Pulse energy control method and system for femtosecond laser processing of carbon nanotube film

Abstract

The invention provides a pulse energy control method and system for femtosecond laser processing of a carbon nanotube film, and relates to the technical field of laser precision machining.The method comprises the steps that plasma spectral signals and intensity data generated when femtosecond laser processing of a carbon nanotube film micropore array is conducted are collected; extracting the characteristics of the carbon element and the metal catalyst element and generating a spectrum ratio; performing time gating integration processing on the plasma spectrum signal based on the ratio to generate a spectrum integration signal; carrying out moving average and frequency spectrum peak value locking processing on the integral signal, and identifying the spectral characteristics of the carbon nanotube after complete gasification; the energy adjustment amount is calculated according to the spectral characteristics, femtosecond laser pulse energy is controlled in real time by adjusting the driving voltage of the acousto-optic modulator, and closed-loop control over the micropore array machining process is achieved. According to the invention, the processing precision and the material integrity of the femtosecond laser processing carbon nanotube film are improved.

Description

Technical Field

[0001] This application relates to the field of laser precision machining technology, and in particular to a pulse energy control method and system for femtosecond laser machining of carbon nanotube thin films. Background Technology

[0002] In the fabrication of field emission cathode devices, a microporous array structure with a specific depth needs to be fabricated on the surface of a carbon nanotube film. Due to the inherent spatial non-uniformity of carbon nanotube films, such as uneven thickness and varying density distribution, it is necessary to be able to sense the material state in real time and dynamically adjust process parameters during fabrication to achieve high-precision control of ablation depth and avoid thermal damage to the material.

[0003] Existing technical solutions employ an open-loop control system based on plasma spectral monitoring. This system establishes a correlation between the overall intensity change of the plasma emission spectrum and the laser energy parameters. The system then uses a preset energy adjustment curve to adjust the pulse energy in stages according to the degree to which the spectral intensity deviates from the standard value.

[0004] However, existing technologies have limited ability to suppress background noise in spectral signals and are easily affected by environmental interference when identifying material phase transition states. Furthermore, the system exhibits a lag in response to the spatial non-uniformity of carbon nanotube films, with a time delay between energy adjustment and actual material state changes. Therefore, this control method can lead to over-ablation or under-processing when dealing with localized thickness abrupt changes in the film, affecting the morphological consistency of the micropore array. Summary of the Invention

[0005] This application provides a pulse energy control method and system for femtosecond laser processing of carbon nanotube thin films, in order to solve the problems of low processing accuracy and poor material integrity in femtosecond laser processing of carbon nanotube thin films in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for controlling the pulse energy of femtosecond laser processing of carbon nanotube thin films, comprising:

[0007] Collect plasma spectral signals and corresponding spectral intensity data generated when femtosecond lasers act on carbon nanotube films to process micropore arrays;

[0008] Carbon element characteristics and metal catalyst element characteristics are extracted from the spectral intensity data, and a spectral ratio is generated based on the carbon element characteristics and metal catalyst element characteristics.

[0009] Based on the spectral ratio, the plasma spectral signal is subjected to time-gated integration processing to generate a spectral integrated signal;

[0010] An adaptive filtering algorithm is used to perform moving average processing and spectral peak locking processing on the spectral integral signal in order to identify spectral features;

[0011] Based on the spectral characteristics, the energy adjustment amount of the femtosecond laser is calculated using an energy compensation algorithm. According to the energy adjustment amount, the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser is adjusted to adjust the pulse energy of the femtosecond laser, thereby realizing closed-loop control of the micropore array processing process on the carbon nanotube film.

[0012] Optionally, the step of using an adaptive filtering algorithm to perform moving average processing and spectral peak locking processing on the spectral integral signal to identify spectral features includes:

[0013] The spectral integral signal is filtered using an LMS adaptive filter to obtain the filtered signal.

[0014] The filtered signal is subjected to a moving average process to obtain a smoothed signal;

[0015] The smoothed signal is subjected to spectrum analysis, and the peak values ​​with amplitudes greater than a preset dynamic threshold are identified from the spectrum analysis results using a peak detection algorithm.

[0016] The spectral peaks are matched with a preset spectral feature library to screen out characteristic peak combinations that characterize the complete vaporization of carbon nanotube films, and spectral features are constructed based on the characteristic peak combinations.

[0017] Optionally, the step of matching the spectral peaks with a preset spectral feature library to screen out characteristic peak combinations that characterize the complete vaporization of carbon nanotube films, and constructing spectral features based on the characteristic peak combinations, includes:

[0018] The wavelength position of the spectral peak is compared one by one with the reference wavelength position in the spectral feature library to calculate the wavelength matching degree of each spectral peak.

[0019] Based on the wavelength matching degree and the amplitude of the spectral peak, the feature significance of each spectral peak is calculated, and spectral peaks with feature significance greater than a preset feature threshold are selected to form a candidate peak set;

[0020] Peaks containing carbon element characteristic spectral lines and peaks containing metal characteristic spectral lines are selected from the candidate peak set to form characteristic peak combinations. The wavelength position and amplitude value of each peak are extracted from the characteristic peak combinations to establish a correspondence table between wavelength and amplitude.

[0021] According to the corresponding table, the ratio between the amplitude of the characteristic spectral line of carbon element and the amplitude of the characteristic spectral line of metal is calculated, and the ratio is combined with the amplitude values ​​of each peak in the characteristic peak combination to form a composite feature;

[0022] The composite features are encapsulated according to a preset data structure to construct spectral features.

[0023] Optionally, the step of extracting carbon elemental features and metal catalyst elemental features from the spectral intensity data, and generating a spectral ratio based on the carbon elemental features and metal catalyst elemental features, includes:

[0024] The first characteristic wavelength range corresponding to carbon and the second characteristic wavelength range corresponding to metal catalyst elements are determined from the spectral intensity data.

[0025] Identify the first spectral peak with the highest intensity within the first characteristic wavelength range, and record the peak intensity corresponding to the first spectral peak as a carbon element feature.

[0026] Identify the second spectral peak with the highest intensity within the second characteristic wavelength range, and record the peak intensity corresponding to the second spectral peak as the elemental characteristic of the metal catalyst.

[0027] The spectral ratio is obtained by calculating the ratio between the carbon element characteristics and the metal catalyst element characteristics.

[0028] Optionally, the step of performing time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal includes:

[0029] The plasma emission period is determined based on the comparison between the spectral ratio and the preset ratio threshold.

[0030] A time-gated window is set to correspond to the emission period. The start time of the time-gated window is set based on the pulse trigger signal, and the window width is dynamically adjusted according to the plasma lifetime.

[0031] Within the time-gated window, the plasma spectral signals within multiple consecutive pulse cycles are integrated over time to obtain the time-integrated signal, while the background noise signal during the laser pulse interval is recorded.

[0032] The corresponding background noise signal is removed from the time integral signal within each pulse period to obtain the denoised plasma spectrum signal.

[0033] The Kalman filter algorithm is used to preprocess each denoised plasma spectral signal. The denoised plasma spectral signals in multiple consecutive pulse periods after preprocessing are accumulated and averaged to obtain the spectral integral signal.

