Method, system, computing device and storage medium for measuring optical constants of thin films

Abstract

The present disclosure relates to a method, system, computing device and storage medium for measuring optical constants of thin films, the method comprising: obtaining light source spectrum data of incident light of multiple wavelengths emitted by a light source; obtaining emission spectrum data about the thin film to be measured, the emission spectrum data being generated by measuring the thin film to be measured including multiple thin film layers using the incident light; calculating measured spectrum data based on the light source spectrum data and the emission spectrum data, the measured spectrum data at least indicating the reflectivity or transmittance of the thin film to be measured under multiple wavelengths of the incident light; inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the thin film to be measured, so that the simulated spectrum data about the thin film to be measured output by the thin film network model is close to the measured spectrum data; and determining at least one of the refractive index of the thin film to be measured and the thickness of the thin film body of the thin film layer based on the parameters of the thin film network model. The present disclosure can quickly realize the measurement of optical parameters of multilayer thin films.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of metrology, and more particularly to methods, systems, computing devices, and computer-readable storage media for measuring optical constants of thin films. Background Art

[0002] Thin film optical measurement is widely used in industrial fields such as semiconductors and optoelectronics. The traditional scheme for measuring the optical constants of thin films (for example, but not limited to, thin films included in semiconductor devices or optoelectronic devices) usually involves two spaces: parameter space and data space. The parameter space is composed of all possible parameters (for example, the thickness and refractive index of the film). The data space is formed by the optical responses of thin films with different parameters (for example, reflection spectra or refraction spectra). In order to achieve the measurement of typical thin film optical constants, it is usually necessary to select initial parameters in the parameter space, and use the initial parameters as the starting point to calculate its optical response through electromagnetic simulation, and then compare the simulated optical response with the measured optical response to determine the update direction of the optical response. Then, by performing several electromagnetic simulations in each dimension of the parameter space and comparing the differences in spectral data, the influence of parameter changes on the spectrum is determined, thereby changing the parameters to achieve the update of the optical response. Repeat the above process until the simulated optical response matches the measured optical response.

[0003] In the above-mentioned conventional scheme for measuring the optical constants of thin films, the number of electromagnetic simulation calculations required to establish the convergence direction is proportional to the dimension of the parameter space. Therefore, for simple thin films with fewer layers to be tested, the above-mentioned conventional scheme for measuring the optical constants of thin films is still competent due to the small number of parameters. However, for thin films with more layers to be tested, as the number of layers and parameters of the thin films increase rapidly, the number of electromagnetic simulation calculations required to establish the convergence direction is huge, which will lead to lengthy calculation time.

[0004] In summary, the conventional solution for measuring the optical constants of thin films has the disadvantage that it is difficult to perform rapid measurement on multi-layer thin films. Summary of the invention

[0005] The present disclosure proposes a method, system, computing device and non-transitory machine-readable storage medium for measuring the optical constants of a thin film, which can quickly measure multi-layer thin films. Furthermore, the present disclosure can quickly measure the optical parameters of thin films with more than ten layers or even more layers and different thicknesses.

[0006] According to the first aspect of the present disclosure, a method for measuring the optical constants of a thin film is provided. The method comprises: obtaining light source spectrum data of incident light of multiple wavelengths emitted by a light source; obtaining emission spectrum data about the thin film to be measured, the emission spectrum data being generated by measuring the thin film to be measured including multiple thin film layers using the incident light; calculating measured spectrum data based on the light source spectrum data and the emission spectrum data, the measured spectrum data at least indicating the reflectivity or transmittance of the thin film to be measured under multiple wavelengths of the incident light; inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the thin film to be measured, so that the simulated spectrum data about the thin film to be measured output by the thin film network model is close to the measured spectrum data; and determining at least one of the refractive index of the thin film to be measured and the thickness of the thin film body of the thin film layer based on the parameters of the thin film network model.

[0007] According to the second aspect of the present disclosure, a computing device is also provided, which includes: at least one processing unit; at least one memory, which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, and when the instructions are executed by the at least one processing unit, the computing device executes the method in the first aspect or the second aspect of the present disclosure.

[0008] According to a third aspect of the present invention, there is further provided a non-transitory machine-readable storage medium having machine-readable program instructions stored thereon, wherein the machine-readable program instructions are configured to cause a machine to execute the method according to the first aspect of the present disclosure.

[0009] According to the fourth aspect of the present disclosure, a system for measuring the optical constants of a thin film is also provided, comprising: a spectral measurement device configured to measure the thin film to be measured based on incident light of multiple wavelengths so as to generate output spectral data about the thin film to be measured; and a computing device configured to be operable to execute the method according to the first aspect or the second aspect of the present disclosure.

[0010] In some embodiments, the film to be tested includes at least 10 film layers, and the thickness of the film body of each film layer is the same or different.

[0011] In some embodiments, the thickness of the film body of each film layer is included in the parameters of the film network model, and the film to be tested is used in a semiconductor device.

[0012] In some embodiments, based on the determined thickness of the film body of each thin film layer, a distribution range of the thickness of the film body is determined; it is determined whether there is a thickness of the film body that deviates from the distribution range by more than a predetermined threshold; and in response to the existence of a thickness of the film body that deviates from the distribution range by more than a predetermined threshold, it is determined that the thin film layer corresponding to the thin film body is an abnormal thin film layer.

[0013] In some embodiments, the thin film network model includes a film body network layer and an interface network layer, the film body network layer is used to simulate the propagation characteristics of light in the thin film body of the thin film layer, and the interface network layer is used to simulate the propagation characteristics of light in the interface layer between adjacent thin film layers, and the forward propagation characteristics of the thin film network model indicate the transmission characteristics of light in the thin film to be tested.

[0014] In some embodiments, the film body network layer is used to convert the field amplitude characteristic vector on the input side of the film body network layer into the field amplitude characteristic vector on the output side of the film body network layer via a film body conversion matrix, and the film body conversion matrix is ​​associated with the additional phase of light propagating in the thin film body of the thin film layer simulated by the film body network layer.

[0015] In some embodiments, the interface network layer is used to convert the field amplitude characteristic vector at the input side of the interface network layer into the field amplitude characteristic vector at the output side of the interface network layer via an interface matrix.

[0016] In some embodiments, the membrane main network layer is configured similar to a neuron layer of a neural network, and the interface network layer is configured similar to a weight layer of a neural network.

[0017] In some embodiments, the method for measuring the optical constants of a thin film also includes: obtaining the number of thin film layers and the initial thickness of the thin film layers included in the thin film to be measured; and based on the number of layers and the initial thickness of the thin film layers, configuring the membrane main network layer and the interface network layer of the thin film network model, and the back propagation process of the thin film network model is configured based on the back propagation characteristics of the neural network.

[0018] In some embodiments, the thin film network model is trained by: taking the measured spectral data as a training target, training the thin film network model so as to make the simulated spectral data fit the measured spectral data by adjusting the parameters of the thin film network model.

[0019] In some embodiments, the measured spectral data at least indicates the reflectivity or transmittance of the film to be tested at multiple wavelengths of incident light, and inputting the light source spectral data into a thin film network model for simulating the propagation characteristics of light in the film to be tested includes: inputting the measured light source reference data into the thin film network model, and the light source spectral data includes the measured light source reference data.

[0020] It should also be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the embodiments of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0022] Figure 1 A schematic diagram comparing a conventional method for measuring the optical constants of a thin film and a method for measuring the optical constants of a thin film according to an embodiment of the present disclosure is shown.

[0023] Figure 2 A schematic diagram showing an example system of a method for measuring optical constants of a thin film according to an embodiment of the present disclosure.

[0024] Figure 3 A flow chart of a method 300 for measuring optical constants of a thin film according to an embodiment of the present disclosure is shown.

[0025] Figure 4 A schematic diagram showing the corresponding connection between the thin film network model and the neural network is shown schematically.

[0026] Figure 5 A schematic diagram of the membrane main network layer and the interface network layer of the thin film network model is schematically shown.

[0027] Figure 6 A schematic diagram showing a comparison between the back propagation process of a thin film network model and the back propagation process of a multi-layer neural network.

[0028] Figure 7 A schematic diagram showing the measured results of the method for measuring the optical constants of a single-layer film to be measured according to an embodiment of the present disclosure is shown.

[0029] Figure 8 A schematic diagram showing comparison of measurement results of a method for measuring optical constants of a multi-layer thin film to be measured according to an embodiment of the present disclosure is shown.

[0030] Fig. 9 A flow chart of a method for determining an abnormal thin film layer of a thin film to be tested according to an embodiment of the present disclosure is shown.

[0031] Fig.10 A schematic diagram showing a method for determining an abnormal film layer of a film to be tested according to an embodiment of the present disclosure

[0032] Fig.11 A block diagram of an electronic device suitable for implementing the embodiments of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0033] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0034] Figure 1 A schematic diagram comparing a conventional method for measuring the optical constants of a thin film and a method for measuring the optical constants of a thin film according to an embodiment of the present disclosure is shown. Figure 1 The right part of FIG schematically shows the conventional method for measuring the optical constants of thin films. Figure 1 As shown, in the conventional method of measuring the optical constants of a thin film, a set of optical parameters of the thin film to be measured (not shown) is used, for example, Figure 1 The spectrum of the film to be measured is represented by a small ball in the parameter space 150 (e.g., a three-dimensional parameter space), and the spectrum of the film to be measured is represented by a small cube in the data space 130 (e.g., a two-dimensional data space). Figure 1 In the example, each ball marks a set of parameter data. Each small cube marks a set of spectral data. Through electromagnetic simulation, each ball corresponds to a small cube, such as Figure 1As shown in the figure, the straight line from the ball in parameter space 150 to the corresponding small cube in data space 130. Each of the above straight lines indicates an electromagnetic simulation, that is, a set of spectral data marked by the small cube is calculated based on a set of parameter data marked by the ball. For the first set of parameter data marked as the first ball 152, its convergence direction toward the minimum is determined by comparing the electromagnetic simulation results of the adjacent points of the first ball 152 (such as the second ball 154 and the third ball 156). For example, the first ball 152 needs to be gradually moved to the adjacent valley position 158, that is, the first set of parameter data needs to be gradually adjusted along the convergence direction until the spectral data in the data space corresponding to the electromagnetic simulation is closest to the minimum parameter data of the measured spectral data (the minimum parameter data is, for example, marked as the valley position 158). In order to know the direction of the convergence, it is necessary to adjust the first set of parameter data marked as the first small ball 152 to the second set of parameter data marked as the second small ball 154, and calculate the second set of spectral data marked as the second small cube 134 based on the second set of parameter data. Then, the first set of parameter data marked as the first small ball 152 is adjusted to the third set of parameter data marked as the third small ball 156, and the third set of spectral data marked as the third small cube 136 is calculated based on the third set of parameter data. Then, the change of the second set of spectral data relative to the first set of spectral data (the first set of spectral data is calculated based on the first set of parameter data) and the change of the third set of spectral data relative to the first set of spectral data are compared to determine which adjustment direction can make the first set of parameters converge quickly (that is, make the small ball drop quickly), and so on, until the small ball is adjusted to the valley bottom position 158.

[0035] In the above-mentioned traditional scheme for measuring the optical constants of thin films, since the number of electromagnetic simulation calculations required to establish the convergence direction is proportional to the dimension of the parameter space, it is necessary to simulate the change of spectral data under each parameter change. Therefore, for thin films with a large number of layers to be tested, as the number of film layers and parameters increase rapidly, the number of electromagnetic simulation calculations required to establish the convergence direction is huge, which will lead to lengthy calculation time. Especially for composite thin films with hundreds of thin film layers to be tested, this will make the measurement process consume a considerable amount of time. Therefore, the traditional scheme for measuring the optical constants of thin films has the disadvantage that it is difficult to perform rapid measurements on multi-layer thin films.

[0036] Figure 1The left part shows a method for measuring the optical constants of a thin film according to an embodiment of the present disclosure. The present disclosure directly constructs a thin film network model 140 similar to a neural network, and the thin film network model 140 is a thin film network model for simulating the propagation characteristics of light in the thin film to be measured (for example, its forward propagation characteristics indicate the transmission characteristics of light in the thin film to be measured). The thin film network model 140, for example, includes a plurality of film body network layers 142 and one or more interface network layers 146. Each film body network layer 142 is used to simulate the propagation characteristics of light in the film body of the thin film layer, and each interface network layer 146 is used to simulate the propagation characteristics of light in the interface layer between adjacent thin film layers. The input of the thin film network model 140 is, for example, the light source spectrum data 110, and the output of the thin film network model 140 is the simulated spectrum data 120 of the thin film to be measured (for example, refraction spectrum data or projection spectrum data). Therefore, the small cubes in the data space 130 in the traditional method for measuring the optical constants of the thin film are converted into simulated spectral data 120 of the thin film network model 140, and a set of thin film parameters (such as small balls) in the parameter space in the traditional method for measuring the optical constants of the thin film are converted into intrinsic parameters of the thin film network model 140, such as network parameters 144, which network parameters 144, for example, include at least the thickness d of the film body, the number n(λ) of the film body network layer 142 and the interface network layer 146.

[0037] It has been found that the thin film network model 140 itself can autonomously update its network parameters 144 through back propagation, and no additional electromagnetic simulation calculation is required to obtain the convergence direction. Therefore, it should be understood that a relationship between the components in the thin film network model 140 is established similar to the relationship between the network layers of the neural network model. Then, for the training process of the thin film network model 140, the network parameters 144 can be optimized so that the output (i.e., simulated spectrum data 120) of the thin film network model 140 based on the input of the thin film network model 140 (i.e., the light source spectrum data 110) is close to the measured spectrum data of the film to be measured, and at least one of the refractive index of the film to be measured and the thickness of the film body is obtained based on the optimized network parameters 144. It does not need to go through multiple electromagnetic simulation calculation processes, so the time required for each iteration can be greatly reduced, and the measurement of multi-layer thin films can be quickly achieved.

[0038] Therefore, in order to at least partially solve one or more of the above problems and other potential problems, the exemplary embodiments of the present disclosure propose a scheme for measuring the optical constants of thin films. By calculating the measured spectral data indicating at least the reflectivity or transmittance of the film to be tested at multiple wavelengths of the incident light based on the acquired light source spectrum data and the emission spectrum data, and by inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the film to be tested (for example, the forward propagation characteristics of the thin film network model indicate the transmission characteristics of light in the film to be tested), it is possible to generate a simulated spectrum close to the measured spectral data of the film to be tested based on the input of the thin film network model (i.e., the light source spectrum). And based on the parameters of the thin film network model to determine the refractive index or transmittance of the film to be tested, the present disclosure no longer requires additional multiple electromagnetic simulation calculations to obtain the convergence direction, so even if a multi-layer film to be tested is measured, the time required for each iteration can be greatly reduced, and the measurement of multi-layer thin films can be quickly achieved. Even for thin films with more than ten layers or even more layers and different thicknesses, the present disclosure can also quickly measure their optical parameters. In addition, the present disclosure adds back propagation in the transfer matrix calculation process, which has significant technical advantages over traditional differential calculation methods; at the same time, the present disclosure does not require a data set for training, only measured spectral data, and therefore has obvious technical advantages over traditional neural network-based solutions.

[0039] Figure 2 Schematic diagram of an example system 200 that can be used to measure optical constants of a thin film according to an embodiment of the present disclosure is shown. Figure 2 As shown, the system 200 includes: a spectrum measurement device 210 , a computing device 230 , and a thin film 220 to be measured.

[0040] Regarding the spectrum measuring device 210, it is used to measure the film to be measured using incident light of multiple wavelengths from a light source, so as to generate output spectrum data at least indicating the object to be measured under the multiple wavelengths of the incident light. Figure 2 The spectral measurement device 210 shown includes at least a plurality of first optical lenses L1, a second optical lens L2, a third optical lens L3, a fourth optical lens L4, an incident light fiber F1 for emitting incident light, an output light fiber F2 for receiving output light, a reflector M, and other components such as a BS half-reflecting half-mirror.

[0041] Regarding the film to be tested 220, it includes, for example, multiple layers of thin film layers. In some embodiments, the thin film layer of the film to be tested 220 is, for example but not limited to, SiO2. In some embodiments, the substrate is a transparent substrate or a semi-transparent substrate. In some embodiments, the refractive index of the thin film layer of the film to be tested is less than the refractive index of the substrate. In some embodiments, the film to be tested 220 includes at least 10 layers of thin film layers, and the thickness of the film body of each layer of the thin film layer is the same or different. In some embodiments, the film to be tested 220 is used for semiconductor devices or optoelectronic devices, and is a component of semiconductor devices or optoelectronic devices. In some embodiments, the film to be tested 220 is integrated with a computing device 230. In some embodiments, the film to be tested 220 is separately arranged from the computing device 230.

[0042] Regarding the computing device 230, it is used, for example, to obtain light source spectrum data of incident light of multiple wavelengths emitted by the light source; obtain emission spectrum data about the film to be tested; calculate measured spectrum data based on the light source spectrum data and the emission spectrum data; input the light source spectrum data into a thin film network model used to simulate the propagation characteristics of light in the film to be tested, so that the simulated spectrum data about the film to be tested output by the thin film network model is close to the measured spectrum data; and determine at least one of the refractive index of the film to be tested and the thickness of the film body of the film layer based on the parameters of the thin film network model. The computing device 230 can have one or more processing units, including dedicated processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines can also be run on each computing device 230.

[0043] The computing device 230 includes, for example, a light source spectrum data acquisition unit 232 , an emission spectrum data acquisition unit 234 , a measured spectrum data calculation unit 236 , a thin film network model unit 238 , and a refractive index and thickness determination unit 240 for the thin film body.

[0044] The light source spectrum data acquisition unit 232 is used to acquire light source spectrum data of incident light of multiple wavelengths emitted by the light source.

[0045] The emission spectrum data acquisition unit 234 is used to acquire emission spectrum data about the thin film to be measured. The emission spectrum data is generated by measuring the thin film to be measured including multiple thin film layers using the incident light.

[0046] The measured spectrum data calculation unit 236 is used to calculate measured spectrum data based on the light source spectrum data and the emission spectrum data. The measured spectrum data at least indicates the reflectivity or transmittance of the film to be measured under multiple wavelengths of incident light.

[0047] Regarding the thin film network model unit 238, it is used to input the light source spectrum data into the thin film network model used to simulate the propagation characteristics of light in the thin film to be tested, so that the simulated spectrum data about the thin film to be tested output by the thin film network model is close to the measured spectrum data.

[0048] The refractive index and film body thickness determination unit 240 is used to determine at least one of the refractive index of the film to be tested and the thickness of the film body of the film layer based on the parameters of the film network model.

[0049] The following will combine Figure 3 to Figure 4 The method 300 for measuring the optical constants of a thin film will be described in detail. Figure 3 A flow chart of a method 300 for measuring optical constants of a thin film according to an embodiment of the present disclosure is shown. Figure 4 FIG. 4 is a schematic diagram of a thin film network model 400 according to some embodiments of the present disclosure. It should be understood that the method 300 may be implemented, for example, in Fig.11 The electronic device 1100 described above is executed. Figure 2 The method 300 is executed at the described computing device 230. It should be understood that the method 300 may also include additional components and actions not shown and / or may omit the components and actions shown, and the scope of the present disclosure is not limited in this respect.

[0050] At step 302, the computing device 230 obtains light source spectrum data of incident light of multiple wavelengths emitted by the light source. For example, the computing device 230 obtains Figure 2 The spectral data of the incident light emitted through the incident light fiber F1 is the light source spectral data.

[0051] At step 304, the computing device 230 obtains emission spectrum data about the film to be tested, where the emission spectrum data is generated by measuring the film to be tested including multiple film layers using the incident light. Figure 2 The spectral data of the outgoing light emitted through the outgoing light optical fiber F2 is the outgoing spectral data.

[0052] Regarding the film to be tested, it, for example, includes at least a plurality of (for example, a predetermined number of) film layers. Figure 4 The left side of FIG. 4 schematically shows the structure of the film 410 to be tested. Figure 4 As shown, the film to be tested 410 includes a stacked multilayer film. There is an interface between every two adjacent film layers in the multilayer film. In some embodiments, the film to be tested includes at least 10 film layers, and the thickness of the film body of each film layer is the same or different. In some embodiments, the film to be tested is used for semiconductor devices.

[0053] At step 304 , the computing device 230 calculates measured spectral data based on the light source spectral data and the emission spectral data, wherein the measured spectral data at least indicates the reflectivity or transmittance of the film to be tested under multiple wavelengths of incident light.

[0054] For example, the computing device 230 calculates the measured spectrum data based on the ratio of the emission spectrum data to the light source spectrum data. By adopting the above means, the present disclosure can make the measured spectrum data reflect the absolute reflectivity or transmittance of the film to be tested without being affected by the difference of the light source spectrum.

[0055] At step 306 , the computing device 230 inputs the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the thin film to be tested, so that the simulated spectrum data of the thin film to be tested output by the thin film network model is close to the measured spectrum data.

[0056] In some embodiments, the computing device 230 inputs the measured light source reference data into the thin film network model, and the light source spectrum data includes the measured light source reference data. In some embodiments, the light source intensity of the measured light source reference data is, for example, 1. Figure 1 The light source spectrum data 110 indicates that the above method is helpful to simplify the calculation.

[0057] Regarding the thin film network model, it includes a film main body network layer and an interface network layer, the film main body network layer is used to simulate the propagation characteristics of light in the film main body of the thin film layer, and the interface network layer is used to simulate the propagation characteristics of light in the interface layer between adjacent thin film layers. The forward propagation characteristics of the thin film network model indicate the transmission characteristics of light in the film to be tested. The present disclosure is conducive to enabling the simulated spectral data output by the thin film network model to directly reflect the absolute reflectivity or transmittance by inputting the acquired light source spectral data into the thin film network model.

[0058] Regarding the film body network layer, it is used to convert the field amplitude characteristic vector on the input side of the film body network layer into the field amplitude characteristic vector on the output side of the film body network layer via the film body conversion matrix, and the film body conversion matrix is ​​associated with the additional phase of the light propagating in the film body of the thin film layer simulated by the film body network layer.

[0059] Regarding the interface network layer, it is used to convert the field amplitude characteristic vector at the input side of the interface network layer into the field amplitude characteristic vector at the output side of the interface network layer via the interface matrix.

[0060] The forward propagation process of the thin film network model is used to simulate the propagation process of light in the thin film to be tested. Figure 4 A schematic diagram schematically shows the corresponding connection between the thin film network model and the neural network model. Figure 4The left side of the diagram schematically shows a schematic diagram of the network structure of the thin film network model 410. The thin film network model 410 includes multiple film body network layers and one or more interface network layers. The film body network layer 412 of the i-1th layer of the thin film network model 410 is used to simulate the film body of the i-1th layer of the film to be tested. The i-th film body network layer 414 of the thin film network model 410 is used to simulate the film body of the i-th layer of the film to be tested. The interface network layer is used to simulate the adjacent interface between the film body of the i-1th layer of the film to be tested and the i-th film body. The adjustable network parameters of the thin film network model 410 include the refractive index n(λ) and the thickness d of the film body. In some embodiments, the thin film network model 410 also includes a film surface network layer (e.g. Figure 4 The 0th layer on the left) and the substrate network layer, i.e. Figure 4 The nth layer is shown on the left.

[0061] Figure 4 The right side of the figure schematically shows a schematic diagram of the network structure of the multilayer neural network 430. The multilayer neural network 430 includes an input layer, multiple neural network layers 432, 436, a weight layer 434, and an output layer 438. The membrane main body network layer of the thin film network model 410 is similar to the neural network layer 432 of the multilayer neural network 430. The interface network layer of the thin film network model 430 is similar to the weight layer 434 of the multilayer neural network 430. The membrane surface network layer of the thin film network model 430 is similar to the input layer of the multilayer neural network 430, and the substrate network layer of the thin film network model 430 is similar to the output layer 438 of the multilayer neural network 430.

[0062] Figure 5 A schematic diagram of the membrane main network layer and the interface network layer of the thin film network model is schematically shown. Figure 5 The left part schematically shows the interface network layer 512 and the i-th membrane main network layer 516 between the output side 510 (below) of the i-1th membrane main network layer and the input side 514 (above) of the i-th membrane main network layer of the thin film network model. Figure 5 The right part of schematically shows a weight layer 520, a neuron layer 522, and the input and output of the weight layer 520, and the input and output of the neuron layer 522 of the multi-layer neural network.

[0063] The forward propagation at the weight layer of the multi-layer neural network is explained below in conjunction with expression (1).

[0064]

[0065] In the above expression (1), represents the input of the i-th weight layer of the multi-layer neural network, Represents the output of the i-th weight layer of the multi-layer neural network. i Represents the i-th weight matrix of a multi-layer neural network, which is used to transform the input X of the i-th weight layer i-1 Converted to the output Y of the i-th weight layer i .

[0066] The forward propagation at the interface network layer of the thin film network model is explained below in conjunction with expression (2).

[0067]

[0068] In the above expression (2), a i-1 and b i-1 represents the field amplitude eigenvector above the i-th interface network layer, c i and d i represents the field amplitude eigenvector below the i-th interface network layer. i The i-th interface matrix representing the thin film network model is used to convert the field amplitude eigenvector above the i-th interface network layer into the field amplitude eigenvector below the i-th interface network layer. That is, the interface network layer is used to convert the field amplitude eigenvector on the input side of the interface network layer into the field amplitude eigenvector on the output side of the interface network layer via the interface matrix.

[0069] By comparing the above expressions (1) and (2), it can be seen that the forward propagation process of the interface network layer of the thin film network model is similar to the forward propagation process of the weight layer of the neural network.

[0070] The forward propagation process of a multi-layer neural network at the neuron layer is explained below in conjunction with expression (3).

[0071]

[0072] In the above expression (3), represents the output of the i-th neuron layer of the multi-layer neural network, Represents the input of the i-th neuron layer of a multi-layer neural network. i represents the activation function of the i-th neuron layer, which is used to convert the input Y of the i-th neuron layer i Conversion output X i .

[0073] The forward propagation at the membrane main network layer of the thin film network model is explained below in conjunction with expression (4).

[0074]

[0075] In the above expression (4), c i and di represents the field amplitude eigenvector above the ith membrane main network layer, a i and b i Represents the field amplitude eigenvector beneath the ith membrane host network layer. Represents the i-th membrane body conversion matrix, which is used to convert the field amplitude feature vector above the i-th membrane body into the field amplitude feature vector below the i-th membrane body. That is, the membrane body network layer converts the field amplitude feature vector on the input side of the membrane body network layer into the field amplitude feature vector on the output side of the membrane body network layer via the membrane body conversion matrix. φ i represents the phase added to the light propagating in the i-th film due to the thickness of the film body. j represents the imaginary unit.

[0076] By comparing the above expressions (3) and (4), it can be seen that the forward propagation process of the membrane main network layer of the thin film network model is similar to the forward propagation process of the neuron layer of the neural network.

[0077] Through the above comparison, it should be understood that the thin film network model used to simulate multilayer thin films has similarities with the forward propagation process of multilayer neural networks and multilayer thin films.

[0078] Regarding the training method of the thin film network model, it includes, for example: taking the measured spectral data as the training target, training the thin film network model so as to make the simulated spectral data fit the measured spectral data by adjusting the parameters of the thin film network model.

[0079] Regarding the method of training a thin film network model, for example: training a thin film network model based on the Levenberg-Marquarelt algorithm LM (Levenberg-Marquarelt). The following is an explanation of the method of training a thin film network model in combination with expression (5).

[0080]

[0081] In the above expression (5), X k+1 and X k Represent the input parameters of the k+1th iteration and the kth iteration respectively, Represents X k The gradient vector of . k represents the number of iterations. u represents a variable parameter. The step size can be adjusted by changing the size of u. For example, by reducing the value of u, the step size can be increased to speed up the convergence. I represents the unit matrix. G represents the matrix. By using the LM algorithm to train the thin film network model, the convergence speed of the thin film network model can be fast, and it is not easy to fall into the local extreme value, and it can stably approach the optimal network parameter value.

[0082] It should be understood that there is no process corresponding to the back propagation of the multi-layer neural network in the film to be tested. Therefore, the film network model of the present disclosure needs to be configured with a back propagation process similar to that of the neural network.

[0083] Figure 6 A schematic diagram showing a comparison between the back propagation process of a thin film network model and the back propagation process of a multi-layer neural network.

[0084] The back propagation of the neural network model is explained below in conjunction with expression (6).

[0085]

[0086] In the above expression (6), L represents the loss function of the multi-layer neural network. Represents the gradient after propagation from the loss function L. Represents the transpose of the weight matrix.

[0087] The back propagation of the thin film network model is explained below in conjunction with expression (7).

[0088]

[0089] In the above expression (7), L1 represents the loss function of the thin film network model, and r represents the energy reflectivity. and Represents the gradient after the loss function L starts to propagate. Represents the transpose of the interface matrix. Represents the transformation matrix of the ith membrane entity.

[0090] The loss function L1 of the thin film network model can be calculated based on the following expression (8).

[0091]

[0092] In the above expression (8), L1 represents the loss function of the thin film network model, and r represents the predicted value of the thin film network model. Represents the expected value of the thin film network model.

[0093] In step 310 , the computing device 230 determines at least one of the refractive index of the film to be tested and the thickness of the film body of the film layer based on the parameters of the film network model.

[0094] For example, the following combination Figure 7 The effect of measuring the refractive index of a single-layer film with different thicknesses according to the method of the embodiment of the present disclosure is described. Figure 7Schematic diagram showing measurement results 700 of a method for measuring optical constants of a single-layer film to be measured according to an embodiment of the present disclosure. For example, Figure 7 The mark 710 indicates the curve of the measured reflectivity of the film to be tested as a function of wavelength. The mark 712 indicates the curve of the fitted reflectivity of the film to be tested as a function of wavelength obtained based on the measurement method disclosed in the present invention. Figure 7 It can be seen that the difference between the fitted reflectivity of the film to be measured based on the measurement method disclosed in the present invention and the actual measured reflectivity is very small, that is, the measurement result of the method disclosed in the present invention is very accurate.

[0095] The following combination Figure 8 The fitting process of measuring the reflectivity of a multi-layer thin film to be tested according to the method of an embodiment of the present disclosure is described. Figure 8 FIG. 8 is a schematic diagram showing a comparison of measurement results 800 of a method for measuring optical constants of a multilayer thin film to be measured according to an embodiment of the present disclosure. For example, Figure 8 Column (a) in the figure shows a schematic diagram of the structure of a multi-layer film to be tested, where label 810 indicates 2 film layers, label 812 indicates 60 film layers, and label 814 indicates 232 film layers. Figure 8 The mark 820 in the column (b) in FIG. 8 indicates the variation curve of the measured reflectivity of the two-layer film to be tested relative to the wavelength, and the mark 822 indicates the variation curve of the initial reflectivity of the two-layer film to be tested relative to the wavelength. Figure 8 The mark 840 in the column (c) in FIG. 8 indicates the variation curve of the measured reflectivity of the two-layer film to be tested relative to the wavelength, and the mark 842 indicates the variation curve of the fitted reflectivity of the two-layer film to be tested relative to the wavelength. Figure 8 The mark 824 in the column (b) in FIG. 8 indicates the variation curve of the measured reflectivity of the 60-layer thin film with respect to the wavelength, and the mark 826 indicates the variation curve of the initial reflectivity of the 60-layer thin film with respect to the wavelength. Figure 8 The mark 844 in the column (c) in FIG. 8 indicates the variation curve of the measured reflectivity of the 60-layer thin film with respect to the wavelength, and the mark 846 indicates the variation curve of the fitted reflectivity of the 60-layer thin film with respect to the wavelength. Figure 8 The mark 828 in the column (b) in FIG. 23 indicates the variation curve of the measured reflectivity of the thin film 232 to be tested relative to the wavelength, and the mark 830 indicates the variation curve of the initial reflectivity of the thin film 232 to be tested relative to the wavelength. Figure 8 The mark 848 in the column (c) in FIG. 1 indicates the variation curve of the measured reflectivity of the 232-layer film to be tested relative to the wavelength, and the mark 850 indicates the variation curve of the fitted reflectivity of the 232-layer film to be tested relative to the wavelength. Figure 8 It can be seen that for 2 thin film layers, 60 thin film layers and 232 thin film layers, the reflectivity calculated according to the method disclosed in the present invention can well fit the measured reflectivity.

[0096] The following is combined with Table 1 to illustrate the comparison of the time consumed by the method for measuring a thin film to be measured disclosed herein and the traditional method for measuring a thin film.

[0097] Table 1

[0098] Multilayer film layers 2 60 232 Traditional method iteration time 0.034s 3.196s 67.498s Each iteration time of our method 0.042s 0.166s 0.924s

[0099] It can be seen from Table 1 above that for the traditional method for measuring the optical parameters of thin films, the iteration time for measuring 2 thin film layers, 60 thin film layers and 232 thin film layers is 0.034s, 3.196s and 67.498s respectively. It can be seen that as the number of layers of the thin film to be measured increases, the time required for each iteration increases significantly. Compared with the traditional method for measuring the optical parameters of thin films, the iteration time for measuring 2 thin film layers, 60 thin film layers and 232 thin film layers of the method disclosed in the present invention is 0.042s, 0.166s and 0.924s respectively. Therefore, as the number of layers increases, the time required for each iteration of the method disclosed in the present invention is greatly reduced.

[0100] In the above scheme, by calculating the measured spectrum data indicating at least the reflectivity or transmittance of the film to be tested at multiple wavelengths of the incident light based on the acquired light source spectrum data and the emission spectrum data, and by inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the film to be tested (for example, the forward propagation characteristics of the thin film network model indicate the transmission characteristics of light in the film to be tested), it is possible to generate a simulated spectrum close to the measured spectrum data of the film to be tested based on the input of the thin film network model (i.e., the light source spectrum). And based on the parameters of the thin film network model to determine the refractive index or transmittance of the film to be tested, the present disclosure no longer requires additional multiple electromagnetic simulation calculations to obtain the convergence direction, so even if a multi-layer film to be tested is measured, the time required for each iteration can be greatly reduced, and the measurement of multi-layer films can be quickly achieved. Even for films with more than ten layers or even more layers of thin film layers and different thicknesses, the present disclosure can also quickly measure their optical parameters.

[0101] The following will combine Fig. 9 and Fig.10 The method 900 for determining an abnormal film layer of a film to be tested will be described in detail. Fig. 9 A flow chart of a method 900 for determining an abnormal thin film layer of a thin film to be tested according to an embodiment of the present disclosure is shown. Fig.10 FIG. 1 is a schematic diagram of a method 1000 for determining an abnormal film layer of a film to be tested according to an embodiment of the present disclosure. It should be understood that the method 900 may be, for example, Fig.11 The electronic device 1100 described above is executed. Figure 2The method 900 is executed at the described computing device 230. It should be understood that the method 900 may also include additional components and actions not shown and / or may omit the components and actions shown, and the scope of the present disclosure is not limited in this respect.

[0102] At step 902 , the computing device 230 determines a distribution range of the thickness of the film body based on the determined thickness of the film body of each film layer.

[0103] At step 904 , the computing device 230 determines whether there is a thickness of the thin film body that deviates from the distribution range by more than a predetermined threshold.

[0104] At step 906 , if the computing device 230 determines that the thickness of the film body has a deviation from the distribution range exceeding a predetermined threshold, the film layer corresponding to the film body is determined to be an abnormal film layer.

[0105] At step 908 , if the computing device 230 does not have a thickness of the film body whose deviation from the distribution range exceeds a predetermined threshold, it is determined that the film layer corresponding to the film body is not an abnormal film layer.

[0106] For example, Fig.10 Column (a) (i.e., the left part) of FIG. 1 shows a multilayer film 1010 to be tested. For example, the film layer 1012 is abnormal during processing (e.g., the thickness is processed by 5 nm more). If the computing device 230 determines that there is no thickness of the film body whose deviation from the distribution range exceeds a predetermined threshold, it is determined that there is no abnormal film layer. For example, Fig.10 The upper half 1020 of the column (b) shows a curve of the thickness of the normal film to be tested relative to the number of film layers. If it is determined that there is no thickness of the film body whose deviation from the distribution range 1022 exceeds a predetermined threshold, it is determined that there is no abnormal film layer.

[0107] By adopting the above means, the present disclosure can quickly and accurately identify abnormal film layers in a multi-layer composite film to be tested.

[0108] Fig.11 The block diagram of an electronic device (or computing device) 1100 suitable for implementing the embodiments of the present disclosure is schematically shown. The device 1100 may be a device for implementing the execution Figure 3 , Fig. 9 The apparatus of the method 300, 900 shown. Fig.11As shown, the device 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 1102 or loaded from a storage unit 1108 to a random access memory (RAM) 1103. In the RAM, various programs and data required for the operation of the device 1100 can also be stored. The CPU, ROM, and RAM are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0109] Multiple components in the device 1100 are connected to an input / output (I / O) 1105, including: an input unit 1106, an output unit 1107, and a storage unit 1108. The central processing unit 1101 performs the various methods and processes described above, such as performing methods 300 and 900. For example, in some embodiments, methods 300 and 900 can be implemented as computer software programs, which are stored in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 1100 via ROM and / or communication unit 1109. When the computer program is loaded into RAM and executed by the CPU, one or more operations of the methods 300 and 900 described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform one or more actions of methods 300 and 900 in any other appropriate manner (for example, by means of firmware).

[0110] It should be further noted that the present disclosure may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0111] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0113] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as C language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0114] Various aspects of the present disclosure are described herein with reference to the flowchart and / or step diagram of the method, device (system), and computer program product according to the embodiments of the present disclosure. It should be understood that each of the steps in the flowchart and / or step diagram and the combination of the steps in the flowchart and / or step diagram can be implemented by computer-readable program instructions.

[0115] These computer-readable program instructions can be provided to a processor in a voice interaction device, a general-purpose computer, a special-purpose computer, or a processing unit of other programmable data processing devices, thereby producing a machine, so that when these instructions are executed by a processing unit of a computer or other programmable data processing device, a device that implements the functions / actions specified in one or more steps in a flowchart and / or a step diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions enable a computer, a programmable data processing device, and / or other devices to work in a specific manner, so that a computer-readable medium storing instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more steps in a flowchart and / or a step diagram.

[0116] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more steps in the flowchart and / or step diagram.

[0117] The flowcharts and step diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each step in the flowchart or step diagram may represent a module, a program segment or a portion of an instruction, which contains one or more executable instructions for implementing a specified logical function.

[0118] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

[0119] The above are only optional embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for measuring optical parameters of a film to be measured, comprising: Acquire light source spectrum data of incident light of multiple wavelengths emitted by the light source; Acquiring emission spectrum data about the film to be tested, wherein the emission spectrum data is generated by measuring the film to be tested including multiple film layers using the incident light; Calculating measured spectrum data based on the light source spectrum data and the emission spectrum data, wherein the measured spectrum data at least indicates the reflectivity or transmittance of the film to be tested under multiple wavelengths of incident light; Inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the thin film to be tested, so that the simulated spectrum data about the thin film to be tested output by the thin film network model is close to the measured spectrum data; as well as Based on the parameters of the thin film network model, at least one of the refractive index of the thin film to be measured and the thickness of the thin film body of the thin film layer is determined, The thin film network model includes a film main body network layer and an interface network layer, wherein the film main body network layer is used to simulate the propagation characteristics of light in the film main body of the thin film layer, and the interface network layer is used to simulate the propagation characteristics of light in the interface layer between adjacent thin film layers. The forward propagation characteristics of the thin film network model indicate the transmission characteristics of light in the thin film to be tested. The film body network layer is used to convert the field amplitude characteristic vector at the input side of the film body network layer into the field amplitude characteristic vector at the output side of the film body network layer via the film body conversion matrix, and the film body conversion matrix is ​​associated with the additional phase of the light propagating in the film body of the thin film layer simulated by the film body network layer, wherein the interface network layer is used to convert the field amplitude characteristic vector at the input side of the interface network layer into the field amplitude characteristic vector at the output side of the interface network layer via the interface matrix, and The membrane main network layer is configured to be similar to a neuron layer of a neural network, and the interface network layer is configured to be similar to a weight layer of a neural network. 2 . The method according to claim 1 , wherein the film to be tested comprises at least 10 film layers, and the thickness of the film bodies of the film layers is the same or different.

3. The method according to claim 2, wherein the thickness of the film body of each film layer is included in the parameters of the film network model, and the film to be tested is used for a semiconductor device.

4. The method according to claim 1, further comprising: Determining a distribution range of the thickness of the film body based on the determined thickness of the film body of each film layer; determining whether there is a thickness of the film body that deviates from the distribution range by more than a predetermined threshold; and In response to the existence of a thickness of a film body whose deviation from the distribution range exceeds a predetermined threshold, the film layer corresponding to the film body is determined to be an abnormal film layer.

5. The method according to claim 1, further comprising: Obtaining the number of thin film layers and the initial thickness of the thin film layers included in the thin film to be tested; as well as Based on the number of layers and the initial thickness of the thin film layer, the membrane main network layer and the interface network layer of the thin film network model are configured, and the back propagation process of the thin film network model is configured based on the back propagation characteristics of the neural network.

6. The method of claim 1, wherein the thin film network model is trained via: The thin film network model is trained with the measured spectral data as a training target, so that the simulated spectral data fits the measured spectral data by adjusting the parameters of the thin film network model.

7. The method according to claim 1, wherein inputting the light source spectrum data into a thin film network model for simulating the propagation characteristics of light in the thin film to be tested comprises: The measured light source reference data is input into the thin film network model, and the light source spectrum data includes the measured light source reference data.

8. A computing device comprising: a memory configured to store one or more computer programs; as well as A processor is coupled to the memory and configured to execute the one or more programs to enable the computing device to perform the method according to any one of claims 1-7.

9. A non-transitory machine-readable storage medium having machine-readable program instructions stored thereon, wherein the machine-readable program instructions are configured to cause a machine to execute the steps of the method according to any one of claims 1 to 7.

10. A system for measuring optical constants of thin films, comprising: A spectrum measurement device configured to measure the film to be measured based on incident light of multiple wavelengths so as to generate output spectrum data about the film to be measured; as well as A computing device configured to operatively perform the method according to any one of claims 1-7.

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