FIG. 3 is a parallel Kalman filtering flow chart for data fusion in the present invention;
FIG. 4 is a computational manufacturing flow chart for use in the present invention;
fig. 5 is a graph comparing film thickness monitoring errors for specific examples.
Detailed Description
Examples
The following detailed description of the embodiments of the invention, provided in the accompanying drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, an optical film hybrid monitoring method based on kalman filter data fusion includes the following steps:
step 1, setting the target thickness of the current film layer, wherein the specific process is as follows:
s11, constructing an objective function F,
in the broad spectrum monitoring process, the monitoring function of the ith film is that
Wherein d i,theo And T theo For the theoretical thickness and theoretical spectrum of the ith film, d i,act And T meas For the actual thickness of the i-th film and the actual measured spectrum, when Φ reaches the minimum value, the film layer stops depositing, at which time the film layer thickness is obtained in advance by numerical calculation, concretely as follows,
wherein T (d) 1,act ,d 2,act ,...,d i,target Lambda) is the spectrum calculated from the optical film feature matrix;
s12, solving the minimum value of the objective function by using an optimization method, wherein the thickness d of the film layer is at the moment i,target Is the target thickness of the i-th layer;
step 2, designing a data fusion algorithm to obtain an optimal estimated value of the film thickness, wherein the method comprises the following specific processes:
s21, establishing a state equation and a measurement equation of the optical film deposition process,
x k ＝Ax k-1 +Bu k +ω k-1
z k ＝Hx k +u k
wherein k is a time series, x k As state variable, z k To observe the variable ω k U is process noise k For observing noise, A is a state transition matrix, B is a control input matrix, H is a state observation matrix, where x is k The specific representation of A, B, H is as follows,
wherein d k For the thickness v of the film layer k For the deposition rate, Δt is the data measurement time interval, the observation variable z of quartz crystal oscillation monitoring k,1 The thickness data in the method is obtained from the observation variable z of quartz crystal oscillator monitoring and broad spectrum monitoring k,2 The thickness data of the film layer is obtained from wide spectrum monitoring, and the deposition rate data is obtained from time difference calculation of the film layer thickness;
s22, designing a Kalman filter to obtain an optimal estimated value of the film thickness, wherein the Kalman filter comprises a serial Kalman filter and a parallel Kalman filter;
the calculation of the serial kalman filter is as follows,
(1) Performing data fusion on the quartz crystal oscillator monitoring and deposition model by using a basic Kalman filtering method, calculating a priori estimated covariance matrix and Kalman gain coefficient as,
in the method, in the process of the invention,prior estimation covariance matrix for quartz crystal oscillator monitoring, K k,1 Kalman gain coefficient, Q for quartz crystal oscillator monitoring 1 Process noise covariance matrix for quartz crystal oscillator monitoring, R 1 The measurement noise covariance matrix for quartz crystal oscillator monitoring is obtained by fusing the quartz crystal oscillator monitoring and the deposition model data, the posterior estimation covariance matrix is calculated,
wherein,optimal estimated value P for monitoring quartz crystal oscillator k,1 Estimating a covariance matrix for a posterior of quartz crystal oscillator monitoring;
(2) Optimal estimation using serial Kalman filterFusion with the data monitored over a broad spectrum,
K k,2 ＝P k,1 H T (HP k,1 H T +R 2 ) -1
P k ＝(I-K k,2 H)P k,1
wherein R is 2 Measurement noise covariance matrix for broad spectrum monitoring, K k,2 Kalman gain factor, P, for broad spectrum monitoring k Estimating a covariance matrix for the posterior;
the parallel Kalman filter is calculated as follows:
(a) A priori state estimation covariance matrix is calculated,
wherein,estimating covariance matrix for a priori state, P k-1 The posterior estimation covariance matrix at the previous moment, Q is a process noise covariance matrix;
(b) Integrating observation variables and measurement noise of quartz crystal oscillator monitoring and broad spectrum monitoring, updating a posterior estimation covariance matrix and a Kalman gain coefficient, fusing data,
in the observation variable z k,1 And z k,2 The thickness data of the film is respectively obtained from quartz crystal oscillation monitoring and broad spectrum monitoring, the deposition rate data is obtained from time difference calculation of the thickness of each film layer, R k,1 And R is k,2 Measuring noise covariance matrixes for quartz crystal oscillator monitoring and broad spectrum monitoring;
and 3, judging the moment of stopping deposition according to the optimal estimated value and the target thickness of the film layer, repeating the steps until the deposition of all the film layers is completed, and judging the moment of stopping deposition as follows: let the time interval of thickness monitoring be Deltat, the optimal estimated value of the film thickness at the time t be d (t), the deposition speed bev, calculating the film thickness d (t+Δt) =d (t) +vΔt at the time t+Δt according to the deposition speed, and when d (t) < d i,target And d (t+Δt) > d i,target At t 1 ＝(d i,target Stopping deposition after-d (t))/v, d i,target Is the target thickness.