[0034] Optionally, the step of calculating the energy adjustment of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm includes:

[0035] Intensity distribution data of characteristic peak combinations are extracted from the spectral features, and the intensity distribution data is compared with the preset reference intensity distribution under a standard fully vaporized state.

[0036] Based on the comparison results, the difference between the intensity distribution data and the reference intensity distribution is calculated using the weighted Euclidean distance algorithm;

[0037] The difference degree is input to an energy compensator built based on fuzzy control, and the difference degree is mapped to an energy compensation level through the fuzzy inference rules pre-stored in the energy compensator.

[0038] Based on the energy compensation level and the spatial location information of the current processing point, the corresponding energy adjustment amount is obtained from the preset energy adjustment amount lookup table.

[0039] Optionally, adjusting the driving voltage of the acousto-optic modulator used to control the femtosecond laser output according to the energy adjustment amount to adjust the pulse energy of the femtosecond laser includes:

[0040] The energy adjustment amount is converted into an adjustment value for the acousto-optic modulator drive voltage;

[0041] Based on the adjustment value of the driving voltage, a corresponding voltage control signal is generated by a digital-to-analog converter, and the voltage control signal is sent to the driving circuit of the acousto-optic modulator.

[0042] The driving circuit amplifies the voltage control signal to the operating level and applies it to the piezoelectric transducer of the acousto-optic modulator to adjust the operating voltage of the acousto-optic modulator in order to control the pulse energy output of the femtosecond laser.

[0043] Secondly, this application provides a pulse energy control system for femtosecond laser processing of carbon nanotube thin films, comprising:

[0044] The acquisition module is used to acquire the plasma spectral signals and corresponding spectral intensity data generated when a femtosecond laser acts on a carbon nanotube film to process a micropore array.

[0045] The extraction module is used to extract carbon element features and metal catalyst element features from the spectral intensity data, and generate spectral ratio values ​​based on the carbon element features and metal catalyst element features;

[0046] The generation module is used to perform time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal;

[0047] The identification module is used to perform moving average processing and spectral peak locking processing on the spectral integral signal using an adaptive filtering algorithm to identify spectral features;

[0048] The calculation module is used to calculate the energy adjustment amount of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm, and adjust the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser according to the energy adjustment amount, so as to adjust the pulse energy of the femtosecond laser and realize closed-loop control of the micropore array processing process on the carbon nanotube film.

[0049] Thirdly, this application provides an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor, configured to execute the computer program to implement the steps of the pulse energy control method for femtosecond laser processing of carbon nanotube films as described in the first aspect above.

[0052] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the pulse energy control method for femtosecond laser processing of carbon nanotube films as described in the first aspect above.

[0053] The technical solution provided in this application has the following beneficial effects:

[0054] First, this application provides a raw data foundation for subsequent analysis by real-time monitoring of material changes during processing. Then, it establishes a quantitative index of material phase transition state to accurately reflect the gasification process of carbon nanotubes. Next, it effectively suppresses background noise interference, thereby improving signal quality and laying the foundation for feature recognition. After that, it identifies the key spectral features of complete carbon nanotube gasification to ensure the accuracy of state judgment. Finally, it achieves real-time and precise control of processing energy, thereby forming a stable closed-loop control system.

[0055] Furthermore, this application also performs noise suppression and signal smoothing on the spectral integral signal through adaptive filtering, identifies significant peaks through spectral analysis, and finally selects characteristic peak combinations representing complete vaporization by matching with a feature library to construct spectral features for energy compensation.

[0056] Furthermore, this scheme effectively improves the accuracy and reliability of spectral feature extraction, ensures the precision of material state identification, and provides a reliable basis for subsequent energy compensation control, thereby ensuring the stability and consistency of the processing.

[0057] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart of a pulse energy control method for femtosecond laser processing of carbon nanotube thin films is provided for embodiments of this application;

[0060] Figure 2 A schematic diagram illustrating a specific implementation of a pulse energy control method for femtosecond laser processing of carbon nanotube thin films provided in this application embodiment;

[0061] Figure 3 This is a schematic diagram of the pulse energy control system for femtosecond laser processing of carbon nanotube thin films, provided as an embodiment of this application. Detailed Implementation

[0062] Existing femtosecond laser processing technology based on plasma spectral monitoring faces significant challenges when dealing with carbon nanotube films with spatially non-uniform characteristics. This technology relies on the phased control of the overall intensity of the plasma spectrum, and its signal processing has limited ability to suppress background noise, making the material state assessment susceptible to environmental interference. At the same time, due to the lag in the system's response to changes in local characteristics such as film thickness and density, it is difficult to adjust the energy parameters in a timely manner to match the actual processing state of the material. Ultimately, this results in the formation of microporous structures with uneven ablation depth on the film surface, which directly affects the performance consistency of field emission cathode devices.

[0063] To address the aforementioned issues, this application proposes a pulse energy control method for femtosecond laser processing of carbon nanotube thin films. This method extracts the characteristic spectral line ratio of carbon to metal catalyst elements as the core criterion for material phase transition. Time-gated integration and adaptive filtering techniques are employed to denoise and enhance the spectral signal, enabling precise identification of the fully vaporized state characteristics of carbon nanotubes. Based on this, an energy compensation algorithm is used to calculate the energy adjustment amount in real time, thereby dynamically adjusting the laser output energy. Furthermore, this scheme effectively overcomes the limitation of existing technologies in responding to the lag of local material properties by establishing a closed-loop control link from state perception to energy regulation, thus improving the morphological consistency and process stability of micropore array processing.

[0064] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] The core of this application is to provide a pulse energy control method for femtosecond laser processing of carbon nanotube thin films, and a flowchart of one specific embodiment is shown below. Figure 1 As shown, the method includes:

[0066] Step 101: Collect the plasma spectral signal and corresponding spectral intensity data generated when a femtosecond laser acts on a carbon nanotube film to process a micropore array.

[0067] In step 101, femtosecond laser refers to a laser source with a pulse duration on the order of femtoseconds, carbon nanotube film is a thin film material composed of carbon nanotubes, micropore array refers to a micro-pore structure arranged according to a specific pattern, plasma spectral signal is the light signal emitted by the plasma generated by the laser acting on the material, and spectral intensity data is a set of light intensity values ​​corresponding to different wavelengths.

[0068] In this embodiment, firstly, during the fabrication of the micropore array, a femtosecond laser system emits laser pulses onto the surface of the carbon nanotube film, and the laser pulses interact with the material to generate plasma luminescence; then, a spectral acquisition device is used to receive the plasma luminescence signal and convert the optical signal into an electrical signal; finally, the electrical signal is digitized by an analog-to-digital converter to obtain spectral intensity data containing wavelength and intensity information.

[0069] Step 102: Extract carbon element characteristics and metal catalyst element characteristics from the spectral intensity data, and generate spectral ratios based on the carbon element characteristics and metal catalyst element characteristics.

[0070] In step 102, carbon element characteristics refer to the intensity value of the characteristic spectral lines of carbon elements, metal catalyst element characteristics refer to the intensity value of the characteristic spectral lines of catalyst elements such as nickel and iron, and the spectral ratio is the ratio of the intensity of carbon element characteristics to the intensity of metal catalyst element characteristics.

