System and method for laser processing of carbon-based chip

Abstract

The embodiment of the invention provides a system and method for laser processing of a carbon-based chip, and the system comprises a multi-wavelength laser emission module which is used for emitting laser beams suitable for carbon-based chips with different crystal structures according to laser parameters; the intelligent control device is used for dynamically adjusting laser parameters according to thermal and optical characteristics of the carbon-based chip based on a processing strategy and sending the laser parameters to the multi-wavelength laser emission module; the self-adaptive machine learning module is used for optimizing a processing strategy according to the monitoring data and the processing characteristics of the carbon-based chip and sending an optimization result to the intelligent control device; the micro-nano laser polishing device is used for treating the surface of the carbon-based chip; the cutting mechanism is used for carrying out hidden cutting on the carbon-based chip; and the multi-mode sensing and analyzing system is used for monitoring the structural integrity of the carbon-based chip in the processing process and sending monitoring data to the self-adaptive machine learning module.

Description

Technical Field

[0001] This document relates to the field of laser processing technology, and in particular to a system and method for laser processing carbon-based chips. Background Art

[0002] Chip cutting is one of the key steps in the integrated circuit manufacturing process. Traditional chip cutting methods usually use technologies such as mechanical cutting or chemical etching, but these methods have some limitations. Mechanical cutting is prone to mechanical stress, which may cause chip damage or cracks. Chemical etching may cause residues or chemical contamination, which will have a negative impact on chip performance and reliability. These problems are particularly significant for carbon-based chips. Carbon-based materials such as graphene, carbon nanotubes or diamonds have unique physical and chemical properties and are more sensitive to traditional cutting methods. Mechanical cutting may destroy the structural integrity of carbon-based materials, while chemical etching may change their electronic properties.

[0003] In order to solve these problems, laser processing technology has gradually been introduced into the field of chip cutting in recent years. Laser cutting has the characteristics of non-contact, high precision and high efficiency, and can achieve precise cutting of chips. However, traditional laser cutting methods still have some challenges. First, laser cutting is prone to heat-affected zones, which may lead to problems such as increased chip surface roughness, residue generation or material splashing. Secondly, when processing carbon-based chips, the laser cutting process must fully consider their unique material properties and complex structures to ensure the accuracy and consistency of cutting. This requires fine control of laser parameters and the development of cutting strategies specifically for carbon-based materials.

[0004] As a new type of cutting technology, laser stealth cutting is a cutting method that reduces the heat-affected zone by controlling the laser parameters and processing conditions. By reasonably adjusting parameters such as laser power, pulse width and scanning speed, local melting and rapid cooling can be achieved during chip cutting, reducing heat conduction and heat diffusion, thereby reducing the size of the heat-affected zone. This can effectively reduce the roughness of the carbon-based chip surface and the possibility of residue generation.

[0005] Secondly, laser polishing is a method that combines laser irradiation and chemical reaction to trim and smooth the cut surface of carbon-based chips. After cutting, the laser beam can be directed to the cut surface to cause microstructural changes on the surface of the material through thermal excitation and surface reaction, thereby further improving the smoothness and quality of the surface. Laser polishing can flexibly adjust parameters such as laser power, irradiation time, and chemical reaction conditions according to the characteristics of the chip material and cutting requirements to achieve precise control and optimization of the surface of the carbon-based chip.

[0006] Machine learning is an artificial intelligence technology that can be applied to the control and optimization of the chip cutting process by building a deep neural network model to achieve learning and inference of complex tasks. By training and learning a large amount of cutting data and polishing data, an accurate model can be established to predict and adjust laser parameters, processing conditions, and cutting / polishing paths, etc., to achieve adaptive control and optimization of the cutting process. Deep learning can improve the accuracy and stability of chip cutting, reduce the need for manual intervention and optimization, and improve cutting efficiency and consistency.

