Automatic method and system for measuring the thickness of a protective film

Abstract

The present application relates to the technical field of thickness measurement, and particularly relates to a method and system for automatically measuring the thickness of a protective film, which solves the technical problem that in the prior art, the highly overlapped reflected light signals of the interfaces of the multi-layer composite film in the time domain and the frequency domain cannot be accurately separated, and thus the thickness of the composite film cannot be determined. The method comprises: obtaining the overlapped reflected light signals of the protective film and the optical parameters of the multi-layer materials constituting the protective film; dividing the overlapped reflected light signals into multiple signal segments according to the time domain characteristics of the overlapped reflected light signals; combining multiple continuous signal segments to obtain a single reflected light signal corresponding to each material layer according to the frequency domain characteristic similarity between the two signal segments adjacent in time sequence; and determining the thickness of each layer of the protective film and obtaining the total thickness according to the single reflected light signal corresponding to each material layer and the optical parameters.

Description

Technical Field

[0001] This invention relates to the field of thickness measurement technology, and specifically to an automatic thickness measurement method and system for protective films. Background Technology

[0002] Protective films for hygiene products and disposable medical supplies are a critical line of defense for ensuring the sterility and safety of these products. Their thickness and uniformity directly affect core properties such as physical strength, air permeability, and liquid barrier properties. Insufficient thickness or uneven distribution can lead to protective failure and affect subsequent processing. Therefore, high-precision automated thickness measurement is necessary during production. Currently, the industry commonly uses non-contact technologies such as laser measurement and spectral interferometry for thickness detection to maintain the sterility and integrity of the film.

[0003] Spectroscopic interferometry, a mainstream method, estimates thickness by analyzing the interference spectra formed by the reflection of broadband light from the upper and lower surfaces of a thin film. Hygiene and medical protective films are often multi-layered composite structures of different materials, with each layer's interface generating reflected light, resulting in complex superposition of spectral signals received by the detector. Current technologies primarily rely on changes in intuitive parameters such as frequency and amplitude to distinguish the reflected light signals from different material interfaces. Due to the extreme thinness of each material layer, the reflected light signals highly overlap in both the time and frequency domains, making it difficult for existing methods to effectively separate the signals from each layer from the interference fringes. Consequently, it is impossible to calculate the accurate thickness of the composite film based on the material's optical properties. Summary of the Invention

[0004] To address the technical problem in existing technologies where the reflected light signals from the interfaces of multilayer composite films are highly aliased in the time and frequency domains, making it impossible to accurately separate the signals of each layer and thus difficult to determine the thickness of the composite film, the present invention aims to provide an automatic thickness measurement method and system for protective films. The specific technical solution adopted is as follows:

[0005] In a first aspect, an automatic thickness measurement method for a protective film is provided, comprising: acquiring the aliased reflected light signal of the protective film and the optical parameters of the multilayer materials constituting the protective film; dividing the aliased reflected light signal into multiple signal segments according to the temporal characteristics of the aliased reflected light signal; combining multiple consecutive signal segments according to the frequency domain similarity between two temporally adjacent signal segments to obtain a single reflected light signal corresponding to each material layer; and determining the thickness of each layer of the protective film and obtaining the total thickness based on the single reflected light signal and optical parameters corresponding to each material layer.

[0006] Based on the above technical solution, in the automatic thickness measurement method for protective films provided by this invention, by acquiring the aliased reflected light signals of multilayer composite protective films and the optical parameters of each layer, the aliased signals are first divided into multiple signal segments according to the time domain characteristics. Then, the signal segments are accurately grouped by the frequency domain characteristics similarity of adjacent time-series signal segments, thereby effectively separating the single reflected light signal corresponding to each material layer. This solves the problem of difficulty in distinguishing the signals of each layer caused by the aliasing of reflected light signals at each interface of multilayer composite films. Finally, the thickness of each layer and the total thickness are determined by combining the single reflected light signal and the optical parameters, ensuring the accuracy and reliability of the thickness measurement of multilayer composite protective films.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for dividing the aliased reflected light signal into multiple signal segments based on the temporal characteristics of the aliased reflected light signal specifically includes: analyzing the degree of change of the temporal characteristics of the aliased reflected light signal at each time point relative to adjacent time points; if the degree of change at the target time point is greater than or equal to a preset change threshold, marking the target time point as the time of reflection light change; and dividing the aliased reflected light signal into multiple consecutive signal segments using the time of reflection light change as the endpoint.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for analyzing the degree of change of temporal features of the aliased reflected light signal at each moment relative to adjacent moments specifically includes: extracting temporal features from the aliased reflected light signal and arranging them in chronological order to form a temporal feature sequence; and determining the degree of change of temporal features of the aliased reflected light signal at each moment based on the feature changes of temporal features at adjacent moments in the temporal feature sequence.

[0009] In conjunction with the first aspect above, in one possible implementation, the aforementioned time-domain features include parameters showing how various reflected light signals change over time.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for combining multiple consecutive signal segments to obtain a single reflected light signal corresponding to each material layer based on the frequency domain feature similarity between two temporally adjacent signal segments specifically includes: extracting frequency domain features for each signal segment to obtain a frequency domain feature sequence for each signal segment at multiple frequency levels; determining the frequency domain feature similarity between two temporally adjacent signal segments based on the sequence similarity of the frequency domain feature sequences of the two temporally adjacent signal segments; if the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, determining that the two signal segments belong to the same material layer; if the frequency domain feature similarity between two temporally adjacent signal segments is less than the preset similarity threshold, determining that the two signal segments belong to different material layers.

[0011] In conjunction with the first aspect above, in one possible implementation, the method for combining multiple consecutive signal segments to obtain a single reflected light signal corresponding to each material layer based on the frequency domain feature similarity between two temporally adjacent signal segments specifically includes: extracting frequency domain features for each signal segment to obtain a frequency domain feature sequence for each signal segment at multiple frequency levels; determining the frequency domain feature similarity between two temporally adjacent signal segments based on the sequence similarity of the frequency domain feature sequences of two temporally adjacent signal segments and the frequency domain feature differences at each frequency level; if the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, determining that the two signal segments belong to the same material layer; if the frequency domain feature similarity between two temporally adjacent signal segments is less than the preset similarity threshold, determining that the two signal segments belong to different material layers.

[0012] In conjunction with the first aspect above, in one possible implementation, the aforementioned frequency domain features include the phase information of each signal segment; the method for extracting frequency domain features for each signal segment specifically includes: processing each signal segment through wavelet transform to obtain wavelet coefficients in complex form; and determining the phase information of each signal segment at multiple frequency levels and times based on the wavelet coefficients.

[0013] In conjunction with the first aspect above, in one possible implementation, the aforementioned optical parameters include the refractive index of each material layer; the method for determining the thickness of each layer of the protective film based on the single reflected light signal corresponding to each material layer and the optical parameters specifically includes: performing a frequency domain transformation on the single reflected light signal corresponding to each material layer to obtain a frequency domain signal, and determining the peak frequency from the frequency domain signal; and determining the thickness of each layer based on the peak frequency and refractive index corresponding to each material layer.