The value of the process noise covariance matrix Q is set according to the magnitude of standard deviation of the film thickness and deposition rate of the film coating process. The value of the measurement noise covariance matrix R is set according to the magnitude of the standard deviation of the monitoring inversion film thickness and deposition rate.
The effect of the hybrid monitoring method is verified by adopting a calculation and manufacturing means. The flow of computational manufacturing is shown in fig. 4. In computational manufacturing, the actual film thickness is generated from the deposition rate, then the measured thickness and the rate of crystal oscillator monitoring containing random errors are generated, and at the same time, the measured spectrum containing random errors is generated, and the inversion algorithm is used to calculate the measured thickness of broad spectrum monitoring. And carrying out data fusion on the thicknesses obtained in various monitoring modes by using a Kalman filter, and finally obtaining the optimal estimated value of the thickness. And then, circulating and judging until all the film layers are deposited.
84 layers of film systems are selected for calculation and manufacture, and the coating material is selected from high refractive index material Ta 2 O 5 And a low refractive index material SiO 2 . The uncertainty of quartz crystal oscillator monitoring is 2%. The deposition rate was 0.3nm/s, the deposition rate uncertainty was 0.015nm/s, and the correlation time was 3s. The monitoring wave band of the wide spectrum monitoring is 500-1200nm, the random error of the spectrum measurement is 1%, the baseline drift amount is 0.5%, and the online characterization algorithm is an S algorithm. And synchronously reading data of the two monitoring modes in the calculation and manufacture, wherein the data sampling time interval is 3s.
The value of the covariance matrix in the example is set according to the estimated value of the monitoring precision. Q (1, 1) is set to 0 and Q (2, 2) is set to 0.0001 in the process noise covariance matrix; r for monitoring quartz crystal oscillator in measurement noise covariance matrix 1 (1, 1) is set to 3% of the thickness measured by quartz crystal oscillator, R 1 (2, 2) set to 0.005, R for broad spectrum monitoring 2 (1, 1) setting to a power function of the number i of currently deposited filmsNumber, R 2 (2, 2) was set to 0.005.
The comparison of the monitoring errors of the hybrid monitoring method and the single monitoring method is shown in fig. 5. The standard deviation of the monitoring error of the hybrid monitoring method based on Kalman filtering data fusion is 0.277nm, and the maximum monitoring error is less than 0.7nm. The standard deviations of quartz crystal vibration monitoring and broad spectrum monitoring are 1.979nm and 0.4699nm respectively, and a plurality of film layers with monitoring errors larger than 1nm exist. The comparison result shows that the hybrid monitoring method after data fusion has higher precision.
Therefore, the optical film hybrid monitoring method based on Kalman filtering data fusion fully combines the error independent characteristic of a non-optical monitoring method and the high-precision and error self-compensation effect of wide-spectrum monitoring, realizes the high-precision monitoring of the optical film, and has important significance for the accurate preparation of the high-performance optical film.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (7)
1. The optical film hybrid monitoring method based on Kalman filtering data fusion is characterized by comprising the following steps of:
step 1, setting a target thickness of a current film layer;
step 2, designing a data fusion algorithm to obtain an optimal estimated value of the film thickness;
and 3, judging the moment of stopping deposition according to the optimal estimated value of the film thickness and the target thickness, and repeating the steps until the deposition of all the film layers is completed.
2. The optical film hybrid monitoring method based on Kalman filtering data fusion according to claim 1, wherein the specific process of obtaining the optimal estimated value of the film thickness in the step 2 is as follows:
s21, establishing a state equation and a measurement equation of the optical film deposition process,
x k ＝Ax k-1 +Bu k +ω k-1
z k ＝Hx k +u k
wherein k is a time series, x k As state variable, z k To observe the variable ω k U is process noise k For observing noise, A is a state transition matrix, B is a control input matrix, H is a state observation matrix, where x is k The concrete representation of A, B, H is as follows
Wherein d k For the thickness v of the film layer k For the deposition rate, Δt is the data measurement time interval, the observation variable z of quartz crystal oscillation monitoring k,1 The thickness data in the method is obtained from the observation variable z of quartz crystal oscillator monitoring and broad spectrum monitoring k,2 The thickness data of the film layer is obtained from wide spectrum monitoring, and the deposition rate data is obtained from time difference calculation of the film layer thickness;
s22, designing a Kalman filter to obtain an optimal estimated value of the film thickness.