[0007] Therefore, developing a chip cutting system and method that can combine laser stealth cutting, laser polishing and machine learning to solve problems such as increased surface roughness, residue generation or material splashing in the carbon-based chip cutting method, and realize the prediction and adjustment of process such as laser parameters, processing conditions and cutting paths, reduce manual intervention and optimization to improve the quality and reliability of carbon-based chips after cutting, has become an urgent problem to be solved by scientific researchers. Summary of the invention

[0008] The purpose of the present invention is to provide a system and method for laser processing carbon-based chips, aiming to solve the above-mentioned problems in the prior art.

[0009] The present invention provides a system for laser processing a carbon-based chip, comprising:

[0010] A multi-wavelength laser emission module is used to emit laser beams suitable for carbon-based chips with different crystal structures according to laser parameters;

[0011] An intelligent control device, connected to the multi-wavelength laser emission module, for dynamically adjusting laser parameters according to thermal and optical properties of the carbon-based chip based on a processing strategy, and sending the laser parameters to the multi-wavelength laser emission module;

[0012] An adaptive machine learning module, connected to the intelligent control device, for optimizing the processing strategy according to the monitoring data and the processing characteristics of the carbon-based chip, and sending the optimization result to the intelligent control device;

[0013] Micro-nano laser polishing device, used to process the surface of carbon-based chips;

[0014] A cutting mechanism for performing invisible cutting of carbon-based chips;

[0015] A multimodal sensing and analysis system is connected to the adaptive machine learning module, and is used to monitor the structural integrity of the carbon-based chip during processing and send the monitoring data to the adaptive machine learning module.

[0016] The present invention provides a system and method for laser processing of carbon-based chips, and the system for laser processing of carbon-based chips comprises:

[0017] Dynamically adjusting laser parameters according to thermal and optical properties of the carbon-based chip based on a processing strategy through an intelligent control device, and sending the laser parameters to the multi-wavelength laser emission module;

[0018] The laser beam suitable for carbon-based chips with different crystal structures is emitted according to laser parameters through a multi-wavelength laser emission module;

[0019] The laser induced stress wave technology is used to perform hidden chip cutting through the cutting mechanism;

[0020] A micro-nano laser polishing device uses femtosecond laser technology to perform atomic-level polishing on the cut surface;

[0021] Monitoring the structural integrity of the carbon-based chip during processing through a multimodal sensing and analysis system, and sending the monitoring data to the adaptive machine learning module;

[0022] Through the adaptive machine learning module, the processing strategy is optimized according to the monitoring data and the processing characteristics of the carbon-based chip, and the optimization results are sent to the intelligent control device.

[0023] By adopting the embodiment of the present invention, laser hidden cutting, laser polishing and machine learning are combined to solve the problems existing in the traditional chip cutting method, and the prediction and adjustment of the process such as laser parameters, processing conditions and cutting path can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 is a schematic diagram of a system for laser processing a carbon-based chip according to an embodiment of the present invention;

[0026] Figure 2 It is a flow chart of the method for laser processing carbon-based chips according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0028] System Example

[0029] According to an embodiment of the present invention, a system for laser processing of carbon-based chips is provided. Figure 1 Schematic diagram of a system for laser processing carbon-based chips according to an embodiment of the present invention. Figure 1 As shown, the system for laser processing carbon-based chips according to an embodiment of the present invention specifically includes:

[0030] The multi-wavelength laser emission module 10 is used to emit laser beams suitable for carbon-based chips with different crystal structures according to laser parameters; specifically used for:

[0031] According to the different allotropes of carbon-based chips, the quantum confinement effect, band structure and optical response characteristics of carbon-based materials, the optimal laser wavelength is selected, and specific layers of the multi-layer carbon-based chip structure are selectively processed.

[0032] The intelligent control device 11 is connected to the multi-wavelength laser emission module and is used to dynamically adjust the laser parameters according to the thermal and optical properties of the carbon-based chip based on the processing strategy, and send the laser parameters to the multi-wavelength laser emission module; specifically used to:

[0033] The power density, pulse width and repetition frequency of the laser are adjusted in real time according to changes in the thermal conductivity, thermal expansion coefficient and light absorption characteristics of the carbon-based chip, material thickness, density or composition.