[0071] In this embodiment, the wavelength ranges of the characteristic spectral lines of carbon and the characteristic spectral lines of the metal catalyst are determined from the spectral intensity data. The spectral peaks with the highest intensity are found in the two wavelength ranges respectively, and the intensity values ​​corresponding to the two peaks are recorded. The intensity value of the characteristic spectral lines of carbon is divided by the intensity value of the characteristic spectral lines of the metal catalyst to obtain the spectral ratio data.

[0072] For example, a research institute used a femtosecond laser with a repetition frequency of 100 kHz to fabricate micropores in a 5-micrometer-thick carbon nanotube film in a vacuum environment. The plasma emission was collected through a lens with a focal length of 100 mm, and the spectral signal in the wavelength range of 200-800 nanometers was acquired using an image-enhanced spectrometer. The optical signal was then converted into an electrical signal and processed by a 16-bit analog-to-digital converter to obtain spectral intensity data containing 1024 data points.

[0073] After obtaining the above spectral intensity data, an intensity of 12,500 counts was found at 247.8 nm within the characteristic wavelength range of carbon (245-250 nm), and an intensity of 4,800 counts was found at 341.4 nm within the characteristic wavelength range of nickel (340-345 nm). The spectral ratio was calculated as 12,500 / 4800 = 2.604, which is used to characterize the phase transition state of the material.

[0074] Step 103: Based on the spectral ratio, perform time-gated integration processing on the plasma spectral signal to generate a spectral integrated signal.

[0075] In step 103, time-gated integration is the process of accumulating the signal within a specific time window, and the spectral integrated signal is the signal after noise suppression processing.

[0076] In this embodiment, firstly, the emission period of the plasma is determined according to the spectral ratio, and a time-gated window corresponding to the emission period is set; then, the plasma spectral signal of multiple consecutive pulse cycles is integrated over time within the time-gated window, while the background noise during the laser pulse interval is recorded and the background noise is subtracted from the integrated signal; finally, the net signal is accumulated and averaged.

[0077] For example, based on the spectral ratio of 2.604, the emission period is determined to be 10-30 nanoseconds after the laser pulse. The time gate window width is set to 20 nanoseconds. The signal of 50 consecutive pulse cycles is integrated, and the background noise is measured to be 500 counts. The noise is subtracted from the integrated signal of 8500 counts in each cycle to obtain a net signal of 8000 counts. After accumulating and averaging, a spectral integrated signal of 7980 counts is obtained.

[0078] Step 104: Adaptive filtering algorithm is used to perform moving average processing and spectral peak locking processing on the spectral integral signal to identify spectral features.

[0079] In step 104, spectral features are used to characterize the complete vaporization of the carbon nanotube film. The complete vaporization of the carbon nanotube film refers to the phase transition process in which the laser energy causes the carbon nanotube material in the working area to instantly undergo a direct transformation from solid to gaseous plasma during the femtosecond laser processing. This state is considered to provide a basis for subsequent identification of spectral feature windows. Spectral peak locking is the process of identifying significant peaks in the spectrum.

[0080] In this embodiment, firstly, an adaptive filter is used to filter the spectral integral signal; then, a moving average is applied to the filtered signal; next, spectral analysis is performed on the smoothed signal; then, a peak detection algorithm is used to identify spectral peaks whose amplitude exceeds a dynamic threshold; finally, the spectral peaks are matched with a feature library to select characteristic peak combinations.

[0081] For example, firstly, the LMS algorithm with a step size of 0.01 is used to adaptively filter the spectral integral signal with a score of 7980; then, the filtered signal is processed by a moving average with a window size of 5; next, the smoothed signal is subjected to a fast Fourier transform to obtain the spectrum; then, spectral peaks with amplitudes exceeding 3 times the noise level are detected; finally, the spectral peaks are compared with the feature library to select the peak combination with a wavelength of 247.9 nm and an amplitude of 8500 and a wavelength of 341.5 nm and an amplitude of 4000.

[0082] Step 105: Based on the spectral characteristics, calculate the energy adjustment amount of the femtosecond laser using an energy compensation algorithm, and adjust the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser according to the energy adjustment amount, so as to adjust the pulse energy of the femtosecond laser and realize closed-loop control of the micropore array processing process on the carbon nanotube film.

[0083] In step 105, an energy regulation relationship is established between the acousto-optic modulator and the "femtosecond laser". The acousto-optic modulator generates an ultrasonic field by receiving the driving voltage signal to form a diffraction grating for the femtosecond laser beam, and achieves real-time control of the femtosecond laser pulse energy by adjusting the diffraction efficiency. This control constitutes an important part of closed-loop energy regulation.

[0084] An acousto-optic modulator is a device that controls the intensity of a laser through sound waves. The driving voltage of an acousto-optic modulator refers to the operating parameter that changes the intensity of the electrical signal applied to the crystal of the acousto-optic modulator, thereby changing the diffraction characteristics of the laser as it passes through the medium by utilizing the acousto-optic effect, thus achieving precise control of the energy of the femtosecond laser pulse; the energy adjustment amount is the energy value that needs to be adjusted.

[0085] In this embodiment, intensity distribution data of characteristic peak combinations are extracted from spectral features. This data is compared with a reference intensity distribution to calculate the difference. Then, the difference is mapped to an energy compensation level through an energy compensator. The energy adjustment amount is then queried based on the compensation level and spatial location information. Finally, the adjustment amount is converted into a driving voltage adjustment value, and the acousto-optic modulator is adjusted through a voltage control circuit.

[0086] For example, firstly, the carbon element intensity of 8500 and the nickel element intensity of 4000 are extracted from the spectral characteristics, and the difference of 500 is calculated by comparing them with the reference intensities of 9000 and 3500. This difference is then mapped to a level 2 energy compensation. Next, the energy adjustment amount of +0.06 microjoules is obtained by consulting the process parameter table based on the processing point coordinates (1.2, 0.8) meters, and this energy adjustment amount is converted into a voltage adjustment value of +1.5 volts. Finally, the laser driving voltage is adjusted from 50 volts to 51.5 volts to achieve precise adjustment of the pulse energy from 0.8 microjoules to 0.86 microjoules.

[0087] This method acquires plasma spectral signals in real time and extracts feature ratios. Through multi-level signal processing and feature recognition, a closed-loop control system is established from material state perception to laser energy regulation. This effectively improves the morphological consistency and processing accuracy of the micropore array during femtosecond laser processing of carbon nanotube films, avoids material thermal damage, and ensures the performance stability of the field emission cathode device.

[0088] To address the issue of noise interference affecting the accuracy of feature identification in plasma spectral signals, some embodiments include step 104: employing an adaptive filtering algorithm to perform moving average processing and spectral peak locking on the spectral integral signal to identify spectral features, such as... Figure 2 As shown, it includes:

[0089] Step 201: The spectral integral signal is filtered using an LMS adaptive filter to obtain the filtered signal.

[0090] In step 201, the filtered signal refers to the spectral integral signal after noise suppression processing.