3. The optical film hybrid monitoring method based on Kalman filtering data fusion according to claim 2, wherein the method is characterized by comprising the following steps: the Kalman filter comprises a serial Kalman filter and a parallel Kalman filter;
the calculation process of the serial kalman filter is as follows,
(1) Performing data fusion on the quartz crystal oscillator monitoring and deposition model by using a basic Kalman filtering method, calculating a priori estimated covariance matrix and Kalman gain coefficient as,
in the method, in the process of the invention,prior estimation covariance matrix for quartz crystal oscillator monitoring, K k,1 Kalman gain coefficient, Q for quartz crystal oscillator monitoring 1 Process noise covariance matrix for quartz crystal oscillator monitoring, R 1 The measurement noise covariance matrix for quartz crystal oscillator monitoring is obtained by fusing the quartz crystal oscillator monitoring and the deposition model data, the posterior estimation covariance matrix is calculated,
wherein,optimal estimated value P for monitoring quartz crystal oscillator k,1 Estimating a covariance matrix for a posterior of quartz crystal oscillator monitoring;
(2) Optimal estimation using serial Kalman filterFusion with the data monitored over a broad spectrum,
K k,2 ＝P k,1 H T (HP k,1 H T +R 2 ) -1
P k ＝(I-K k,2 H)P k,1
wherein R is 2 Measurement noise covariance matrix for broad spectrum monitoring, K k,2 Kalman gain factor, P, for broad spectrum monitoring k Estimating a covariance matrix for the posterior;
the parallel kalman filter is calculated as follows,
(a) A priori state estimation covariance matrix is calculated,
wherein,estimating covariance matrix for a priori state, P k-1 The posterior estimation covariance matrix at the previous moment, Q is a process noise covariance matrix;
(b) Integrating observation variables and measurement noise of quartz crystal oscillator monitoring and broad spectrum monitoring, updating a posterior estimation covariance matrix and a Kalman gain coefficient, fusing data,
in the observation variable z k,1 And z k,2 The thickness data of the film is respectively obtained from quartz crystal oscillation monitoring and broad spectrum monitoring, the deposition rate data is obtained from time difference calculation of the thickness of each film layer, R k,1 And R is k,2 The measurement noise covariance matrix is used for quartz crystal oscillation monitoring and broad spectrum monitoring.
4. The optical thin film hybrid monitoring method based on Kalman filtering data fusion according to claim 1, wherein the specific process of determining the time of stopping deposition in the step 3 is as follows: setting the time interval of thickness monitoring as deltat, the optimal estimated value of the film thickness at the moment t is d (t), the deposition speed is v, and the film thickness d (t+deltat) =d (t) +vdeltat at the moment t+deltat is calculated according to the deposition speed, when d (t) < d i,target And d (t+Δt) > d i,target At t 1 ＝(d i,target Stopping deposition after-d (t))/v, d i,target Is the target thickness.
5. The optical film hybrid monitoring method based on Kalman filtering data fusion according to claim 1, wherein the specific process of calculating the target thickness of the current film layer in the step 1 is as follows:
s11, constructing an objective function F,
in the broad spectrum monitoring process, the monitoring function of the ith film is that
Wherein d i,theo And T theo For the theoretical thickness and theoretical spectrum of the ith film, d i,act And T meas For the actual thickness of the i-th layer film and the actual measured spectrum, when Φ reaches the minimum value, the film layer is stopped from being deposited, and the film layer thickness at this time is obtained in advance by numerical calculation, specifically as follows
Wherein T (d) 1,act ,d 2,act ,...,d i,target Lambda) is the spectrum calculated from the optical film feature matrix;
s12, solving the minimum value of the objective function by using an optimization method, wherein the thickness d of the film layer is at the moment i,target Is the target thickness of the i-th layer.
6. The optical film hybrid monitoring method based on Kalman filtering data fusion according to claim 3, wherein the method is characterized by comprising the following steps: the numerical value of the process noise covariance matrix Q is set according to the magnitude of standard deviation of the thickness of a film layer and the deposition rate in the film plating process.