[0014] In conjunction with the first aspect above, in one possible implementation, the method for determining the peak frequency from the frequency domain signal specifically includes: identifying characteristic peak regions related to the material layer thickness from the frequency domain signal using a peak detection algorithm; interpolating the frequency domain signal to obtain a high-resolution signal; the interpolation interval of the characteristic peak regions is smaller than that of other regions; and curve fitting the high-resolution signal to determine the peak frequency.

[0015] Secondly, an automatic thickness measurement system for protective films is provided, comprising: a signal acquisition unit, a parameter storage unit, a signal processing unit, and an automatic thickness measurement unit; the signal acquisition unit is used to acquire the aliased reflected light signal of the protective film; the parameter storage unit is used to store the optical parameters of the multilayer materials constituting the protective film; the signal processing unit is used to divide the aliased reflected light signal into multiple signal segments according to the temporal characteristics of the aliased reflected light signal; the signal processing unit is also used to combine multiple consecutive signal segments to obtain a single reflected light signal corresponding to each material layer according to the frequency domain similarity between two temporally adjacent signal segments; the automatic thickness measurement unit is used to determine the thickness of each layer of the protective film and obtain the total thickness according to the single reflected light signal and optical parameters corresponding to each material layer.

[0016] Thirdly, an automatic thickness measurement device for protective films is provided, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is configured to execute the instructions to perform the actions described in the first aspect and any possible implementation thereof. This automatic thickness measurement device for protective films can be an electronic device or a chip within an electronic device.

[0017] Fourthly, a computer-readable storage medium is provided, which stores instructions that, when executed on an automatic thickness measuring device for protective films, cause the automatic thickness measuring device for protective films to perform the actions described in the first aspect and any possible implementation thereof.

[0018] Fifthly, a computer program product containing instructions is provided that, when the computer program product is run on an automatic thickness measuring device for protective films, causes the automatic thickness measuring device for protective films to perform the actions described in the first aspect and any possible implementation thereof.

[0019] The present invention has the following beneficial effects:

[0020] By acquiring the aliased reflected light signals and optical parameters of each layer of the multilayer composite protective film, the aliased signal is first divided into multiple signal segments based on the time-domain characteristics. Then, the signal segments are accurately grouped by the frequency domain similarity of adjacent time-series signal segments, thereby effectively separating the single reflected light signal corresponding to each material layer. This solves the problem of difficulty in distinguishing signals between layers caused by the aliasing of reflected light signals at each interface of the multilayer composite film. Finally, the thickness of each layer and the total thickness are determined by combining the single reflected light signal and optical parameters, ensuring the accuracy and reliability of the thickness measurement of the multilayer composite protective film. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0022] Figure 1 This is a system structure diagram of an automatic thickness measurement system for protective films provided in one embodiment of the present invention;

[0023] Figure 2 This is a flowchart of an automatic thickness measurement method for protective films according to an embodiment of the present invention;

[0024] Figure 3 A flowchart illustrating another method for automatic thickness measurement of protective films according to an embodiment of the present invention;

[0025] Figure 4 A flowchart illustrating another method for automatic thickness measurement of protective films according to an embodiment of the present invention;

[0026] Figure 5 A flowchart illustrating another method for automatic thickness measurement of protective films according to an embodiment of the present invention;

[0027] Figure 6 A flowchart illustrating another method for automatic thickness measurement of protective films according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the hardware structure of an automatic thickness measuring device for protective films provided in one embodiment of the present invention. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic thickness measurement method and system for protective films proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automatic thickness measurement method and system for protective films provided by the present invention.

[0032] Please see Figure 1 The diagram shows a system structure of an automatic thickness measurement system for protective films according to an embodiment of the present invention. The automatic thickness measurement system for protective films includes: a signal acquisition unit 1, a parameter storage unit 2, a signal processing unit 3, and an automatic thickness measurement unit 4.

[0033] Among them, the signal acquisition unit 1 is the basis for the system to acquire raw data. It is responsible for acquiring the aliased reflected light signal of the protective film under test, and its output signal provides the core processing object for the signal processing unit 3.

[0034] In some implementations, the signal acquisition unit 1 includes a sensor submodule 11, a light source submodule 12, and a signal conversion submodule 13.

[0035] The sensor submodule 11 uses a spectral interferometric thickness sensor, which is vertically and stably set above the surface of the protective film to ensure the stability of the light wave irradiation path; the light source submodule 12 emits a broadband light signal, which generates multiple reflected light beams and forms aliasing when it irradiates the interfaces of different materials in the multilayer composite protective film; the signal conversion submodule 13 is connected to the sensor submodule 11 and converts the received aliased reflected light signal into an electrical signal form, which is synchronously transmitted to the signal processing unit 3 for subsequent processing.

[0036] The parameter storage unit 2 provides key reference data for thickness calculation, and the stored optical parameters directly support the thickness calculation of the automatic thickness measurement unit 4.

[0037] In some implementations, the parameter storage unit 2 includes a parameter input submodule 21 and a classification storage submodule 22:

[0038] The parameter input submodule 21 receives destructive test data from the protective film material sample to obtain the optical parameters (the core being the refractive index) of each material layer, ensuring the accuracy and specificity of the parameters. The classification and storage submodule 22 classifies and archives the optical parameters according to the material layer number, establishing a standardized parameter database. When the automatic thickness measurement unit 4 performs thickness calculation, it can quickly retrieve the refractive index data of the corresponding material layer, improving the calculation efficiency.

[0039] The signal processing unit 3 is the core processing module of the system. It receives the mixed reflected light signals from the signal acquisition unit 1 and separates the single reflected light signals corresponding to each material layer through layer processing, providing accurate calculation basis for the automatic thickness measurement unit 4.

[0040] In some implementations, the signal processing unit 3 includes a signal segmentation submodule 31, a frequency domain feature extraction submodule 32, and a signal combination submodule 33.

[0041] The signal segmentation submodule 31 first extracts time-domain features such as amplitude and frequency from the aliased reflected light signal and forms a time sequence. It calculates the degree of change of time-domain features at each moment and marks the moment when the preset change threshold is reached as the moment of reflected light change. Then, it divides the signal into multiple continuous signal segments with this moment as the endpoint. The frequency domain feature extraction submodule 32 performs frequency domain processing on each signal segment and obtains complex wavelet coefficients through wavelet transform. Then, it determines the phase information and other frequency domain features of each signal segment at multiple frequency levels and times, forming a frequency domain feature sequence. The signal combination submodule 33 calculates the frequency domain feature similarity of temporally adjacent signal segments (based on feature sequence similarity or a combination of similarity and frequency domain feature differences). It determines the signal segment affiliation according to the preset similarity threshold, combines continuous signal segments belonging to the same material layer, obtains a single reflected light signal corresponding to each material layer, and transmits it to the automatic thickness measurement unit 4.

[0042] The automatic thickness measurement unit 4 is the system's result output module. It combines the single reflected light signal from the signal processing unit 3 and the optical parameters from the parameter storage unit 2 to complete the calculation of the thickness of each layer and the total thickness.

[0043] In some implementations, the automatic thickness measurement unit 4 includes a frequency domain conversion submodule 41, a peak value determination submodule 42, and a thickness calculation submodule 43.