[0091] In this embodiment, the spectral integral signal is input into the LMS adaptive filter. The filter continuously adjusts its parameters by comparing the error between the actual output and the expected output in order to gradually reduce the noise component in the output signal and finally output a filtered signal with significantly reduced noise level.

[0092] Step 202: Perform a moving average process on the filtered signal to obtain a smoothed signal.

[0093] In step 202, the smoothed signal refers to the filtered signal after smoothing.

[0094] In this embodiment, the filtered signal is first averaged point-by-point using a fixed-length moving window. As the window slides across the signal sequence, the output value at each position is the arithmetic mean of all data points within the window, thus obtaining a smoothed signal with fluctuations less than a preset fluctuation threshold. This embodiment does not specifically limit the value of the preset fluctuation threshold; it can be set according to actual conditions.

[0095] Step 203: Perform spectrum analysis on the smoothed signal and identify the spectrum peaks with amplitudes greater than a preset dynamic threshold from the spectrum analysis results using a peak detection algorithm.

[0096] In step 203, amplitude refers to the signal intensity value at a specific wavelength or frequency after the spectral integral signal is analyzed by spectrum analysis. This value is obtained by performing a fast Fourier transform on the smoothed signal to obtain a spectrum, and then extracting the intensity values ​​corresponding to each wavelength position from the spectrum.

[0097] The preset dynamic threshold can be a variable threshold value that is adaptively adjusted according to the overall energy level of the smoothed signal. In this embodiment, the value of the dynamic threshold is not specifically limited, but can be set according to the actual situation. The spectral peak refers to the frequency domain feature point whose amplitude exceeds the dynamic threshold.

[0098] In this embodiment, the smoothed signal is first subjected to a fast Fourier transform to obtain a spectrum. Then, a sliding window is used to search for local maxima on the spectrum. Finally, the amplitude of each maximum point is compared with a threshold value dynamically calculated based on the overall energy level of the signal to filter out all significant spectral peaks whose amplitudes exceed the dynamic threshold.

[0099] Step 204: Match the spectral peaks with a preset spectral feature library to screen out characteristic peak combinations that characterize the complete vaporization of carbon nanotube films, and construct spectral features based on the characteristic peak combinations.

[0100] In step 204, the spectral feature library is a pre-established database containing standard spectral features of carbon nanotubes in the fully vaporized state, and the feature peak combination is a set of feature peaks corresponding to the fully vaporized state selected from the spectral peaks.

[0101] In this embodiment, firstly, the wavelength position of the identified spectral peaks is compared one by one with the standard characteristic wavelengths in the spectral feature library to calculate the matching degree of each spectral peak; then, based on the matching degree and peak amplitude, a comprehensive evaluation is conducted to select the characteristic peak combination that best characterizes the fully vaporized state of carbon nanotubes; finally, the wavelength and amplitude information of these characteristic peak combinations are combined to construct a standard format spectral feature.

[0102] Here is a specific example:

[0103] Based on the 7980-scoring spectral integral signal obtained in the aforementioned embodiment, a research institution used a minimum mean square adaptive filter to filter the signal. The filter step size parameter was set to 0.01. The filter coefficients were continuously adjusted through iterative calculation to minimize the output signal error. After 1024 iterations, a filtered signal with a score of 7950 was obtained.

[0104] The filtered signal was then subjected to a moving average with a window size of 5, that is, the arithmetic mean of each data point and the two data points before and after it was taken as the new value of that point. The smoothed signal was then scored as 7940.

[0105] Next, a fast Fourier transform is performed on the smoothed signal to obtain a spectrum. A peak detection algorithm is used to scan the spectrum and a dynamic threshold of 3 times the noise level, i.e., 3000, is set to identify 15 spectral peaks with amplitudes exceeding the threshold.

[0106] Then, the wavelength positions of these spectral peaks are matched with the standard characteristic wavelengths of complete carbon nanotube vaporization in the preset spectral feature library. For example, the matching degree calculation formula is o=1-|uv|÷x, where o is the matching degree, u is the detection wavelength, v is the reference wavelength, and x is the reference wavelength. The detection wavelength refers to the measured peak wavelength, and the reference wavelength refers to the standard wavelength in the feature library. The matching degree between wavelength 247.9 nm and standard wavelength 247.8 nm is calculated to be 1-|247.9-247.8|÷247.8=0.9996, and the matching degree between wavelength 341.5 nm and standard wavelength 341.4 nm is 1-|341.5-341.4|÷341.4=0.9997.

[0107] Then, based on the condition that the matching degree is greater than 0.999, two peaks with a wavelength of 247.9 nm and an amplitude of 8500 and a wavelength of 341.5 nm and an amplitude of 4000 were selected to form a characteristic peak combination. Finally, a spectral feature containing wavelength position and amplitude value was constructed based on this characteristic peak combination. The specific format of this spectral feature is a set of wavelength amplitude pairs [247.9, 8500; 341.5, 4000], and this spectral feature will be used for subsequent energy compensation calculations.

[0108] In this embodiment of the application, the step scheme effectively improves the accuracy and reliability of spectral feature recognition through multi-level signal processing, provides a reliable basis for precise compensation of pulse energy, and thus ensures the stability of the micropore processing quality of carbon nanotube films.

[0109] To further improve the accuracy and standardization of spectral feature construction, in some embodiments, step 204: matching the spectral peaks with a preset spectral feature library to screen out characteristic peak combinations representing the complete vaporization of carbon nanotube films, and constructing spectral features based on the characteristic peak combinations, includes:

[0110] Step 301: Compare the wavelength position of the spectral peak with the reference wavelength position in the spectral feature library one by one to calculate the wavelength matching degree of each spectral peak.

[0111] In step 301, wavelength matching degree is a quantitative indicator that characterizes the degree of similarity between the detection wavelength and the reference wavelength. Its value ranges from 0 to 1, and the larger the value, the higher the matching degree.

[0112] In this embodiment of the application, the wavelength position of each detected spectral peak is compared one by one with the reference wavelength position corresponding to the complete vaporization of carbon nanotubes pre-stored in the spectral feature library, so as to quantify the degree of matching by calculating the relative magnitude of the wavelength difference between the two, and obtain the wavelength matching degree value corresponding to each spectral peak.

[0113] Step 302: Calculate the feature significance of each spectral peak based on the wavelength matching degree and the amplitude of the spectral peak, and select spectral peaks with feature significance greater than a preset feature threshold to form a candidate peak set.

[0114] In step 302, the feature significance is a composite index that evaluates the importance of spectral peaks by combining wavelength matching degree and signal amplitude. The candidate peak set is a temporary set of spectral peaks whose feature significance reaches a set standard.

[0115] In this embodiment, the wavelength matching degree of each spectral peak is multiplied by its signal amplitude and then normalized to obtain the feature significance value. Then, an appropriate feature threshold is set, and all spectral peaks with feature significance exceeding the threshold are selected and uniformly included in the candidate peak set.

[0116] Step 303: Select peaks containing carbon element characteristic spectral lines and peaks containing metal characteristic spectral lines from the candidate peak set as characteristic peak combinations, and extract the wavelength position and amplitude value of each peak from the characteristic peak combinations to establish a correspondence table between wavelength and amplitude.