[0034] For graphene, it is possible to utilize its high absorption rate in the visible to infrared spectrum range to select the appropriate wavelength for precise processing; for carbon nanotubes, its chirality and diameter-dependent optical absorption characteristics are considered to select the wavelength that matches the specific chirality; for diamond films, its wide band gap characteristics are utilized to select the ultraviolet band for efficient processing.

[0035] The adaptive machine learning module 12 is connected to the intelligent control device and is used to optimize the processing strategy according to the monitoring data and the processing characteristics of the carbon-based chip, and send the optimization result to the intelligent control device; specifically, it is used to:

[0036] A reinforcement learning algorithm is used to predict the behavior of different carbon-based materials under various processing conditions, and to perform real-time quality control, identify and repair potential defects, and detect and handle abnormal situations. By simulating the response of carbon-based chips under different laser parameters, the processing strategy is optimized to improve cutting accuracy and efficiency.

[0037] The adaptive machine learning algorithm can form a unique response based on the unique crystal structure and physical and chemical properties of different carbon-based chip materials, accurately control the interaction between laser and material, and automatically optimize laser power, pulse width, repetition frequency, scanning speed and cutting path.

[0038] The micro-nano laser polishing device 13 is used to process the surface of the carbon-based chip; specifically used for:

[0039] Femtosecond laser technology is used to achieve atomic-level surface flatness without destroying the sp2 or sp3 hybrid structure of the carbon-based chip. By precisely controlling the laser pulse energy and duration, the hexagonal lattice structure of graphene or the tubular structure of carbon nanotubes is avoided from being damaged. The ultra-short pulse characteristics of femtosecond laser are used to selectively act on surface defects or impurities without affecting the strong bonding between carbon atoms. Considering the high thermal conductivity and low heat capacity characteristics of carbon-based materials, special scanning strategies and thermal management technologies are used to prevent structural deformation caused by local overheating. While maintaining the integrity of the basic structure, functional modification of specific areas of the surface is achieved through laser-induced local chemical reactions. According to the needs of different types of carbon-based chips, surface features from nanometer to micrometer scales are processed simultaneously. Through special laser scanning modes, the residual stress introduced during the processing is minimized and the intrinsic electrical and mechanical properties of carbon-based materials are maintained. The ultra-high peak power and ultra-short pulse width of femtosecond laser are used to achieve precise manipulation of single or a few atoms and achieve atomic-level surface flatness.

[0040] By using low-energy, high-repetition-rate femtosecond laser pulses, precise manipulation of carbon atoms can be achieved. For graphene, by utilizing the characteristics of its two-dimensional structure and adjusting the direction of laser polarization, atoms can be selectively removed along a specific lattice direction to maintain the integrity of the hexagonal lattice. For carbon nanotubes, considering their cylindrical structure, a spiral scanning mode is used to evenly remove surface defects without destroying the tube wall structure. For diamond films, by utilizing their wide bandgap characteristics, appropriate photon energy is selected to achieve precise surface atomic desorption. This method ensures that heat diffusion is limited to the nanoscale by precisely controlling the pulse duration at the femtosecond level, effectively avoiding the heat accumulation effect and maintaining the sp2 or sp3 hybrid structure of the carbon material to the greatest extent. At the same time, by adjusting the laser parameters and scanning strategies in real time, precise layer-by-layer removal of surface atoms can be achieved, not only achieving sub-nanometer flatness, but also being able to selectively retain or remove specific surface functional groups to achieve surface chemical modification.

[0041] The cutting mechanism 14 is used for performing hidden cutting of the carbon-based chip; specifically for:

[0042] Laser induced stress wave technology is used to adjust the direction and intensity of the laser induced stress wave according to the structural anisotropy of the carbon-based material; and the high strength and toughness of the carbon-based material are used to achieve clear fracture without generating edge microcracks; at the same time, the atomic configuration of the cutting edge is precisely controlled, the edge electronic state is optimized, and selective and precise segmentation of the multi-layer carbon-based structure is achieved.