[0044] The frequency domain conversion submodule 41 receives the single reflected light signal from each material layer, performs frequency domain conversion on it, and converts the time domain signal into a frequency domain signal that is easy to extract features from. The peak determination submodule 42 identifies the characteristic peak region related to the thickness through the peak detection algorithm, performs interpolation processing on the frequency domain signal (the interpolation interval of the characteristic peak region is smaller than that of other regions), and after obtaining the high-resolution signal, accurately determines the peak frequency through curve fitting. The thickness calculation submodule 43 retrieves the refractive index of the corresponding material layer in the parameter storage unit 2, calculates the thickness of each material layer in combination with the peak frequency, and then accumulates the thickness of all material layers to finally output the total thickness of the protective film.

[0045] Please see Figure 2 The diagram illustrates a flowchart of an automatic thickness measurement method for a protective film according to an embodiment of the present invention. This automatic thickness measurement method for a protective film includes:

[0046] S1. Obtain the aliased reflected light signal of the protective film and the optical parameters of the multilayer materials that make up the protective film.

[0047] In some implementations, a signal acquisition device is pre-built to obtain the aliased reflected light signal of the protective film. The spectral interferometric thickness sensor is vertically and stably mounted above the surface of the protective film to be measured, ensuring that the vertical distance between the sensor and the film surface remains constant to avoid distortion of the reflected light signal acquisition due to optical path offset.

[0048] Simultaneously, a broadband light source and a spectrometer with matched wavelengths are configured. The broadband light source emits continuous-wavelength light waves towards the protective film. When these light waves illuminate the interfaces between the materials of the multilayer composite protective film, reflected light is generated at each interface. Due to the thinness of each material layer and the close proximity of the interfaces, multiple reflected beams overlap along the propagation path, forming aliased reflected light signals. A spectral interferometric thickness sensor receives these aliased reflected light signals in real time and converts them into electrical signals. The spectrometer, by matching the wavelength of the broadband light source, filters out clutter interference, ensuring that the acquired aliased reflected light signals have a high signal-to-noise ratio. This electrical signal of the aliased reflected light is then transmitted to the signal processing unit to provide raw data for subsequent signal segmentation.

[0049] To ensure the accuracy of subsequent thickness calculations, it is also necessary to obtain the optical parameters of the multilayer materials that make up the protective film. A material sample identical to the protective film under test is selected, and its optical properties are detected using a destructive testing method. The testing process simulates the light wave irradiation environment used in actual thickness measurement, obtaining the optical parameters of each material layer under the corresponding light waveband. The obtained optical parameters are categorized and organized according to the material layer number, forming a standardized set of optical parameters, and stored in the database of the parameter storage unit. This categorized storage method allows for rapid retrieval of parameters for the corresponding material layer during subsequent thickness calculations, avoiding parameter confusion. These optical parameters will serve as the core reference for subsequent thickness calculations, directly affecting the accuracy of single-layer thickness and total thickness calculations.

[0050] Among the optical parameters related to the thickness measurement of protective films, refractive index is the core. Refractive index is the ratio of the speed of light propagation in a material to its speed of propagation in a vacuum, and it is the core basis for thickness calculation, directly related to the conversion relationship between the peak value of the reflected light signal and the thickness. Destructive testing can refer to the ASTM D1003 standard, using an ellipsometer to measure the refractive index.

[0051] Optionally, optical parameters may also include key parameters such as extinction coefficient, dispersion relation, and interface reflectivity: the extinction coefficient reflects the degree of absorption of light waves in the thickness measurement band by the material. When the thin film has significant light absorption, this parameter is needed to correct the calculation deviation caused by signal attenuation; the dispersion relation describes the variation of refractive index with the wavelength of light, adapts to the multi-band characteristics of broadband light sources, and ensures the consistency of thickness calculation at different wavelengths; the interface reflectivity is the inherent reflectivity data of the interface between each material layer, which helps to determine the source of the reflected light signal and optimize the accuracy of signal segmentation.

[0052] S2. Based on the time-domain characteristics of the aliased reflected light signal, the aliased reflected light signal is divided into multiple signal segments.

[0053] The reflected light signal of multilayer thin films is essentially a superposition of layered reflections, meaning that the interfaces between different material layers generate independent reflected light. These reflected lights arrive sequentially along their propagation paths in the time dimension, causing abrupt changes in the temporal characteristics (such as amplitude and frequency) of the aliased signal at the interfaces. In contrast, the reflected light from the same material layer has consistent propagation characteristics and relatively stable temporal features.

[0054] The core function of time-domain segmentation is to initially separate aliased signals. By analyzing the degree of change in time-domain characteristics and marking the signal abrupt change times at the interface, the aliased signal can be divided into multiple continuous and stable signal segments. Each signal segment either corresponds to the reflected light from a single material layer or is a local superposition of reflected light from adjacent layers, thus avoiding interference from direct analysis of the entire aliased signal segment.

[0055] In some implementations, temporal features such as amplitude and frequency are extracted from the aliased reflected light signal and formed into a temporal feature sequence in chronological order. The degree of change of the temporal features at each moment relative to the adjacent moments is calculated. The moment when the degree of change reaches a preset change threshold is marked as the moment of reflected light change. The aliased reflected light signal is divided into multiple continuous signal segments with this moment as the endpoint. This effectively isolates the mutual interference of reflected signals from different material interfaces and provides structured basic data for subsequent frequency domain feature extraction and signal combination.

[0056] S3. Based on the frequency domain feature similarity between two temporally adjacent signal segments, multiple consecutive signal segments are combined to obtain a single reflected light signal corresponding to each material layer.

[0057] Reflected light from the same material layer exhibits highly consistent frequency domain characteristics (especially phase information at multiple frequency levels) due to its fixed refractive index and propagation path. Reflected light from different material layers, however, displays significantly different frequency domain characteristics due to differences in physical properties. Therefore, by calculating the frequency domain similarity of adjacent signal segments, it is possible to accurately determine whether these signal segments originate from the same material layer. This allows for the integration of dispersed signal segments from the same layer into a complete, single reflected light signal, ultimately achieving the separation of reflected signals from each material layer.

[0058] In some implementations, frequency domain features are extracted from each signal segment to form a frequency domain feature sequence (e.g., including phase information at multiple frequency levels). The frequency domain feature similarity of temporally adjacent signal segments is calculated. Based on a preset similarity threshold, continuous signal segments belonging to the same material layer are combined to obtain a single reflected light signal corresponding to each material layer. This effectively solves the problem of overlapping reflected light signals in multilayer composite films, which makes it difficult to separate layers. This provides a reliable signal basis for accurately calculating each layer and the total thickness.

[0059] S4. Based on the single reflected light signal and optical parameters corresponding to each material layer, determine the thickness of each layer of the protective film and obtain the total thickness.

[0060] In some implementations, the single reflected light signal of each material layer is frequency-domain converted, and the peak frequency is accurately determined by differential interpolation and curve fitting. The thickness of each layer is calculated by combining the refractive index of the material layer and the total thickness is obtained by summing them up. This achieves accurate quantification of the thickness of the multilayer composite protective film and provides reliable data for the quality control of film production.