[0117] In step 303, the carbon element characteristic spectral line specifically refers to the spectral line with a specific wavelength produced by carbon element in the plasma spectrum. The metal characteristic spectral line refers to the spectral line with a specific wavelength produced by catalyst elements such as iron and nickel in the spectrum. The correspondence table is a structured data table that records the wavelength position and amplitude value of each peak in the characteristic peak combination.

[0118] In this embodiment of the application, peak values ​​in the wavelength range corresponding to the characteristic spectral lines of carbon elements and peak values ​​in the wavelength range corresponding to the characteristic spectral lines of metals are found in the candidate peak value set. Peak values ​​that meet the conditions are combined to form a characteristic peak value combination. Then, the specific wavelength value and amplitude value of each peak value in the combination are extracted, and a correspondence table between the two is established.

[0119] Step 304: According to the correspondence table, calculate the ratio between the amplitude of the carbon element characteristic spectral line and the amplitude of the metal characteristic spectral line, and combine the ratio with the amplitude values ​​of each peak in the characteristic peak combination to form a composite feature.

[0120] In step 304, the composite feature is a multi-dimensional feature representation that integrates the relative intensity ratio and the absolute amplitude value. It includes the amplitude ratio of the characteristic spectral lines of carbon elements and metal catalyst elements, and also retains the original amplitude information of each characteristic spectral line.

[0121] In this embodiment of the application, the amplitude values ​​of the characteristic spectral lines of carbon element and the amplitude values ​​of the characteristic spectral lines of metal are obtained from the correspondence table, and then the ratio between the two is calculated. Then, the ratio is combined with the amplitude values ​​of each peak in the characteristic peak combination in a predetermined order to form a composite feature containing multiple information.

[0122] Step 305: Encapsulate the composite features according to a preset data structure to construct spectral features.

[0123] In step 305, the preset data structure is a data organization format specifically designed for spectral features, which can ensure the standardization and consistency of feature data during transmission and processing.

[0124] In this embodiment of the application, all data elements contained in the composite feature are arranged and combined according to the field requirements of the preset data structure, and necessary format identifiers and verification information are added. After data encapsulation, the final spectral feature is formed.

[0125] Here is a specific example:

[0126] Following the aforementioned embodiment, a research institution first established a table of correspondence between wavelength and amplitude. This table contains two entries: an amplitude score of 8500 corresponding to a wavelength of 247.9 nanometers and an amplitude score of 4000 corresponding to a wavelength of 341.5 nanometers.

[0127] Next, the ratio between the amplitude of the carbon element characteristic spectral line and the amplitude of the metal characteristic spectral line is calculated according to the correspondence table. The ratio = 8500 ÷ 4000 = 2.125. This ratio 2.125 is combined with the amplitude values ​​of each peak in the characteristic peak combination to form a composite feature containing three elements, specifically the ratio 2.125, the amplitude of the carbon element characteristic spectral line 8500, and the amplitude of the metal characteristic spectral line 4000, forming a composite feature [2.125, 8500, 4000]. Finally, this composite feature is encapsulated according to a preset ternary data structure. The ternary data structure requires the data to be arranged in a specific order. The first position stores the ratio data, the second position stores the original amplitude data of the carbon element characteristic spectral line, and the third position stores the original amplitude data of the metal characteristic spectral line. After encapsulation, a standard format spectral feature is constructed. This spectral feature will be used as the input parameter of the energy compensation algorithm for the subsequent calculation of energy adjustment.

[0128] In this embodiment of the application, the step scheme ensures the accuracy and completeness of spectral features through a multi-level screening and standardized construction process, providing a reliable data foundation for subsequent energy compensation and effectively improving the stability and accuracy of the entire control system.

[0129] To accurately quantify the phase transition state of materials, in some embodiments, step 102: extracting carbon elemental characteristics and metal catalyst elemental characteristics from the spectral intensity data, and generating a spectral ratio based on the carbon elemental characteristics and metal catalyst elemental characteristics, includes:

[0130] Step 401: Determine the first characteristic wavelength range corresponding to carbon elements and the second characteristic wavelength range corresponding to metal catalyst elements from the spectral intensity data.

[0131] In step 401, the first characteristic wavelength range is the specific wavelength range in which the characteristic spectral lines of carbon elements appear, and the second characteristic wavelength range is the specific wavelength range in which the characteristic spectral lines of metal catalyst elements appear.

[0132] In this embodiment, based on the characteristics of the carbon nanotube thin film material, the wavelength range in which the characteristic spectral lines of carbon elements appear in the spectral intensity data is defined as the first characteristic wavelength range, and the wavelength range in which the characteristic spectral lines of metal catalyst elements appear in the spectral intensity data is defined as the second characteristic wavelength range.

[0133] Step 402: Identify the first spectral peak with the highest intensity within the first characteristic wavelength range, and record the peak intensity corresponding to the first spectral peak as a carbon element feature.

[0134] In step 402, the first spectral peak is the spectral peak with the highest intensity within the first characteristic wavelength range, and the carbon element characteristic is the intensity value corresponding to this spectral peak.

[0135] In this embodiment of the application, all data points are scanned within the first characteristic wavelength range. By comparing the intensity values ​​corresponding to each wavelength position, the position of the spectral peak with the largest intensity is found, and the intensity value corresponding to that position is recorded as a carbon element feature.

[0136] Step 403: Identify the second spectral peak with the highest intensity within the second characteristic wavelength range, and record the peak intensity corresponding to the second spectral peak as a characteristic of the metal catalyst element.

[0137] In step 403, the second spectral peak is the spectral peak with the highest intensity within the second characteristic wavelength range, and the elemental characteristic of the metal catalyst is the intensity value corresponding to this spectral peak.

[0138] In this embodiment of the application, all data points are scanned within the second characteristic wavelength range. By comparing the intensity values ​​corresponding to each wavelength position, the position of the spectral peak with the highest intensity is found, and the intensity value corresponding to that position is recorded as the elemental characteristic of the metal catalyst.

[0139] Step 404: Calculate the ratio between the carbon element characteristics and the metal catalyst element characteristics to obtain the spectral ratio.

[0140] In the embodiments of this application, the spectral ratio reflects the gasification state of the carbon nanotube material.

[0141] Here is a specific example:

[0142] Following the aforementioned embodiments, a research institution first determined, based on the characteristics of carbon nanotube thin film materials, that the first characteristic wavelength range corresponding to carbon element is 245 nm to 250 nm, and at the same time determined that the second characteristic wavelength range corresponding to nickel catalyst element is 340 nm to 345 nm.

[0143] Secondly, all data points were scanned in the first characteristic wavelength range of 245-250 nm. By comparing the intensity values ​​corresponding to each wavelength position, the first spectral peak with the largest intensity at a wavelength of 247.8 nm was identified, and the peak intensity of 12500 counts at this position was recorded as a characteristic of carbon element.

[0144] Then, all data points were scanned in the second characteristic wavelength range of 340-345 nm. By comparing the intensity values ​​corresponding to each wavelength position, the second spectral peak with the greatest intensity at wavelength 341.4 nm was identified, and the peak intensity of 4800 count at this position was recorded as a characteristic of the metal catalyst element.