7. The optical film hybrid monitoring method based on Kalman filtering data fusion according to claim 1, wherein the method is characterized by comprising the following steps: the value of the measurement noise covariance matrix R is set according to the magnitude of the standard deviation of the monitoring inversion film thickness and the deposition rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310781874.4A CN117089817B (en) | 2023-06-29 | 2023-06-29 | Optical film hybrid monitoring method based on Kalman filtering data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310781874.4A CN117089817B (en) | 2023-06-29 | 2023-06-29 | Optical film hybrid monitoring method based on Kalman filtering data fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117089817A true CN117089817A (en) | 2023-11-21 |
| CN117089817B CN117089817B (en) | 2024-02-13 |
Family
ID=88768684
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310781874.4A Active CN117089817B (en) | 2023-06-29 | 2023-06-29 | Optical film hybrid monitoring method based on Kalman filtering data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117089817B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62117321A (en) * | 1985-11-18 | 1987-05-28 | Hitachi Ltd | Controller for thickness of coating film by karman-filter |
| US4931982A (en) * | 1987-07-17 | 1990-06-05 | Toray Industries, Inc. | Method for controlling the thickness of a sheet material and method for monitoring a correspondence relationship between the thickness distribution across a sheet material and means for adjusting the thickness |
| US6290572B1 (en) * | 2000-03-23 | 2001-09-18 | Micron Technology, Inc. | Devices and methods for in-situ control of mechanical or chemical-mechanical planarization of microelectronic-device substrate assemblies |
| US20030014147A1 (en) * | 2001-07-13 | 2003-01-16 | Mario Ignagni | Data fusion of stationary array sensor and scanning sensor measurements |
| JP2003029026A (en) * | 2001-07-18 | 2003-01-29 | Alps Electric Co Ltd | Device for deposition of optical thin film, method for depositing film and optical filter |
| CN1696336A (en) * | 2005-06-03 | 2005-11-16 | 中国科学院上海光学精密机械研究所 | Computer controlled filming device |
| CN2911757Y (en) * | 2006-05-09 | 2007-06-13 | 北京奥博泰科技有限公司 | Intelligent optical membrane full automatic monitoring system |
| JP2010138463A (en) * | 2008-12-12 | 2010-06-24 | K2 Technology Kk | Method for forming optical thin film |
| CN103540906A (en) * | 2013-09-29 | 2014-01-29 | 中国科学院上海光学精密机械研究所 | Optical/crystal comprehensive film thickness monitoring method |
| CN109816810A (en) * | 2018-12-29 | 2019-05-28 | 西南技术物理研究所 | A kind of optical film layer monitoring method based on digital filtering |
| CN111611977A (en) * | 2020-06-05 | 2020-09-01 | 吉林求是光谱数据科技有限公司 | Face recognition monitoring system and recognition method based on spectrum and multiband fusion |
| CN115807219A (en) * | 2023-02-13 | 2023-03-17 | 南昌大学 | Photoelectric thin film material preparation control system and method |
| CN116336951A (en) * | 2023-04-26 | 2023-06-27 | 湖南科技大学 | High-precision slag thickness detection method and device based on Kalman filtering |
-
2023
- 2023-06-29 CN CN202310781874.4A patent/CN117089817B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62117321A (en) * | 1985-11-18 | 1987-05-28 | Hitachi Ltd | Controller for thickness of coating film by karman-filter |
| US4931982A (en) * | 1987-07-17 | 1990-06-05 | Toray Industries, Inc. | Method for controlling the thickness of a sheet material and method for monitoring a correspondence relationship between the thickness distribution across a sheet material and means for adjusting the thickness |
| US6290572B1 (en) * | 2000-03-23 | 2001-09-18 | Micron Technology, Inc. | Devices and methods for in-situ control of mechanical or chemical-mechanical planarization of microelectronic-device substrate assemblies |
| US20030014147A1 (en) * | 2001-07-13 | 2003-01-16 | Mario Ignagni | Data fusion of stationary array sensor and scanning sensor measurements |
| JP2003029026A (en) * | 2001-07-18 | 2003-01-29 | Alps Electric Co Ltd | Device for deposition of optical thin film, method for depositing film and optical filter |
| CN1696336A (en) * | 2005-06-03 | 2005-11-16 | 中国科学院上海光学精密机械研究所 | Computer controlled filming device |
| CN2911757Y (en) * | 2006-05-09 | 2007-06-13 | 北京奥博泰科技有限公司 | Intelligent optical membrane full automatic monitoring system |