[0061] Based on the above technical solution, by acquiring the aliased reflected light signals and optical parameters of each layer of the multilayer composite protective film, the aliased signal is first divided into multiple signal segments according to the time domain characteristics. Then, the signal segments are accurately grouped by the frequency domain similarity of adjacent time-series signal segments, thereby effectively separating the single reflected light signal corresponding to each material layer. This solves the problem of difficulty in distinguishing the signals of each layer caused by the aliasing of reflected light signals at each interface of the multilayer composite film. Finally, the thickness of each layer and the total thickness are determined by combining the single reflected light signal and optical parameters, ensuring the accuracy and reliability of the thickness measurement of the multilayer composite protective film.

[0062] In one possible implementation, combining Figure 2 ,like Figure 3 As shown, the method in S2 above can be specifically implemented through the following steps S21 to S23, which are explained in detail below:

[0063] S21. For the aliased reflected light signal at each time moment, analyze the degree of change of the temporal characteristics relative to adjacent time moments.

[0064] In some implementations, temporal features are extracted from the aliased reflected light signals and arranged in chronological order to form a temporal feature sequence. Then, based on the changes in temporal features between adjacent time points in the temporal feature sequence, the degree of change in the temporal features of the aliased reflected light signals at each time point is determined.

[0065] Among them, the time-domain characteristics include various parameters of how the reflected light signal changes over time, such as the amplitude, frequency, and signal strength of the reflected light signal.

[0066] First, temporal features are extracted from the aliased reflected light signal. Based on the characteristics of the reflected light signal from the protective film, key parameters that comprehensively reflect the signal's fluctuations over time are selected as temporal features. These include parameters such as the amplitude, frequency, and intensity of the reflected light signal, which vary over time. These parameters capture the temporal fluctuation patterns of the aliased reflected light signal from different dimensions, ensuring the comprehensiveness of feature extraction and avoiding biases in change judgments due to the absence of a single feature.

[0067] Next, the extracted temporal features at each moment are arranged sequentially according to the order of signal acquisition, forming a continuous temporal feature sequence. This arrangement transforms the originally scattered single-moment feature data into a structured temporal data chain, fully preserving the trajectory of the aliased reflected light signal across time. This provides clear data support for feature comparisons at subsequent adjacent moments, avoiding analytical errors caused by disordered temporal order.

[0068] Then, the feature changes of time-domain features at adjacent time points in the time-domain feature sequence are calculated. For each time-domain feature (such as amplitude and frequency), the difference between the feature value at the current time point and the corresponding feature value at the previous time point is calculated one by one to obtain the individual feature change of each time-domain feature at each time point. This calculation can quantify the feature differences between adjacent time points, providing basic data for subsequent comprehensive judgment of the degree of change.

[0069] Finally, the degree of change in temporal features at each time point is determined by combining the changes in features. First, the average characteristic value of each temporal feature within the current analysis period is calculated. Then, the relative difference between the change in each feature and its corresponding average characteristic value at each time point is calculated. By integrating the relative differences of all temporal features, the comprehensive degree of change in the temporal features of the aliased reflected light signal at each time point is obtained. This integration process avoids interference from a single feature mutation on the overall judgment, ensuring that the assessment of the degree of change is more objective and reliable, accurately reflecting the overall fluctuation of the signal at each time point. Taking time t as an example, it is expressed as:

[0070]

[0071] In the formula, This represents the change in the f-th temporal feature of the aliased reflected light signal at time t relative to time t-1, i.e., the eigenvalue of the f-th temporal feature of the aliased reflected light signal at time t. With the eigenvalue at time t-1 The difference.

[0072] The eigenvalue representing the f-th temporal characteristic of the aliased reflected light signal is the average value at all detection times.

[0073] Calculate the ratio of the absolute value of the change in the f-th time-domain feature to its average value to obtain the relative degree of change of the f-th time-domain feature (the ratio of the change in the same dimension to the average value; the result is dimensionless). The characteristic values ​​of reflected light (such as amplitude and frequency) will not be zero under normal measurement scenarios (no signal, no measurement), therefore the denominator will not be zero.

[0074] This represents the total number of time-domain feature types of aliased reflected light signals.

[0075] The relative changes of all F time-domain features are summed and then divided by the total number of features to obtain the average relative change of all features at time t, which is the change of the aliased reflected light signal at time t relative to the signal at the previous time. .

[0076] S22. If the degree of change of the target time is greater than or equal to the preset change threshold, the target time is marked as the time of change of reflected light.

[0077] In some implementation methods, a preset change threshold is first determined. By combining a large amount of historical thickness measurement data of similar multilayer composite protective films, the maximum degree of change of the reflected light signal under normal fluctuations (not caused by material interfaces) is statistically analyzed. Based on this maximum value, a preset change threshold is set (e.g., the preset change threshold can be set to 0.1). This ensures that the threshold can effectively distinguish between normal fluctuations and drastic changes caused by material interfaces, while avoiding missed judgments due to an excessively high threshold or false judgments due to an excessively low threshold, thus providing an objective standard that fits the actual scenario for judgment.

[0078] Next, the temporal characteristic changes of the aliased reflected light signal at each moment are retrieved. The change data for each target moment are extracted sequentially to ensure that the analysis at each moment is supported by corresponding quantitative data, avoiding data omissions or disordered order that could affect the judgment results. The change degree at each target moment is compared one by one with a preset change threshold. Through point-to-point quantitative comparison, target moments whose change degree reaches or exceeds the threshold are accurately selected.

[0079] Finally, the target moment that meets the conditions after comparison is marked as the moment of change in reflected light. This moment is clearly marked in the form of a timestamp, clearly recording the time node of the signal change, thus making the signal change point corresponding to the material interface concrete.

[0080] S23. Using the moment of change of reflected light as the endpoint, divide the aliased reflected light signal into multiple continuous signal segments.

[0081] In some implementations, the first step is to organize all the marked moments of reflected light change. Following the acquisition time sequence of the aliased reflected light signals, all moments of reflected light change are arranged in ascending order to form an ordered sequence of reflected light change moments. This organization avoids segmentation misalignment caused by disordered time order, ensuring the temporal logic of each endpoint is consistent and laying the foundation for accurate segmentation.

[0082] Next, the complete time range of the aliased reflected light signal is determined. The start and end times of signal acquisition recorded by the signal acquisition unit are retrieved and integrated with the sorted sequence of reflected light change times to form a complete set of segmented time nodes. This set includes both the abrupt change times corresponding to the material interface and covers the entire signal acquisition cycle, avoiding the omission of signal segments or incomplete segmentation.

[0083] Subsequently, the aliased reflected light signal is segmented using the sorted reflection light change times as the dividing endpoints. Starting from the signal acquisition start time, the first reflection light change time is taken as the first dividing point, and the signal between the start time and this dividing point is captured as the first signal segment. Then, using this dividing point as the starting point, the next reflection light change time is taken as the next dividing point, and the signal between the two dividing points is captured as the second signal segment. This process continues until the signal between the last reflection light change time before the signal acquisition end time is captured, forming the final signal segment.

[0084] Ultimately, multiple continuous and independent signal segments are obtained. Each signal segment corresponds to a relatively stable time-domain signal in the aliased reflected light, effectively isolating the mutual interference of reflection signals from different material interfaces. This allows subsequent frequency domain feature extraction for a single signal segment to focus on the reflection characteristics of a single material layer, avoiding the impact of aliased signals on the accuracy of feature extraction.

[0085] Based on the above technical solution, by extracting the temporal features of the aliased reflected light signals and forming a time sequence, the degree of feature change at each moment is quantitatively analyzed, and the moment of reflected light change corresponding to the material interface is marked. In this way, multiple continuous and independent signal segments are divided, effectively isolating the mutual interference of reflected signals from different material interfaces. This provides a structured basic data that is regular and focuses on the reflection characteristics of a single material layer for subsequent frequency domain feature extraction, signal segment combination, and accurate thickness measurement, ensuring the accuracy and efficiency of subsequent processing steps.

[0086] In one possible implementation, combining Figure 2 ,like Figure 4 As shown, the method in S3 above can be specifically implemented through the following steps S311 to S314, which are explained in detail below:

[0087] S311. Extract frequency domain features for each signal segment to obtain frequency domain feature sequences for each signal segment at multiple frequency levels.

[0088] In some implementations, frequency domain features include phase information for each signal segment. Phase information characterizes the temporal distribution and propagation characteristics of the signal in the frequency domain. Methods for extracting phase information for each signal segment can include: processing each signal segment using wavelet transform to obtain complex wavelet coefficients. Then, based on the wavelet coefficients, the phase information of each signal segment at multiple frequency levels and times is determined.

[0089] First, multiple consecutive independent signal segments are retrieved, and each segment is preprocessed. By using a preset signal denoising algorithm (such as wavelet threshold denoising algorithm) to filter out clutter interference in the signal segments, the effective signal components related to the reflection of the material layer are retained. This can improve the purity of the signal segments, avoid clutter interference on subsequent frequency domain feature extraction, and ensure that the extracted features can truly reflect the reflection characteristics of the material layer.

[0090] Next, wavelet transform is used to decompose each preprocessed signal segment. A wavelet basis function (such as Morlet) adapted to the characteristics of the reflected light signal is selected, and the signal segment is decomposed into multiple different frequency levels according to frequency. In wavelet transform, frequency level refers to the sub-bands of different frequency ranges separated from the signal through wavelet decomposition. Each sub-band corresponds to a specific frequency interval, used to distinguish the characteristics of the signal in different frequency dimensions. Simultaneously, complex wavelet coefficients are output. In wavelet transform, complex wavelet coefficients are coefficients obtained through complex wavelet transform, composed of real and imaginary parts (represented as amplitude and phase), capable of simultaneously characterizing the energy intensity and phase information of the signal at each frequency level. The advantage of wavelet transform is that it can simultaneously preserve the time-domain and frequency-domain information of the signal. This decomposition achieves feature splitting of the signal segment in multiple frequency dimensions, while the complex wavelet coefficients contain amplitude and phase-related information of the signal at each frequency level.

[0091] Subsequently, the phase information of each signal segment is calculated based on the wavelet coefficients in complex form. Using the logic of complex number argument calculation (the argument of a complex number is the angle between the complex number and the positive real axis in the complex plane; the magnitude of this angle represents the phase value of the corresponding signal component), the wavelet coefficients at each frequency level are calculated to obtain the phase values ​​of the signal at different time points within that frequency level. These phase values ​​are then categorized by frequency level and arranged in chronological order to form a phase sequence corresponding to each frequency level. This calculation process visualizes the phase characteristics implicit in the wavelet coefficients, accurately capturing the phase differences in the reflected light signals from different material layers.

[0092] Finally, the phase sequences from each frequency level are integrated to form the frequency domain feature sequence for each signal segment. The phase sequences of the same signal segment at all frequency levels are integrated in ascending order of frequency to construct a structured frequency domain feature sequence. This sequence fully encompasses the phase variation patterns of the signal segment across multiple frequency levels. This integration process creates a unified comparison carrier for the dispersed frequency-level features, ensuring multi-dimensional and comprehensive feature comparison when calculating the frequency domain feature similarity of adjacent time-series signal segments, thus improving the accuracy of similarity judgment.

[0093] Optionally, frequency domain features also include key types such as amplitude spectrum, power spectral density, frequency centroid, and harmonic components, all of which are suitable for multilayer thin film thickness measurement scenarios.

[0094] The amplitude spectrum reflects the signal intensity of each frequency component and can demonstrate the energy distribution of the reflected light signal at different frequencies, thus aiding in the determination of signal validity. Its extraction methods include: performing Fourier transform or wavelet transform on the signal segment to obtain the complex coefficients corresponding to each frequency component; taking the modulus of the complex coefficients to obtain the signal intensity at different frequencies; and arranging them in frequency order to form the amplitude spectrum.

[0095] Power spectral density is used to quantify the distribution of signal power with frequency, filtering out clutter interference and highlighting frequency components related to material layer thickness. Its extraction method includes: based on the amplitude spectrum, squaring the amplitude at each frequency point and dividing by the frequency resolution (the frequency interval of the frequency domain transformation) to obtain the power distribution with frequency.

[0096] The frequency centroid represents the main energy concentration frequency of a frequency domain signal, which can quickly pinpoint the core frequency range associated with thickness and improve peak identification efficiency. Its extraction method includes: calculating the sum of the products of the frequency and the corresponding amplitude (or power) at each frequency point in the amplitude spectrum (or power spectral density), and then dividing by the sum of the amplitudes (or power) at all frequency points to obtain the core frequency of energy concentration.

[0097] Harmonic components reflect the frequency harmonic distribution of a signal. For thin films with poor thickness uniformity, thickness calculation deviations can be corrected through harmonic characteristics. The extraction method includes: first, determining the fundamental frequency of the signal (the core frequency directly related to the thickness of the material layer) through Fourier transform; then, extracting frequency components that are integer multiples of the fundamental frequency; and calculating the ratio of the amplitude of each harmonic to the amplitude of the fundamental frequency to obtain the harmonic distribution characteristics.

[0098] S312. Determine the frequency domain feature similarity between two temporally adjacent signal segments based on the sequence similarity of their frequency domain feature sequences.

[0099] In some implementations, the frequency domain feature sequences of all signal segments are first retrieved, and two consecutive signal segments in time sequence are selected as a set of comparison objects to ensure the temporal logic of the comparison objects and avoid judgment bias caused by cross-time period comparison.

[0100] Next, a sequence similarity algorithm suitable for the frequency domain feature sequence is selected. Combining the structured features of the phase sequence, the cosine similarity algorithm, Pearson correlation coefficient algorithm, or dynamic time warping (DTW) algorithm are selected. These algorithms can effectively measure the morphological fit between two sequences and adapt to the changing patterns of phase information.

[0101] Then, similarity is calculated for the frequency domain feature sequences of each pair of time-adjacent signal segments. The frequency domain feature sequences of the two signal segments at corresponding frequency levels are input into the selected algorithm, and the similarity is calculated one by one and obtained through normalization mapping at each frequency level. Taking the DTW algorithm as an example, after obtaining the DTW distance, it can be... The similarity is converted to the [0, 1] interval. Then, the average of the similarities across all frequency levels is taken to obtain the overall sequence similarity between the two signal segments. This transforms the multi-dimensional frequency domain features of the two signal segments into a single quantitative indicator, intuitively reflecting the degree of feature fit.

[0102] Finally, the calculated sequence similarity is directly determined as the frequency domain feature similarity between two temporally adjacent signal segments, expressed as:

[0103]

[0104] In the formula, This represents the sequence similarity between the frequency domain feature sequences corresponding to the x-th signal segment and the (x+1)-th signal segment at the a-th frequency level. The similarity value is [0, 1], and the larger the value, the higher the degree of fit between the sequences. Here, x and x+1 are the identifiers of two temporally adjacent signal segments. A is the total number of frequency levels.

[0105] Directly mapping actions using sequence similarity simplifies the calculation process and improves processing efficiency, while ensuring the core representational significance of similarity. This approach is suitable for scenarios with high requirements for thickness measurement efficiency and significant differences between thin film material layers.

[0106] In particular, if the frequency domain features include multiple types such as amplitude spectrum, power spectral density, frequency centroid, and harmonic components in addition to phase information, the sequence similarity can be calculated for each type of feature, and then the mean of the sequence similarities corresponding to multiple types can be used as the frequency domain feature similarity.

[0107] S313. If the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, it is determined that the two signal segments belong to the same material layer.

[0108] In some implementations, a preset similarity threshold is first determined. A large amount of historical thickness measurement data for similar multilayer composite protective films is collected, and temporally adjacent signal segments known to belong to the same material layer are extracted. The sequence similarity of their frequency domain characteristic sequences is calculated, and the minimum value of these similarities is used as the basic threshold. Then, considering the error range of normal signal fluctuations, a slight upward adjustment (e.g., 5%-10%) is made as the final preset similarity threshold (e.g., the preset similarity threshold can be set to 0.8).

[0109] Next, the frequency domain feature similarity of two temporally adjacent signal segments is retrieved and directly compared with a preset similarity threshold. If the similarity value is greater than or equal to the preset similarity threshold, it indicates that the frequency domain feature morphology of the two signal segments is highly consistent, reflecting the same reflected light characteristics, thus determining that the two signal segments belong to the same material layer.

[0110] S314. If the frequency domain feature similarity between two temporally adjacent signal segments is less than a preset similarity threshold, determine that the two signal segments belong to different material layers.

[0111] If the similarity value is less than the preset similarity threshold, it indicates that the frequency domain features of the two signal segments are significantly different, reflecting reflection characteristics originating from the reflection behavior of different material layers. This confirms that the two signal segments belong to different material layers. This judgment clearly defines the boundary node of the signal segments, avoiding the incorrect integration of reflection signals from different material layers and ensuring the purity of subsequent single reflection signals.

[0112] Based on the above technical solution, by extracting frequency domain feature sequences of multiple frequency levels from each signal segment, calculating the sequence similarity of temporally adjacent signal segments, and combining a preset threshold to determine whether they belong to the same material layer, efficient layering of multilayer composite thin film cascaded reflective light signals is achieved, resulting in a single material layer reflective light signal with a regular structure and clear characteristics. While ensuring processing efficiency, this effectively solves the problem of distinguishing signals by material layer, providing a reliable signal basis for subsequent accurate thickness measurement, and improving the accuracy of signal layering and the reliability of thickness calculation.

[0113] In another possible implementation, combining Figure 2 ,like Figure 5 As shown, the method in S3 above can be specifically implemented through the following steps S321 to S324, which are explained in detail below:

[0114] S321. Extract frequency domain features for each signal segment to obtain frequency domain feature sequences for each signal segment at multiple frequency levels.

[0115] The specific implementation method of S321 in this embodiment can be referred to the description of S311, and will not be repeated here.

[0116] S322. Determine the frequency domain feature similarity between two temporally adjacent signal segments based on the sequence similarity of their frequency domain feature sequences and the frequency domain feature differences at each frequency level.

[0117] In some implementations, the sequence similarity of the frequency domain feature sequences is calculated first. For details, please refer to the description in S312, which will not be repeated here.

[0118] Next, the frequency domain feature differences at each frequency level are calculated. For each frequency level, the frequency domain feature sequences corresponding to the two signal segments are extracted, and the average difference of the feature values ​​at the corresponding time points is calculated to obtain the quantified value of the frequency domain feature differences at that frequency level. This process accurately captures the feature detail differences in a single frequency dimension, compensates for the neglect of local differences by the overall sequence similarity, and improves the comprehensiveness of feature description.

[0119] Then, across all frequency levels, the maximum value of the phase difference characterization is selected, focusing on the most significant frequency level differences and filtering out interference from minor differences.

[0120] Finally, the maximum value is normalized to the frequency domain feature similarity. The comprehensive quantization result is mapped to the [0, 1] interval to obtain the frequency domain feature similarity between two temporally adjacent signal segments. The normalization process makes the similarity values ​​more comparable, facilitating subsequent comparison with a preset similarity threshold. This method improves the accuracy of similarity judgment by integrating overall fit and local detail differences. It is suitable for high-precision thickness measurement scenarios where thin film materials have insignificant interface differences, providing more reliable quantitative support for signal segment attribution. Taking frequency domain features as phase information as an example, it is expressed as:

[0121]

[0122] In the formula, This represents the sequence similarity between the frequency domain feature sequences corresponding to the x-th signal segment and the (x+1)-th signal segment at the a-th frequency level. The similarity value is [0, 1], and the larger the value, the higher the degree of fit between the sequences. Here, x and x+1 are the identifiers of two temporally adjacent signal segments.

[0123] This represents the average phase value at all times in the x-th signal segment at the a-th frequency level; It represents the average phase value at the a-th frequency level for all times in the (x+1)-th signal segment at the a-th frequency level.

[0124] The difference in the average value of the phase values ​​is used to represent the phase difference between the x-th signal segment and the (x+1)-th signal segment at the a-th frequency level. The maximum theoretical difference in phase, divided by This is used to normalize the phase difference, obtaining a dimensionless relative phase difference, with values ​​[0, 1]. The larger the value, the more significant the phase difference. By taking the absolute value function, the positive and negative signs of the phase difference are eliminated, focusing only on the difference amplitude. Then, the difference amplitude is subtracted from 1 to obtain the phase fit (the smaller the phase difference, the closer this value is to 1).

[0125] The sequence similarity and phase fit are multiplied to obtain the combined quantitative value of the fit and phase fit between sequences at the a-th frequency level. The larger the product, the higher the degree of similarity in sequence morphology and the smaller the phase difference.

[0126] This is used to extract the maximum value across all frequency levels, focusing on the most significant frequency level fit, and serving as the frequency domain feature similarity between the x-th signal segment and the (x+1)-th signal segment. .

[0127] Specifically, if the frequency domain features include multiple types beyond phase information, such as amplitude spectrum, power spectral density, frequency centroid, and harmonic components, the phase fit part in the formula is replaced with the average fit of multiple types. Referring to the phase fit, the fit for each type of feature is calculated as: 1 - (absolute difference of feature values ​​between adjacent signal segments) / (maximum theoretical difference of feature values).

[0128] S323. If the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, it is determined that the two signal segments belong to the same material layer.

[0129] In some implementations, a preset similarity threshold is first determined. A large amount of signal segment data from known material layers of similar multilayer composite protective films is collected, frequency domain feature similarity is calculated, the minimum value of frequency domain feature similarity is statistically analyzed, and the error range of signal feature fluctuations is combined with the calibration of a standard thickness film validation set, with the error controlled within 3%, to determine the final preset similarity threshold (e.g., the preset similarity threshold can be set to 0.8).

[0130] Next, the frequency domain feature similarity of two adjacent signal segments is retrieved and compared with a preset similarity threshold. If the frequency domain feature similarity value is greater than or equal to the preset similarity threshold, it indicates that the sequence similarity and frequency domain feature difference of the two signal segments at the most significant frequency level are highly consistent, thus determining that the two signal segments belong to the same material layer. This judgment, through deep fusion of phase features at multiple frequency levels and precise threshold calibration, can accurately identify signal segments of the same material layer even in scenarios where the material layer is thin and the interface differences are not obvious, avoiding misjudgments caused by local feature fluctuations and providing a reliable basis for signal attribution for high-precision thickness measurement.

[0131] S324. If the frequency domain feature similarity between two temporally adjacent signal segments is less than a preset similarity threshold, determine that the two signal segments belong to different material layers.

[0132] If the frequency domain feature similarity value is less than the preset similarity threshold, it indicates that there is an essential difference in the frequency domain features of the two signal segments at the core frequency level, originating from the reflection behavior of different material layers. This determines that the two signal segments belong to different material layers. This judgment fully utilizes the advantages of multi-dimensional frequency domain feature fusion. Even if there is accidental feature matching between signal segments, it can accurately identify material layer differences through the implicit constraints at the core frequency level, avoiding misintegration in thin material layers or scenarios with blurred interfaces.

[0133] Based on the above technical solution, by extracting multi-frequency hierarchical frequency domain feature sequences from signal segments, combining sequence similarity with frequency domain feature differences to calculate frequency domain feature similarity, and then determining the material layer affiliation of the signal segment according to a preset threshold, high-precision signal layering of multilayer composite films is achieved in scenarios where the material layers are thin and the interface differences are not obvious. Pure single reflected light signals of each material layer are obtained, effectively solving the problem of easy misjudgment in traditional methods. This lays a solid foundation for subsequent ultra-high precision thickness calculation and significantly improves the accuracy and reliability of film thickness measurement.

[0134] In one possible implementation, combining Figure 2 ,like Figure 6 As shown, the method in S4 above for determining the thickness of each layer of the protective film based on the single reflected light signal and optical parameters corresponding to each material layer can be specifically implemented through the following S41 to S42, which are explained in detail below:

[0135] S41. Perform frequency domain transformation on the single reflected light signal corresponding to each material layer to obtain the frequency domain signal, and determine the peak frequency from the frequency domain signal.

[0136] In some implementations, methods for determining the peak frequency from a frequency domain signal may include: identifying characteristic peak regions related to the material layer thickness from the frequency domain signal using a peak detection algorithm; then interpolating the frequency domain signal to obtain a high-resolution signal, where the interpolation interval for the characteristic peak regions is smaller than that for other regions; and finally, performing curve fitting on the high-resolution signal to determine the peak frequency.

[0137] First, a pre-defined frequency domain transformation algorithm (such as Fast Fourier Transform, Wavelet Packet Transform, etc.) is used to process the single reflected light signal (time domain form), converting the time-domain signal, which originally reflected the signal's change over time, into a frequency-domain signal that reflects the frequency distribution. This transformation overcomes the limitation that time-domain signals are difficult to directly correlate with the thickness of material layers, making the thickness-related frequency characteristics in the signal explicit.

[0138] Next, the frequency domain signal is analyzed using a preset peak detection algorithm (such as a peak scanning algorithm based on amplitude threshold) to identify characteristic peak regions directly related to the material layer thickness. This algorithm filters out clutter peaks in the frequency domain signal by setting amplitude thresholds and peak spacing thresholds, retaining only characteristic peaks strongly correlated with the material layer's reflection characteristics, avoiding interference from irrelevant frequencies, and initially locking down the core frequency range related to thickness.

[0139] Then, differential interpolation is performed on the frequency domain signal. For the identified characteristic peak regions, a smaller interpolation interval (e.g., 0.1MHz) is used to supplement data points; for other regions of the frequency domain signal, a larger interpolation interval (e.g., 1MHz) is used. This differential interpolation strategy significantly improves the frequency resolution of the characteristic peak regions while ensuring computational efficiency, resulting in a high-resolution frequency domain signal that fully preserves the detailed features of the peak frequencies.

[0140] Finally, curve fitting (such as Gaussian curve fitting, Lorentz curve fitting, etc.) is performed on the characteristic peak regions in the high-resolution frequency domain signal. The resulting smooth curve accurately locates the frequency value corresponding to the curve's peak, i.e., the peak frequency. This fitting process eliminates the discrete fluctuations in the interpolated signal, ensuring the accuracy of the peak frequency.

[0141] S42. Determine the thickness of each layer based on the peak frequency and refractive index corresponding to each material layer.

[0142] In some implementations, the peak frequency and corresponding refractive index of each material layer are first extracted to ensure parameter compatibility, avoid cross-layer confusion, and provide reliable basic physical quantities for thickness calculation.

[0143] Next, the core principles and formulas for thickness calculation are clarified. Based on the thin-film interference effect and the laws of electromagnetic wave propagation, the peak frequency of a material layer is fixedly related to its thickness and refractive index: the peak frequency is determined by the optical thickness of the material layer (the product of thickness and refractive index), and the optical thickness is inversely proportional to the peak frequency. Based on this, the thickness calculation formula is derived:

[0144]

[0145] In the formula, This indicates the peak frequency corresponding to the current material layer, in Hz, or 1 / s.

[0146] Represents the refractive index of the current material layer. It is dimensionless and is an inherent optical parameter of the material, describing the ratio of the speed of light propagation in the material to the speed of vacuum. The speed of light in a vacuum is approximately 3 × 10⁸ m / s.

[0147] The total path length of light traveling back and forth within the material layer is 2d, and the speed of light in the current material layer is... The round-trip time of light is .

[0148] Due to the peak frequency of the interference signal It is inversely proportional to the round-trip time (fundamental frequency relationship), that is The deformation yields the thickness calculation formula described above. Wherein, peak frequency... A higher refractive index indicates a shorter round-trip time and a thinner material layer; The larger the refractive index, the slower the light travels through the material, and the thinner the material layer at the same frequency. Furthermore, the refractive index... The peak frequency must be greater than zero; otherwise, the material would have no practical optical significance. Furthermore, since the speed of light in a vacuum is constant, the peak frequency will be higher when there is physical thickness. It must also be greater than zero.

[0149] Then, the parameters are substituted to calculate the layer thickness. For each material layer, its unique peak frequency f and refractive index n are substituted into the core formula to obtain the thickness value of that material layer through precise calculation. During the calculation, sufficient decimal places must be retained to avoid the loss of precision caused by numerical truncation. This process transforms the abstract frequency domain signal characteristics into specific physical thickness data, realizing the key transformation from signal analysis to thickness quantification.

[0150] Based on the above technical solution, by performing frequency domain transformation on the single reflected light signal of each material layer, and combining peak detection, differential interpolation and curve fitting to accurately lock the peak frequency, and then completing the quantification and data verification of single-layer thickness based on the inherent refractive index of the material and scientific calculation model, the accurate conversion from signal characteristics to physical thickness is realized, which effectively ensures the accuracy and reliability of single-layer thickness data, provides high-quality core data support for subsequent total thickness calculation, and significantly improves the overall accuracy of multilayer composite film thickness measurement.

[0151] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0152] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0153] In this embodiment of the invention, the automatic thickness measurement device for protective films can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0154] This invention also provides a schematic diagram of the hardware structure of an automatic thickness measurement device for protective films, see below. Figure 7 The automatic thickness measuring device 700 for protective films includes a processor 701, and optionally, a memory 702 connected to the processor 701.

[0155] In the first possible implementation, see Figure 7 The automatic thickness measurement device 700 for protective films also includes a transceiver 703. The processor 701, memory 702, and transceiver 703 are connected via a bus. The transceiver 703 is used to communicate with other devices or communication networks. Optionally, the transceiver 703 may include a transmitter and a receiver. The device in the transceiver 703 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 703 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0156] Based on the first possible implementation method Figure 7 The structural diagram shown can be used to illustrate the structure of the automatic thickness measuring device for protective films involved in the above embodiments.

[0157] in, Figure 7 The system chip in the automatic thickness measurement device for protective films can also be illustrated. In this case, the actions performed by the aforementioned automatic thickness measurement device for protective films can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0158] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in this embodiment can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0159] The processor in this invention may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a System-on-a-Chip (SoC), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations.

[0160] The memory in the embodiments of the present invention may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0161] This invention also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0162] This invention also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0163] This invention also provides a chip, which includes a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0164] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0165] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0166] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for automatically measuring the thickness of a protective film, characterized by, include: Acquire the aliased reflected light signal of the protective film and the optical parameters of the multilayer materials constituting the protective film; Based on the time-domain characteristics of the aliased reflected light signal, the aliased reflected light signal is divided into multiple signal segments; Based on the frequency domain feature similarity between two temporally adjacent signal segments, multiple consecutive signal segments are combined to obtain a single reflected light signal corresponding to each material layer; The thickness of each layer of the protective film is determined based on the single reflected light signal and optical parameters corresponding to each material layer, and the total thickness is obtained.

2. The method of claim 1, wherein Based on the time-domain characteristics of the aliased reflected light signal, the aliased reflected light signal is divided into multiple signal segments, including: For the aliased reflected light signal at each time step, analyze the degree of change in its temporal characteristics relative to adjacent time steps; If the degree of change at the target time is greater than or equal to the preset change threshold, the target time will be marked as the time of change in reflected light. Using the moment of change of the reflected light as the endpoint, the aliased reflected light signal is divided into multiple continuous signal segments.

3. The automatic thickness measurement method for protective films according to claim 2, characterized in that, For the aliased reflected light signal at each time step, analyze the degree of change in its temporal characteristics relative to adjacent time steps, including: Temporal features are extracted from the aliased reflected light signals and arranged in chronological order to form a temporal feature sequence; Based on the characteristic changes of the temporal features at adjacent time points in the temporal feature sequence, the degree of change of the temporal features of the aliased reflected light signal at each time point is determined.

4. The automatic thickness measurement method for protective films according to claim 3, characterized in that, The time-domain features include parameters showing how various reflected light signals change over time.

5. The method of claim 1, wherein Based on the frequency domain feature similarity between two temporally adjacent signal segments, multiple consecutive signal segments are combined to obtain a single reflected light signal corresponding to each material layer, including: Frequency domain features are extracted for each signal segment to obtain the frequency domain feature sequence of each signal segment at multiple frequency levels; The frequency domain feature similarity between two temporally adjacent signal segments is determined based on the sequence similarity of their frequency domain feature sequences. If the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, it is determined that the two signal segments belong to the same material layer. If the frequency domain feature similarity between two temporally adjacent signal segments is less than a preset similarity threshold, it is determined that the two signal segments belong to different material layers.

6. The method of claim 1, wherein Based on the frequency domain feature similarity between two temporally adjacent signal segments, multiple consecutive signal segments are combined to obtain a single reflected light signal corresponding to each material layer, including: Frequency domain features are extracted for each signal segment to obtain the frequency domain feature sequence of each signal segment at multiple frequency levels; The frequency domain feature similarity between two temporally adjacent signal segments is determined based on the sequence similarity of the frequency domain feature sequences of the two temporally adjacent signal segments and the frequency domain feature differences at each frequency level. If the frequency domain feature similarity between two temporally adjacent signal segments is greater than or equal to a preset similarity threshold, it is determined that the two signal segments belong to the same material layer. If the frequency domain feature similarity between two temporally adjacent signal segments is less than a preset similarity threshold, it is determined that the two signal segments belong to different material layers.

7. The method of automatically measuring the thickness of a protective film according to claim 5 or 6, characterized by, The frequency domain features include phase information for each signal segment; Frequency domain feature extraction is performed on each signal segment, including: Each signal segment is processed by wavelet transform to obtain wavelet coefficients in complex form; Based on the wavelet coefficients, the phase information of each signal segment at multiple frequency levels and times is determined.

8. The method of claim 1, wherein The optical parameters include the refractive index of each material layer; the thickness of each layer of the protective film is determined based on the single reflected light signal and optical parameters corresponding to each material layer, including: A frequency domain transformation is performed on the single reflected light signal corresponding to each material layer to obtain a frequency domain signal, and the peak frequency is determined from the frequency domain signal. The thickness of each layer is determined based on the peak frequency and refractive index of each material layer.

9. The method of claim 8, wherein the step of automatically measuring the thickness of the protective film is performed by a thickness gauge. Determining the peak frequency from the frequency domain signal includes: The peak detection algorithm is used to identify characteristic peak regions related to the material layer thickness from the frequency domain signal; The frequency domain signal is interpolated to obtain a high-resolution signal; the interpolation interval of the characteristic peak region is smaller than that of other regions. The high-resolution signal is subjected to curve fitting to determine the peak frequency.

10. An automatic thickness measurement system for protective films, characterized in that, include: Signal acquisition unit, parameter storage unit, signal processing unit, and automatic thickness measurement unit; The signal acquisition unit is used to acquire the aliased reflected light signal of the protective film; The parameter storage unit is used to store the optical parameters of the multilayer materials that make up the protective film; The signal processing unit is used to divide the aliased reflected light signal into multiple signal segments according to the time-domain characteristics of the aliased reflected light signal; The signal processing unit is also used to combine multiple consecutive signal segments to obtain a single reflected light signal corresponding to each material layer based on the frequency domain feature similarity between two temporally adjacent signal segments. The automatic thickness measurement unit is used to determine the thickness of each layer of the protective film and obtain the total thickness based on the single reflected light signal and optical parameters corresponding to each material layer.

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