[0145] Subsequently, the carbon element characteristic count of 12500 and the metal catalyst element characteristic count of 4800 are compared. The calculation formula is that the spectral ratio is equal to the carbon element characteristic count divided by the metal catalyst element characteristic count, that is, spectral ratio = 12500 ÷ 4800 = 2.604. In this calculation, 12500 is the numerical unit count of the carbon element characteristic count, 4800 is the numerical unit count of the metal catalyst element characteristic count, and 2.604 is the dimensionless spectral ratio.

[0146] The resulting spectral ratio of 2.604 was ultimately passed as a key parameter to subsequent processing steps.

[0147] In this embodiment of the application, the step scheme establishes a reliable quantitative index of the phase transition state of the material by accurately extracting the intensity of characteristic spectral lines and calculating the ratio, providing key parameter basis for the precise control of subsequent processing.

[0148] To effectively improve signal quality and suppress noise interference, in some embodiments, step 103: performing time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal includes:

[0149] Step 501: Determine the plasma emission period based on the comparison result between the spectral ratio and the preset ratio threshold.

[0150] In step 501, the preset ratio threshold is a critical value pre-set according to the material properties for judging the plasma luminescence state, and the plasma luminescence period refers to the time interval during which the plasma luminescence intensity reaches an effective detection level.

[0151] In this embodiment of the application, the calculated spectral ratio is compared with a preset ratio threshold, and then the material phase transition state is determined based on the comparison result, thereby determining the effective time range of plasma luminescence.

[0152] Step 502: Set a time-gated window corresponding to the emission period. The start time of the time-gated window is set based on the pulse trigger signal, and the window width is dynamically adjusted according to the plasma lifetime.

[0153] In step 502, the termination time of the time-gated window is determined by the start time plus the window width dynamically adjusted according to the plasma lifetime, wherein the window width is the time length set based on the plasma luminescence decay characteristics.

[0154] The pulse trigger signal is an electrical synchronization signal generated synchronously by the internal control system of the femtosecond laser each time a laser pulse is emitted. This signal is used to identify the start time of each laser pulse and provide a precise time reference for subsequent time-gated processing.

[0155] The lifetime of plasma refers to the time period from the generation of plasma by femtosecond laser on carbon nanotube films until its luminescence intensity decays to a specific proportion of the initial value. This lifetime data is obtained by pre-measuring the plasma luminescence decay curve.

[0156] In this embodiment, the pulse trigger signal is used as the time reference, and the corresponding delay time is set as the window start time according to the determined plasma emission period. Then, the window width is dynamically adjusted according to the duration characteristics of plasma emission to form a complete time-gated window.

[0157] Step 503: Within the time-gated window, the plasma spectral signals within multiple consecutive pulse cycles are integrated over time to obtain the time-integrated signal, while the background noise signal within the laser pulse interval is recorded.

[0158] In step 503, "continuous" in multiple continuous pulse cycles refers to a sequence of pulses that are adjacent in time and uninterrupted. The continuity is determined by detecting whether the time interval between the pulse trigger signals is constant. For example, when 10 consecutive pulse trigger signals are detected with an interval of 1 millisecond, it is determined that these 10 pulses constitute a continuous pulse cycle.

[0159] In this application, the pulse period specifically includes the laser pulse action period and the subsequent interval period. The total duration of each period is determined by the repetition frequency set by the laser. For example, at a repetition frequency of 100 kHz, each pulse period is 10 microseconds, where the pulse action period is on the order of femtoseconds and the remaining time is the interval period.

[0160] The laser pulse interval refers to the time interval between two adjacent laser pulses. This interval is obtained by subtracting the pulse duration from the total pulse cycle duration. Each pulse cycle contains one pulse duration and one pulse interval.

[0161] The time-integrated signal is the result of accumulating the signal within a time-gated window, while the background noise signal is the environmental noise data collected during the laser pulse interval.

[0162] In this embodiment of the application, within a set time-gated window, the plasma spectral signals in multiple consecutive pulse cycles are accumulated over time, while background noise signals are collected as a reference during the intervals between these pulse cycles.

[0163] Step 504: Remove the corresponding background noise signal from the time integral signal within each pulse period to obtain the denoised plasma spectrum signal.

[0164] In step 504, the denoised plasma spectral signal is the pure signal after removing background noise.

[0165] In this embodiment of the application, the background noise signal corresponding to the acquisition is removed from the time integral signal obtained in each pulse cycle, which can eliminate the influence of environmental noise and obtain a net signal that reflects the true plasma luminescence characteristics.

[0166] Step 505: The Kalman filter algorithm is used to preprocess each denoised plasma spectral signal. The denoised plasma spectral signals in multiple consecutive pulse periods after preprocessing are accumulated and averaged to obtain the spectral integral signal.

[0167] In this embodiment, the Kalman filter algorithm is used to preprocess each denoised plasma spectral signal, and the signal quality is improved by iterative calculation of state prediction and measurement update. Then, the signals of multiple consecutive pulse cycles after processing are accumulated and summed and the average value is calculated to obtain the final spectral integral signal.

[0168] Here is a specific example:

[0169] Following the aforementioned embodiment, a research institution first compared the spectral ratio of 2.604 with a preset ratio threshold of 2.0. Since 2.604 is greater than 2.0, the plasma emission period was determined to be the interval between 10 nanoseconds and 30 nanoseconds after the laser pulse. The start time of the time-gated window was set to open 10 nanoseconds after the pulse trigger signal, and the window width was dynamically adjusted to 20 nanoseconds according to the plasma emission decay characteristics.

[0170] Then, within this time-gated window, the plasma spectral signal within 50 consecutive pulse cycles is integrated over time to obtain a time-integrated signal value of 8500 counts for each cycle. At the same time, the background noise signal is recorded during the laser pulse interval, i.e., from 40 nanoseconds to 60 nanoseconds after the laser action ends, and the average background noise value is measured to be 500 counts.

[0171] Then, the corresponding background noise of 500 counts is subtracted from the 8500 counts of the time integral signal within each pulse cycle to obtain the denoised plasma spectrum signal of 8000 counts. These net signals are preprocessed using a Kalman filter algorithm, which optimizes the signal through two steps: state prediction and measurement update. The state prediction formula is as follows: ,in This represents the current predicted signal value in units, and A represents the state transition coefficient, which is dimensionless and takes the value of 0.95. The measurement update formula represents the unit count of the signal value at the previous moment. ,in This indicates the unit count of the updated signal value. This indicates that the Kalman gain is dimensionless, 0.8. This indicates the current measurement value is 8000. This indicates that the observation coefficient is dimensionless (1.0), and after filtering and calculation, the optimized signal value is 7980 counts.

[0172] Finally, the denoised plasma spectral signals processed by 50 consecutive pulse cycles are summed to obtain a total signal value of 399,000 counts. This total signal value is then divided by the number of cycles, 50, to obtain an average signal value of 7,980 counts. The final spectral integral signal of 7,980 counts is obtained, which will be used for subsequent spectral feature recognition processing.

[0173] In this embodiment of the application, the step scheme improves signal quality through precise time gating and multiple noise suppression processes, providing a reliable data foundation for subsequent feature recognition.

[0174] Optionally, step 105: calculating the energy adjustment of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm includes:

[0175] Step 601: Extract the intensity distribution data of the characteristic peak combination from the spectral features, and compare the intensity distribution data with the preset reference intensity distribution under the standard fully vaporized state.

[0176] In step 601, the intensity distribution data of the characteristic peak combination is the set of intensity values ​​corresponding to each characteristic peak in the spectral features, and the reference intensity distribution is the set of ideal intensity values ​​of each characteristic peak under standard complete vaporization conditions.

[0177] In this embodiment of the application, firstly, the intensity values ​​of each characteristic peak contained in the characteristic peak combination are read from the spectral features to form the intensity distribution data currently detected; then, the intensity distribution data is compared item by item with the reference intensity distribution under the pre-stored standard fully vaporized state.

[0178] Step 602: Based on the comparison results, calculate the degree of difference between the intensity distribution data and the reference intensity distribution using the weighted Euclidean distance algorithm.

[0179] In step 602, the degree of difference is a comprehensive index that quantifies the degree of deviation between the current intensity distribution and the reference intensity distribution.

[0180] In this embodiment, firstly, the weighted Euclidean distance algorithm is used to calculate the degree of difference between the two intensity distributions; then, corresponding weight coefficients are assigned according to the importance of different characteristic peaks; finally, the final difference value is obtained by the calculation of the weighted sum of squares and the square root.

[0181] Step 603: Input the difference degree into the energy compensator constructed based on fuzzy control, and map the difference degree into an energy compensation level through the fuzzy inference rules pre-stored in the energy compensator.

[0182] In step 603, the energy compensator is a computing device designed based on the fuzzy control principle, and the energy compensation level is a graded index that characterizes the amount of energy that needs to be compensated.

[0183] In this embodiment of the application, firstly, the calculated difference value is input into the energy compensator; then, logical judgment is performed through pre-stored fuzzy inference rules to map the continuous difference value into discrete energy compensation levels.

[0184] Step 604: Based on the energy compensation level and the spatial location information of the current processing point, obtain the corresponding energy adjustment amount from the preset energy adjustment amount lookup table.

[0185] In step 604, the current processing point refers to the specific position coordinates of the femtosecond laser beam performing processing on the surface of the carbon nanotube film. The spatial position information of the current processing point includes the two-dimensional coordinate data of the processing point on the surface of the carbon nanotube film and its corresponding film thickness information. This information is obtained by matching the coordinate data read by the position encoder with the pre-measured film thickness distribution map.

[0186] The coordinates are obtained in real time by the position encoder of the laser scanning system. Their physical meaning is the instantaneous spatial position of the interaction between the laser beam and the thin film material. The energy adjustment lookup table is a pre-established data table containing energy adjustment amounts corresponding to different compensation levels and spatial positions.

[0187] In this embodiment of the application, the corresponding energy adjustment amount is obtained by performing a joint search in a preset lookup table based on the determined energy compensation level and the spatial coordinates of the current processing point.

[0188] To achieve precise control of pulse energy, in some embodiments, step 105: adjusting the driving voltage of the acousto-optic modulator used to control the femtosecond laser output according to the energy adjustment amount to adjust the pulse energy of the femtosecond laser includes:

[0189] Step 701: Convert the energy adjustment amount into the adjustment value of the acousto-optic modulator drive voltage.

[0190] In step 701, the adjustment value of the acousto-optic modulator drive voltage refers to the amount of voltage change that needs to be adjusted.

[0191] In this embodiment, based on the correspondence between the energy adjustment amount and the driving voltage, the energy adjustment amount is converted into a specific voltage adjustment value by looking up a table or by calculation.

[0192] Step 702: Based on the adjustment value of the driving voltage, a corresponding voltage control signal is generated by a digital-to-analog converter, and the voltage control signal is sent to the driving circuit of the acousto-optic modulator.

[0193] In step 702, the digital-to-analog converter is an electronic device that converts digital signals into analog signals, the voltage control signal is an analog electrical signal used to control the magnitude of the voltage, and the drive circuit is an electronic circuit used to amplify and control the signal.

[0194] In this embodiment, the adjustment value of the driving voltage is input to the digital-to-analog converter to generate a corresponding analog voltage signal, and the signal is transmitted to the driving circuit of the acousto-optic modulator.

[0195] Step 703: The voltage control signal is amplified to the working level by the driving circuit and applied to the piezoelectric transducer of the acousto-optic modulator to adjust the working voltage of the acousto-optic modulator in order to control the pulse energy output of the femtosecond laser.

[0196] In step 703, the operating level is the voltage level required for the drive circuit to operate normally, and the piezoelectric transducer is a device that converts electrical signals into mechanical vibrations.

[0197] In this embodiment, firstly, after receiving the voltage control signal, the driving circuit amplifies the voltage control signal through an internal amplifier circuit to raise it to the required operating level; then, the amplified voltage control signal is applied to the piezoelectric transducer of the acousto-optic modulator; finally, the diffraction efficiency of the laser is adjusted by changing the operating voltage, thereby controlling the pulse energy output of the femtosecond laser.

[0198] In the embodiments of this application, this step scheme realizes the accurate conversion from digital control quantity to physical energy output, ensuring the real-time and precise control of femtosecond laser pulse energy.

[0199] Figure 3 A schematic diagram of a pulse energy control system for femtosecond laser processing of carbon nanotube thin films, provided for embodiments of this application, is shown. The system includes the following modules:

[0200] The acquisition module 31 is used to acquire the plasma spectral signal and corresponding spectral intensity data generated when a femtosecond laser acts on a carbon nanotube film to process a micropore array.

[0201] Extraction module 32 is used to extract carbon element features and metal catalyst element features from the spectral intensity data, and generate spectral ratios based on the carbon element features and metal catalyst element features.

[0202] The generation module 33 is used to perform time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal.

[0203] The identification module 34 is used to perform moving average processing and spectral peak locking processing on the spectral integral signal using an adaptive filtering algorithm to identify spectral features.

[0204] The calculation module 35 is used to calculate the energy adjustment amount of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm, and adjust the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser according to the energy adjustment amount, so as to adjust the pulse energy of the femtosecond laser and realize closed-loop control of the micropore array processing process on the carbon nanotube film.

[0205] The pulse energy control system for femtosecond laser processing of carbon nanotube films in this application embodiment is used to implement the aforementioned pulse energy control method for femtosecond laser processing of carbon nanotube films. Therefore, the specific implementation of the pulse energy control system for femtosecond laser processing of carbon nanotube films can be found in the embodiment section of the pulse energy control method for femtosecond laser processing of carbon nanotube films above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0206] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the pulse energy control method for femtosecond laser processing of carbon nanotube films as described above.

[0207] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described femtosecond laser processing carbon nanotube thin films pulse energy control methods.

[0208] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0209] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the pulse energy control method for femtosecond laser processing of carbon nanotube thin films.

[0210] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0211] The pulse energy control method and system for femtosecond laser processing of carbon nanotube thin films provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for controlling the pulse energy of femtosecond laser processing of carbon nanotube thin films, characterized in that, include: Collect plasma spectral signals and corresponding spectral intensity data generated when femtosecond lasers act on carbon nanotube films to process micropore arrays; Carbon element characteristics and metal catalyst element characteristics are extracted from the spectral intensity data, and a spectral ratio is generated based on the carbon element characteristics and metal catalyst element characteristics. Based on the spectral ratio, the plasma spectral signal is subjected to time-gated integration processing to generate a spectral integrated signal; An adaptive filtering algorithm is used to perform moving average processing and spectral peak locking processing on the spectral integral signal in order to identify spectral features; Based on the spectral characteristics, the energy adjustment amount of the femtosecond laser is calculated using an energy compensation algorithm. According to the energy adjustment amount, the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser is adjusted to adjust the pulse energy of the femtosecond laser, thereby realizing closed-loop control of the micropore array processing process on the carbon nanotube film.

2. The method according to claim 1, characterized in that, The adaptive filtering algorithm is used to perform moving average processing and peak locking processing on the spectral integral signal to identify spectral features, including: The spectral integral signal is filtered using an LMS adaptive filter to obtain the filtered signal. The filtered signal is subjected to a moving average process to obtain a smoothed signal; The smoothed signal is subjected to spectrum analysis, and the peak values ​​with amplitudes greater than a preset dynamic threshold are identified from the spectrum analysis results using a peak detection algorithm. The spectral peaks are matched with a preset spectral feature library to screen out characteristic peak combinations that characterize the complete vaporization of carbon nanotube films, and spectral features are constructed based on the characteristic peak combinations.

3. The method according to claim 2, characterized in that, The process of matching the spectral peaks with a preset spectral feature library to screen out characteristic peak combinations that characterize the complete vaporization of carbon nanotube films, and constructing spectral features based on the characteristic peak combinations, includes: The wavelength position of the spectral peak is compared one by one with the reference wavelength position in the spectral feature library to calculate the wavelength matching degree of each spectral peak. Based on the wavelength matching degree and the amplitude of the spectral peak, the feature significance of each spectral peak is calculated, and spectral peaks with feature significance greater than a preset feature threshold are selected to form a candidate peak set; Peaks containing carbon element characteristic spectral lines and peaks containing metal characteristic spectral lines are selected from the candidate peak set to form characteristic peak combinations. The wavelength position and amplitude value of each peak are extracted from the characteristic peak combinations to establish a correspondence table between wavelength and amplitude. According to the corresponding table, the ratio between the amplitude of the characteristic spectral line of carbon element and the amplitude of the characteristic spectral line of metal is calculated, and the ratio is combined with the amplitude values ​​of each peak in the characteristic peak combination to form a composite feature; The composite features are encapsulated according to a preset data structure to construct spectral features.

4. The method according to claim 1, characterized in that, The step of extracting carbon elemental features and metal catalyst elemental features from the spectral intensity data, and generating a spectral ratio based on the carbon elemental features and metal catalyst elemental features, includes: The first characteristic wavelength range corresponding to carbon and the second characteristic wavelength range corresponding to metal catalyst elements are determined from the spectral intensity data. Identify the first spectral peak with the highest intensity within the first characteristic wavelength range, and record the peak intensity corresponding to the first spectral peak as a carbon element feature. Identify the second spectral peak with the highest intensity within the second characteristic wavelength range, and record the peak intensity corresponding to the second spectral peak as the elemental characteristic of the metal catalyst. The spectral ratio is obtained by calculating the ratio between the carbon element characteristics and the metal catalyst element characteristics.

5. The method according to claim 1, characterized in that, The step of performing time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal includes: The plasma emission period is determined based on the comparison between the spectral ratio and the preset ratio threshold. A time-gated window is set to correspond to the emission period. The start time of the time-gated window is set based on the pulse trigger signal, and the window width is dynamically adjusted according to the plasma lifetime. Within the time-gated window, the plasma spectral signals within multiple consecutive pulse cycles are integrated over time to obtain the time-integrated signal, while the background noise signal during the laser pulse interval is recorded. The corresponding background noise signal is removed from the time integral signal within each pulse period to obtain the denoised plasma spectrum signal. The Kalman filter algorithm is used to preprocess each denoised plasma spectral signal. The denoised plasma spectral signals in multiple consecutive pulse periods after preprocessing are accumulated and averaged to obtain the spectral integral signal.

6. The method according to claim 1, characterized in that, The step of calculating the energy adjustment of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm includes: Intensity distribution data of characteristic peak combinations are extracted from the spectral features, and the intensity distribution data is compared with the preset reference intensity distribution under a standard fully vaporized state. Based on the comparison results, the difference between the intensity distribution data and the reference intensity distribution is calculated using the weighted Euclidean distance algorithm; The difference degree is input to an energy compensator built based on fuzzy control, and the difference degree is mapped to an energy compensation level through the fuzzy inference rules pre-stored in the energy compensator. Based on the energy compensation level and the spatial location information of the current processing point, the corresponding energy adjustment amount is obtained from the preset energy adjustment amount lookup table.

7. The method according to claim 1, characterized in that, The step of adjusting the driving voltage of the acousto-optic modulator used to control the femtosecond laser output according to the energy adjustment amount, so as to adjust the pulse energy of the femtosecond laser, includes: The energy adjustment amount is converted into an adjustment value for the acousto-optic modulator drive voltage; Based on the adjustment value of the driving voltage, a corresponding voltage control signal is generated by a digital-to-analog converter, and the voltage control signal is sent to the driving circuit of the acousto-optic modulator. The driving circuit amplifies the voltage control signal to the operating level and applies it to the piezoelectric transducer of the acousto-optic modulator to adjust the operating voltage of the acousto-optic modulator in order to control the pulse energy output of the femtosecond laser.

8. A pulse energy control system for femtosecond laser processing of carbon nanotube thin films, characterized in that, include: The acquisition module is used to acquire the plasma spectral signals and corresponding spectral intensity data generated when a femtosecond laser acts on a carbon nanotube film to process a micropore array. The extraction module is used to extract carbon element features and metal catalyst element features from the spectral intensity data, and generate spectral ratio values ​​based on the carbon element features and metal catalyst element features; The generation module is used to perform time-gated integration processing on the plasma spectral signal based on the spectral ratio to generate a spectral integrated signal; The identification module is used to perform moving average processing and spectral peak locking processing on the spectral integral signal using an adaptive filtering algorithm to identify spectral features; The calculation module is used to calculate the energy adjustment amount of the femtosecond laser based on the spectral characteristics using an energy compensation algorithm, and adjust the driving voltage of the acousto-optic modulator used to control the output of the femtosecond laser according to the energy adjustment amount, so as to adjust the pulse energy of the femtosecond laser and realize closed-loop control of the micropore array processing process on the carbon nanotube film.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the pulse energy control method for femtosecond laser processing of carbon nanotube films as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the pulse energy control method for femtosecond laser processing of carbon nanotube films as described in any one of claims 1 to 7.

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