| JP2010138463A (en) * | 2008-12-12 | 2010-06-24 | K2 Technology Kk | Method for forming optical thin film |
| CN103540906A (en) * | 2013-09-29 | 2014-01-29 | 中国科学院上海光学精密机械研究所 | Optical/crystal comprehensive film thickness monitoring method |
| CN109816810A (en) * | 2018-12-29 | 2019-05-28 | 西南技术物理研究所 | A kind of optical film layer monitoring method based on digital filtering |
| CN111611977A (en) * | 2020-06-05 | 2020-09-01 | 吉林求是光谱数据科技有限公司 | Face recognition monitoring system and recognition method based on spectrum and multiband fusion |
| CN115807219A (en) * | 2023-02-13 | 2023-03-17 | 南昌大学 | Photoelectric thin film material preparation control system and method |
| CN116336951A (en) * | 2023-04-26 | 2023-06-27 | 湖南科技大学 | High-precision slag thickness detection method and device based on Kalman filtering |
Non-Patent Citations (8)
| Title |
|---|
| M.NIXON ET AL.: ""DIGITAL SIGNAL-PROCESSING OF OPTICAL MONITORING BY MEANS OF A KALMAN FILTER"", 《PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS》, pages 93 - 99 * |
| 任豪;王巧彬;罗宇强;李康业;: "复合光路光学镀膜宽光谱膜厚监控系统", 激光与光电子学进展, no. 05, pages 139 - 143 * |
| 李华;: "卡尔曼滤波器在热轧GM厚度控制中的应用研究", 自动化与仪器仪表, no. 10, pages 26 - 28 * |
| 杨春华;夏文建;李蓬;余文峰;程祖海;: "用复合监控法镀制的TEA CO_2激光器增透膜", 光学与光电技术, no. 01, pages 78 - 81 * |
| 殷浩;应雄纯;宗杰;: "光学膜厚监控方法", 红外与激光工程, no. 04, pages 164 - 168 * |
| 潘洪昌;张豪杰;张甫光;江绍基;: "数字滤波在光学镀膜宽光谱监控中的应用", 光学仪器, no. 04, pages 70 - 74 * |
| 赵汝进;马孜;姚远程;何长涛;: "基于广义卡尔曼滤波的光学膜厚监控信号处理", 激光技术, no. 04, pages 78 - 81 * |
| 赵汝进;马孜;姚远程;何长涛;: "基于自适应卡尔曼滤波的光学监控信号处理", 光学仪器, no. 04, pages 83 - 87 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117089817B (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102620664B (en) | Method for detecting coating-thickness uniformity of optical coating machine | |
| CN106093810B (en) | Method for testing electromagnetic parameters of material and method for solving multivalue problem in electromagnetic parameter testing of material | |
| CN102519489B (en) | Fiber optic gyro (FOG) scale factor modelling method based on temperatures and input angular rates | |
| CN109508510B (en) | Improved Kalman filtering-based rubidium atomic clock parameter estimation algorithm | |
| CN103534553B (en) | The method of plural layers deposition is determined on piezo-electric crystal | |
| CN110098875B (en) | Adaptive equalization method, apparatus, electronic device and medium in optical fiber communication system | |
| CN117089817B (en) | Optical film hybrid monitoring method based on Kalman filtering data fusion | |
| CN109067381B (en) | Real-time filtering system and method for random noise of MEMS gyroscope | |
| US7442589B2 (en) | System and method for uniform multi-plane silicon oxide layer formation for optical applications | |
| US20230236007A1 (en) | Method for measuring thickness and optical constants of diamond film | |
| Tikhonravov et al. | Online characterization and reoptimization of optical coatings | |
| CN116776094B (en) | Crystal oscillator temperature test data intelligent analysis storage system | |
| CN116627028B (en) | Control method for crosslinked cable production line | |
| CN112697215B (en) | Kalman filtering parameter debugging method for ultrasonic water meter data filtering | |
| Piegari et al. | Analysis of inhomogeneous thin films by spectrophotometric measurements | |
| JP2002267835A (en) | Deciding method for refractive index dispersion and deciding method for refractive index distribution | |
| Tikhonravov et al. | Real-time characterization and optimization of e-beam evaporated optical coatings | |
| CN114136896B (en) | Experimental method for optical film optical constant process correlation | |
| CN113640115B (en) | Optimization method and system suitable for solving inverse problem of quasi-isentropic compression experimental data | |
| Linkens et al. | Modeling and fabrication of optical interference rugate filters | |
| Aliev et al. | Optimization of parameters of integrated optical waveguides based on thin-film Sol-gel structures by using mathematical modelling | |
| Tikhonravov | Virtual deposition plant | |
| RU2717359C1 (en) | Method of profiling composition at epitaxial formation of semiconductor structure based on solid solutions | |
| Kildemo et al. | Real-time control of the deposition of optical coatings by multiwavelength ellipsometry | |
| RU2246121C1 (en) | Method for evaluating parameters of radio radiation source in two-positional angle meter radiolocation system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |