Method and device for determining parameters of film forward modeling model and electronic equipment

Abstract

The invention discloses a parameter determination method and device for a film forward modeling model and electronic equipment, and the method comprises the steps: carrying out the depolarization correction of an initial Stokes vector expression of a film forward modeling model function based on a parameter adjustment factor, and obtaining a target Stokes vector expression, the parameter adjustment factors comprise a wavelength adjustment factor, a thickness adjustment factor and / or an angle adjustment factor; respectively determining a Gaussian node sequence value and a node weight value according to a preset Gaussian node number; forward modeling is carried out based on the target film forward modeling model function containing the target stokes vector expression, and a forward modeling spectrum is obtained; determining a parameter adjustment factor in a forward modeling model function of the target film as a target parameter adjustment factor when a mean square error value of the forward modeling spectrum and the measurement spectrum accords with a preset deviation range; and the accuracy and the reliability of the to-be-solved parameters extracted by the thin film are improved.

Description

Technical Field

[0001] This invention relates to the field of optical nanoscale precision measurement technology, specifically to a method, apparatus, electronic device, and computer-readable storage for determining parameters of a thin film forward modeling model. Background Technology

[0002] In the thin-film nanometry industry, thickness measurement of unknown novel materials accounts for an increasingly large proportion. Thin-film nanoellipsometry based on optical scattering measurement is essentially a model-based measurement method. First, a film thickness measurement device is used to acquire the measurement spectrum. Then, a thin-film morphology model is established. Finally, the morphology model is used to fit and match the measurement spectrum, thereby extracting the parameters to be determined from the model, including thickness. Ideally, when acquiring ellipsometry spectra using a film thickness measurement device, the incident light, after reflection or transmission from the sample surface, remains fully polarized, i.e., without depolarization effect.

[0003] However, in actual ellipsometric spectroscopy measurements, polarization state degradation occurs due to factors such as optical anisotropy, surface roughness, and incomplete thickness uniformity in certain materials. This results in depolarization effects in the ellipsometric spectra acquired by the thickness measurement equipment. Consequently, the final parameters extracted by fitting the measurement spectrum using LM nonlinear regression have significant errors. Conventional depolarization methods only reflect angle, wavelength, and thickness depolarization in one dimension during forward modeling, and the depolarization points are symmetrically fixed. However, the ellipsometric spectra measured by actual thin-film nanometer measurement equipment sometimes only contain depolarization information corresponding to some Gaussian nodes, leading to significant errors in the results of thin-film forward modeling.

[0004] Therefore, existing technologies suffer from low accuracy and reliability when performing forward modeling of thin films. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for depolarization detection and elimination based on a thin film morphology model, aiming to solve the problems of low accuracy and reliability in the prior art when performing forward modeling of thin films.

[0006] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: A method for determining parameters of a thin film forward modeling model, comprising: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling model function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and the initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. The Gaussian node sequence value and the node weight value are determined according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum; The parameter adjustment factor in the target thin film forward modeling model function is the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum is within a preset deviation range.

[0007] Optionally, the node weight values ​​include wavelength node weight values, thickness node weight values, and angle node weight values; the debiasing correction of the initial Stokes vector expression of the thin film forward modeling function based on the parameter adjustment factor to obtain the target Stokes vector expression includes: The wavelength node weight values ​​in the initial Stokes vector expression are adjusted according to the wavelength adjustment factor to obtain the target wavelength node weight values. And / or, The thickness node weight values ​​in the initial Stokes vector expression are adjusted according to the thickness adjustment factor to obtain the target thickness node weight values; And / or, The angle node weight values ​​in the initial Stokes vector expression are adjusted according to the angle adjustment factor to obtain the target angle node weight values; The target Stokes vector expression is determined based on the target wavelength node weight value, the target thickness node weight value, the target angle node weight value, and the initial Stokes vector expression.

[0008] Optionally, the initial Stokes vector expression is:

[0009] in, As the initial Stokes vector, The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. This is the thickness deflection parameter.

[0010] Optionally, the target Stokes vector expression is:

[0011] in, Let Stokes vector be the target vector. The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. For thickness deflection parameters, Angle adjustment factor, For wavelength adjustment factor, This is the thickness adjustment factor.

[0012] Optionally, the step of performing forward modeling based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum includes: The target Stokes vector is input into the forward modeling function of the target thin film to obtain the forward modeling spectrum.

[0013] Optionally, the parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets a preset deviation range is a target parameter adjustment factor, including: Obtain the mean square error value between the forward modeling spectrum and the measured spectrum; The parameter adjustment factor in the target thin film forward modeling function corresponding to the minimum value among all mean square error values ​​is taken as the target parameter adjustment factor; The target parameter adjustment factors include target wavelength adjustment factor, target thickness adjustment factor and / or target angle adjustment factor.

[0014] Optionally, after determining the target parameter adjustment factor, the method further includes: When the target parameter adjustment factor is the target wavelength adjustment factor and its value is zero, it is determined that there is no wavelength de-polarization at the point corresponding to the target parameter adjustment factor. When the target parameter adjustment factor is the target thickness adjustment factor and its value is zero, it is determined that there is no thickness deviation at the point corresponding to the target parameter adjustment factor. When the target parameter adjustment factor is the target angle adjustment factor and its value is zero, it is determined that the point corresponding to the target parameter adjustment factor does not have an angle back-off.

[0015] A parameter determination device for a thin film forward modeling model, comprising: The target Stokes vector expression determination module is used to perform debiasing correction on the initial Stokes vector expression of the thin film forward modeling model function based on the parameter adjustment factor to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factors and the initial Stokes vector expression. The parameter adjustment factors include wavelength adjustment factors, thickness adjustment factors and / or angle adjustment factors. The Gaussian processing module is used to determine the Gaussian node sequence value and the node weight value according to the preset number of Gaussian nodes; The forward modeling module is used to perform forward modeling based on the target thin film forward modeling model function containing the target Stokes vector expression, and obtain the forward modeling spectrum; The target parameter adjustment factor determination module is used to determine the parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

[0016] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling model function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and the initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. The Gaussian node sequence value and the node weight value are determined according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum; The parameter adjustment factor in the target thin film forward modeling model function is the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum is within a preset deviation range.

[0017] A computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the method for determining the parameters of the thin film forward model described above.

[0018] In this embodiment of the invention, the initial Stokes vector expression is debiased by parameter adjustment factors covering key variables such as wavelength, thickness, and angle, so that the target Stokes vector expression can more accurately reflect the polarization characteristics of the thin film under different conditions. The Gaussian node sequence value and node weight value are determined by presetting the number of Gaussian nodes, which further improves the modeling accuracy of the target thin film forward modeling model function. Finally, the target parameter adjustment factor is determined by mean square error optimization, so that the deviation between the forward modeling spectrum and the measurement spectrum is within a preset range, thereby effectively improving the accuracy and reliability of the parameters to be determined extracted from the thin film. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic diagram of a scenario for an embodiment of the depolarization detection system based on a thin film morphology model provided by the present invention; Figure 2 A schematic diagram of another embodiment of the depolarization detection system based on thin film morphology model provided by the present invention; Figure 3 A flowchart illustrating an embodiment of the method for determining parameters of a thin film forward modeling model provided by the present invention; Figure 4 A schematic diagram of an embodiment of the depolarization detection device based on a thin film morphology model provided by the present invention; Figure 5 This is a schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0023] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0024] This invention provides a method, apparatus, and electronic device for depolarization detection and elimination based on a thin film morphology model.

[0025] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an embodiment of the depolarization detection system based on a thin film morphology model provided by the present invention. The system may include a client 100 and a server 200, which are connected via a network. The server 200 integrates a depolarization detection device based on the thin film morphology model. The server 200 may be a work platform server (i.e., a server loaded with a work platform), such as... Figure 1In this embodiment of the invention, server 200 is mainly used to perform depolarization correction on the Stokes vector of the initial thin film morphology model based on Gaussian nodes and node weights to obtain the target Stokes vector; acquire the measurement spectral data of the sample to be tested; and perform depolarization detection on the measurement spectral data based on the target thin film morphology model to obtain the depolarization detection result of the sample to be tested; wherein, the target Stokes vector can be obtained after the target thin film morphology model is forward modeled.

[0026] In this embodiment of the invention, the server 200 can be a standalone server, a server network, or a server cluster. For example, the server 200 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment of the invention, communication between the server and the client can be achieved through any communication method, including but not limited to, mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0027] It is understood that the client 100 used in this embodiment of the invention can be understood as a client device. A client device includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a client device may include cellular or other communication devices, having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the client 100 may be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet computer, laptop computer, etc.

[0028] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1The number of more or fewer servers shown, or the server network connectivity relationships, for example... Figure 1 Only one server and two clients are shown in the diagram. It is understood that the depolarization detection system based on the thin film morphology model may also include one or more other servers, and / or one or more clients connected to the server network. The specifics are not limited here.

[0029] In some embodiments of the present invention, the working platform may be an enterprise office platform, such as WeChat for Business. Taking server 200 as an example, it may further include an enterprise office platform contact server, an enterprise office platform configuration management server, and a web management server. Enterprise users or developers can access the web management server using a web browser terminal to configure the field configuration information on the enterprise office platform configuration management server, and set and store the enterprise user information of enterprise employees of the enterprise office platform on the enterprise office platform contact server.

[0030] In addition, such as Figure 2 As shown, Figure 2 This is a schematic diagram of another embodiment of the depolarization detection system based on a thin film morphology model provided by the present invention. The depolarization detection system based on a thin film morphology model may further include a storage terminal 300 for storing data, such as a storage object database. The object database stores object data, which may include application templates (such as approval templates, attendance templates, and other application templates), file data (such as Word files, Excel files, or PPT files, and other files in various formats), image data (such as images in various formats such as jpg, png, and bmp), and other types of data. Correspondingly, the object database may also be divided into multiple types of data, such as an application database, a file database, or an image database.

[0031] It should be noted that, Figure 1-2 The schematic diagram of the depolarization detection system based on thin film morphology model shown is merely an example. The depolarization detection system and scenario based on thin film morphology model described in this embodiment are for the purpose of more clearly illustrating the technical solution of this embodiment and do not constitute a limitation on the technical solution provided by this invention. As those skilled in the art will know, with the evolution of depolarization detection systems based on thin film morphology model and the emergence of new business scenarios, the technical solution provided by this invention is also applicable to similar technical problems.

[0032] The following detailed description is based on specific embodiments.

[0033] In this embodiment, the description will be based on a depolarization detection device based on a thin film morphology model, which can be integrated into the server 200.

[0034] This invention provides a method, apparatus, electronic device, and computer-readable storage for determining parameters of a thin film forward modeling model. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 A flowchart illustrating an embodiment of the method for determining parameters of a thin film forward modeling model provided by the present invention includes: S301: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. It should be noted that the thin film forward modeling function is the core function for generating forward ellipsometric spectra based on theoretical models in the field of thin film nanometry.

[0035] S302: Determine the Gaussian node sequence value and node weight value according to the preset number of Gaussian nodes; It should be noted that, given the number of Gaussian nodes n, the Gaussian node sequence values ​​gaussx_1_value, gaussx_2_value, ..., gaussx_n_value can be directly calculated from the number of Gaussian nodes. Substituting the Gaussian node sequence into the Gaussian function, the node weight values ​​gaussy_1_value, gaussy_2_value, ..., gaussy_n_value are calculated. That is, given the number of Gaussian nodes n, the Gaussian node sequence values ​​and node weight values ​​are uniquely determined.

[0036] In addition, the distribution of Gaussian nodes is symmetric about the y-axis.

[0037] The preset number of Gaussian nodes is a pre-set value, usually 9, but other values ​​can be used as needed, which are not limited here.

[0038] S303: Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum; S304: The parameter adjustment factor in the target thin film forward modeling model function is determined to be the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

[0039] In this embodiment, the initial Stokes vector expression is debiased by adjusting parameters covering key variables such as wavelength, thickness, and angle, so that the target Stokes vector expression can more accurately reflect the polarization characteristics of the thin film under different conditions. The Gaussian node sequence value and node weight value are determined by setting the number of Gaussian nodes, which further improves the modeling accuracy of the target thin film forward modeling model function. Finally, the target parameter adjustment factor is determined by mean square error optimization, so that the deviation between the forward modeling spectrum and the measured spectrum is within the preset range, thereby effectively improving the accuracy and reliability of the parameters to be determined extracted from the thin film.

[0040] In one specific embodiment, in S301, the node weight values ​​include wavelength node weight values, thickness node weight values, and angle node weight values; the initial Stokes vector expression of the thin film forward modeling function is debiased based on a parameter adjustment factor to obtain a target Stokes vector expression, including: adjusting the wavelength node weight values ​​in the initial Stokes vector expression according to the wavelength adjustment factor to obtain a target wavelength node weight value; and / or, adjusting the thickness node weight values ​​in the initial Stokes vector expression according to the thickness adjustment factor to obtain a target thickness node weight value; and / or, adjusting the angle node weight values ​​in the initial Stokes vector expression according to the angle adjustment factor to obtain a target angle node weight value; and determining the target Stokes vector expression based on the target wavelength node weight value, the target thickness node weight value, the target angle node weight value, and the initial Stokes vector expression.

[0041] Specifically, the initial Stokes vector expression is:

[0042] in, As the initial Stokes vector, The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. This is the thickness deflection parameter.

[0043] The target Stokes vector expression is:

[0044] in, Let Stokes vector be the target vector. The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. For thickness deflection parameters, Angle adjustment factor, For wavelength adjustment factor, This is the thickness adjustment factor.

[0045] In this embodiment, by multiplying the weight values ​​of the angle node, wavelength node, and thickness node by the corresponding angle adjustment factor, wavelength adjustment factor, and thickness adjustment factor, respectively, the weights of each parameter node in the thin film forward modeling model are precisely adjusted, optimizing the flexibility and targeting of the depolarization correction, thereby obtaining a target Stokes vector expression that is more consistent with the actual depolarization situation and improving the accuracy of thin film forward modeling.

[0046] In one specific embodiment, in S303, forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum, including: inputting the target Stokes vector into the target thin film forward modeling model function to obtain the forward modeling spectrum.

[0047] In one specific embodiment, in S304, determining the parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets a preset deviation range is the target parameter adjustment factor. This includes: obtaining the mean square error value between the forward modeling spectrum and the measured spectrum; taking the parameter adjustment factor in the target thin film forward modeling model function corresponding to the minimum value among all mean square error values ​​as the target parameter adjustment factor; the target parameter adjustment factor includes a target wavelength adjustment factor, a target thickness adjustment factor, and / or a target angle adjustment factor.

[0048] In one specific embodiment, a thin film sample A is given, and the ellipsometric measurement spectrum of sample A is measured using a thin film nanometer measurement device, denoted as mea_spectrum_value; the Stokes vector expression is given by the thin film transfer matrix method as s=F(wave), where wave is the wavelength. The Stokes vector s can be directly converted into an ellipsometric spectrum, i.e., spectrum=G(s). Then, the thin film forward modeling function can be expressed as spectrum=G(F(wave)), that is, the ellipsometric spectrum spectrum is the forward modeling spectrum.

[0049] By employing the LM algorithm and fitting the measured spectrum with a forward modeling function, the parameters to be determined (such as thickness) can be obtained. With angle depolarization (aoi_dep), wavelength depolarization (wave_dep), and thickness depolarization (thk_dep), an adaptive adjustment factor will be introduced for each single forward modeling step in the fitting process, calculating the forward modeling spectrum corresponding to the wavelength (wave_value).

[0050] Understandably, in this implementation, anything ending with "_value" represents a numerical value.

[0051] Understandably, thin film nanometry is a model-based measurement method, often employing a method based on LM nonlinear regression, where the forward function used in the LM algorithm is the forward modeling function for thin films.

[0052] Understandably, the Stokes vector expression above is s = F(wave), where wave is actually a sequence, such as 300 to 1000, and its value is the wavelength corresponding to the spectrum in the measurement spectrum.

[0053] Understandably, the main parameters in the forward modeling function include the parameters of the optical constant model, thickness, incident angle, and wavelength. Since the main objects of this patent study are incident angle, wavelength, and thickness, the Stokes vector expression is written as s = F(aoi,wave,thk).

[0054] When there is depolarization, wavelength The Stokes vector generated by forward modeling corresponds to the initial Stokes vector expression, specifically:

[0055] in, As the initial Stokes vector, The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first iAngle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. This is the thickness deflection parameter.

[0056] Specifically, the initial Stokes vector expression introduces de-biasing in three dimensions: angle de-biasing, wavelength de-biasing, and thickness de-biasing.

[0057] Further, for debiasing in one dimension, an adaptive adjustment factor for n nodes is given, the value of which is... Its value is 0 or 1.

[0058] Therefore, the expression for the target Stokes vector after adding the adjustment factor is as follows:

[0059] in, Let Stokes vector be the target vector. The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. For thickness deflection parameters, Angle adjustment factor, For wavelength adjustment factor, This is the thickness adjustment factor.

[0060] Understandably, since there are three dimensions of angle depolarization, wavelength depolarization, and thickness depolarization, there are also three dimensions of corresponding adjustment factors: factor_k, factor_j, and factor_i each have n adjustment factors, so there are a total of 3n adjustment factor variables.

[0061] Understandably, in reality, the three types of debiasing do not necessarily occur simultaneously; that is, the adjustment factor can be zero.

[0062] Finally, the Stokes vector expression in the model function during the original LM algorithm regression solution is replaced with the Stokes vector expression after adding the adjustment factor. During the LM algorithm regression solution process, the adjustment factor is set as a floating parameter. Finally, parameters including thickness and the floating factor are extracted to obtain the desired result.

[0063] Understandably, due to the fluctuation of the adjustment factor, the regression solution using the LM algorithm may be relatively slower than usual, but the accuracy is greatly improved. Therefore, in practical applications, the adjustment factor in the forward modeling function expression can be verified first using a standard thin film sample. Then, the adjustment factor can be fixed and not fluctuated, and the modeling function can be used to solve other samples of the same type. This greatly improves the accuracy of thin film thickness calculation without significantly affecting the speed.

[0064] Furthermore, after determining the target parameter adjustment factor, the method further includes: when the target parameter adjustment factor is a target wavelength adjustment factor and its value is zero, determining that there is no wavelength de-polarization at the point corresponding to the target parameter adjustment factor; when the target parameter adjustment factor is a target thickness adjustment factor and its value is zero, determining that there is no thickness de-polarization at the point corresponding to the target parameter adjustment factor; and when the target parameter adjustment factor is a target angle adjustment factor and its value is zero, determining that there is no angle de-polarization at the point corresponding to the target parameter adjustment factor.

[0065] To facilitate better implementation of the parameter determination method for the thin film forward modeling model provided in the embodiments of the present invention, the embodiments of the present invention also provide an apparatus based on the above-described parameter determination method for the thin film forward modeling model. The meanings of the terms used are the same as in the above-described parameter determination method for the thin film forward modeling model, and specific implementation details can be found in the descriptions in the method embodiments.

[0066] Please see Figure 4 , Figure 4 This is a schematic diagram of an embodiment of a parameter determination device for a thin film forward modeling model provided in this invention. The parameter determination device 400 for the thin film forward modeling model may include: The target Stokes vector expression determination module 401 is used to perform debiasing correction on the initial Stokes vector expression of the thin film forward modeling function based on the parameter adjustment factor to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factors and the initial Stokes vector expression. The parameter adjustment factors include wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. Gaussian processing module 402 is used to determine the Gaussian node sequence value and node weight value according to the preset number of Gaussian nodes; The forward modeling module 403 is used to perform forward modeling based on the target thin film forward modeling model function containing the target Stokes vector expression, and obtain the forward modeling spectrum; The target parameter adjustment factor determination module 404 is used to determine the parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

[0067] This invention also provides an electronic device, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, specifically: The electronic device may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 502, and by calling data stored in the memory 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.

[0068] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the electronic device. In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0069] The electronic device also includes a power supply 503 that supplies power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management storage medium, thereby enabling functions such as charging, discharging, and power consumption management through the power management storage medium. The power supply 503 may also include one or more DC or AC power supplies, recharge storage media, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0070] The electronic device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0071] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 501 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 502 according to the following instructions, and the processor 501 runs the applications stored in the memory 502 to realize various functions, as follows: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. Determine the Gaussian node sequence value and node weight value according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression, and the forward modeling spectrum is obtained; The parameter adjustment factor in the target thin film forward modeling model function is determined to be the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

[0072] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0073] To this end, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, the computer program being loaded by a processor to execute the steps in the parameter determination method for any of the thin film forward modeling models provided in the embodiments of the present invention. For example, the computer program, when loaded by a processor, can execute the following steps: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. Determine the Gaussian node sequence value and node weight value according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression, and the forward modeling spectrum is obtained; The parameter adjustment factor in the target thin film forward modeling model function is determined to be the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

[0074] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0075] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0076] Since the computer program stored in the computer-readable storage medium can execute the steps in the parameter determination method of any thin film forward modeling model provided in the embodiments of the present invention, the beneficial effects that the parameter determination method of any thin film forward modeling model provided in the embodiments of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0077] The foregoing provides a detailed description of a depolarization detection and elimination method, apparatus, and electronic device based on a thin film morphology model provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining parameters of a thin film forward modeling model, characterized in that, include: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling model function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and the initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. The Gaussian node sequence value and the node weight value are determined according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum; The parameter adjustment factor in the target thin film forward modeling model function is the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum is within a preset deviation range.

2. The method for determining parameters of the thin film forward modeling model according to claim 1, characterized in that, The node weight values ​​include wavelength node weight values, thickness node weight values, and angle node weight values; the debiasing correction of the initial Stokes vector expression of the thin film forward modeling function based on the parameter adjustment factor to obtain the target Stokes vector expression includes: The wavelength node weight values ​​in the initial Stokes vector expression are adjusted according to the wavelength adjustment factor to obtain the target wavelength node weight values. And / or, The thickness node weight values ​​in the initial Stokes vector expression are adjusted according to the thickness adjustment factor to obtain the target thickness node weight values; And / or, The angle node weight values ​​in the initial Stokes vector expression are adjusted according to the angle adjustment factor to obtain the target angle node weight values; The target Stokes vector expression is determined based on the target wavelength node weight value, the target thickness node weight value, the target angle node weight value, and the initial Stokes vector expression.

3. The method for determining parameters of the thin film forward modeling model according to claim 1, characterized in that, The initial Stokes vector expression is: in, As the initial Stokes vector, The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. This is the thickness deflection parameter.

4. The method for determining parameters of the thin film forward modeling model according to claim 1, characterized in that, The target Stokes vector expression is: in, Let Stokes vector be the target vector. The angle node weight value, The wavelength node weight value, e is the thickness node weight value. This is the function corresponding to the thin-film transport matrix method. For the first i Angle values For the first i Angle deflection parameters The angle is the Gaussian node sequence value. The wavelength is the Gaussian node sequence value. For the thickness Gaussian node sequence values, This is the wavelength value. For wavelength depolarization parameters, This is the thickness value. For thickness deflection parameters, Angle adjustment factor, For wavelength adjustment factor, This is the thickness adjustment factor.

5. The method for determining parameters of a thin film forward modeling model according to claim 1, characterized in that, The forward modeling based on the target thin film forward modeling model function containing the target Stokes vector expression, to obtain the forward modeling spectrum, includes: The target Stokes vector is input into the forward modeling function of the target thin film to obtain the forward modeling spectrum.

6. The method for determining parameters of the thin film forward modeling model according to claim 1, characterized in that, The parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets the preset deviation range is the target parameter adjustment factor, including: Obtain the mean square error value between the forward modeling spectrum and the measured spectrum; The parameter adjustment factor in the target thin film forward modeling function corresponding to the minimum value among all mean square error values ​​is taken as the target parameter adjustment factor; The target parameter adjustment factors include target wavelength adjustment factor, target thickness adjustment factor and / or target angle adjustment factor.

7. The method for determining parameters of a thin film forward modeling model according to claim 6, characterized in that, After determining the target parameter adjustment factor, the method further includes: When the target parameter adjustment factor is the target wavelength adjustment factor and its value is zero, it is determined that there is no wavelength de-polarization at the point corresponding to the target parameter adjustment factor. When the target parameter adjustment factor is the target thickness adjustment factor and its value is zero, it is determined that there is no thickness deviation at the point corresponding to the target parameter adjustment factor. When the target parameter adjustment factor is the target angle adjustment factor and its value is zero, it is determined that the point corresponding to the target parameter adjustment factor does not have an angle back-off.

8. A parameter determination device for a thin film forward modeling model, characterized in that, include: The target Stokes vector expression determination module is used to perform debiasing correction on the initial Stokes vector expression of the thin film forward modeling model function based on the parameter adjustment factor to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factors and the initial Stokes vector expression. The parameter adjustment factors include wavelength adjustment factors, thickness adjustment factors and / or angle adjustment factors. The Gaussian processing module is used to determine the Gaussian node sequence value and the node weight value according to the preset number of Gaussian nodes; The forward modeling module is used to perform forward modeling based on the target thin film forward modeling model function containing the target Stokes vector expression, and obtain the forward modeling spectrum; The target parameter adjustment factor determination module is used to determine the parameter adjustment factor in the target thin film forward modeling model function when the mean square error value between the forward modeling spectrum and the measured spectrum meets the preset deviation range.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the following steps: Based on the parameter adjustment factor, the initial Stokes vector expression of the thin film forward modeling model function is debiased and corrected to obtain the target Stokes vector expression. The target Stokes vector expression includes Gaussian node sequence values, node weight values, parameter adjustment factor and the initial Stokes vector expression. The parameter adjustment factor includes wavelength adjustment factor, thickness adjustment factor and / or angle adjustment factor. The Gaussian node sequence value and the node weight value are determined according to the preset number of Gaussian nodes; Forward modeling is performed based on the target thin film forward modeling model function containing the target Stokes vector expression to obtain the forward modeling spectrum; The parameter adjustment factor in the target thin film forward modeling model function is the target parameter adjustment factor when the mean square error between the forward modeling spectrum and the measured spectrum is within a preset deviation range.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the method for determining the parameters of the thin film forward modeling model according to any one of claims 1 to 7.

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