[0043] By precisely controlling the laser pulse energy, repetition frequency, wavelength and spatial distribution, a directional local stress field is generated inside the carbon-based chip to achieve high-precision implicit cutting. For graphene, the anisotropy of its two-dimensional structure is utilized to induce stress wave propagation along a specific lattice direction to achieve atomic-level precision cutting. For carbon nanotubes, considering the mechanical properties of its tubular structure, precise segmentation is achieved by regulating the stress wave intensity without destroying the tube wall structure. For diamond film, its high Young's modulus characteristics are utilized to achieve precise cutting through the cumulative effect of high-frequency and low-energy pulses. This method minimizes the heat-affected zone, avoids sp2-sp3 hybridization transformation and defect formation, and uses the high thermal conductivity of carbon materials to quickly dissipate heat and prevent local thermal damage. By adjusting the laser parameters and scanning path in real time, the atomic configuration of the cutting edge can be controlled, the edge electronic state can be optimized, and even functionalized edge structure can be achieved.

[0044] The multimodal sensing and analysis system 15 is connected to the adaptive machine learning module and is used to monitor the structural integrity of the carbon-based chip during the processing and send the monitoring data to the adaptive machine learning module.

[0045] The multimodal sensing and analysis system is specifically: a Raman spectrometer;

[0046] The Raman spectrometer is specifically used for: real-time monitoring of structural changes and defect formation of carbon-based chips; monitoring changes in the degree of graphitization of carbon-based materials; drawing stress distribution maps around cutting areas; distinguishing and quantifying different types of defects; real-time monitoring of changes in the number of layers of multi-layer carbon-based materials; identifying impurities or contaminants that may be introduced; combined with a micro-electrical measurement device, real-time evaluation of the impact of the cutting process on the electrical properties of the material; and monitoring local temperature changes during processing.

[0047] Through high time resolution Raman spectrum acquisition, real-time analysis of characteristic peak changes, including but not limited to G peak (~1580cm -1 )、D peak (~1350cm -1 ) and 2D peak (~2700cm -1For graphene, the intensity ratio of the G peak and the 2D peak and the shape of the 2D peak are monitored to evaluate the change in the number of layers and the stress state in real time. For carbon nanotubes, the displacement of the radial breathing mode (RBM) peak is analyzed to determine the change in tube diameter and chirality. For diamond films, the sp3 carbon characteristic peak (~1332cm -1 ) to evaluate the purity and stress state; by analyzing the intensity ratio of the G peak and the D peak, the change in defect density can be quantified in real time, where an increase in the D peak intensity indicates an increase in defects or disorder; at the same time, by monitoring the position shift of the G peak, the stress state introduced by processing can be evaluated; based on these real-time data, the system can timely adjust laser parameters such as power, pulse width, scanning speed, etc. to minimize structural damage; in addition, by analyzing the shape and intensity of the G' peak (also called the 2D peak), the change in interlayer coupling strength can be evaluated, providing guidance for the precise processing of multilayer carbon-based structures.

[0048] The technical solution of the present invention is described in detail below in conjunction with specific embodiments. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0049] Embodiment 1:

[0050] This embodiment takes cutting of graphene chips as an example, and the working process is described in detail as follows:

[0051] Laser wavelength selection:

[0052] Using a multi-wavelength laser emission module, we select the optimal laser wavelength for the unique optical properties of graphene. Considering the high absorption rate of graphene in the visible to near-infrared spectrum range (400-1100nm), we choose a femtosecond laser with a wavelength of 800nm. This wavelength can not only be effectively absorbed by graphene, but also minimize the impact on the substrate material. At the same time, the ultra-short pulse characteristics of the femtosecond laser can significantly reduce the heat-affected zone and maintain the lattice integrity of graphene.

[0053] Parameter optimization:

[0054] The intelligent control device optimizes the laser parameters according to the physical properties of graphene. Considering the high thermal conductivity (~5000W / mK) and low heat capacity (~700J / kgK) of graphene, we use a laser parameter setting with a high repetition rate (80MHz) and low single pulse energy (~1nJ). This configuration can achieve effective processing by superposition of multiple pulses while maintaining low heat accumulation effects. The laser power density is set at 10-12W / cm 2 This range is sufficient to initiate the multiphoton absorption process and achieve precise material removal while avoiding excessive thermal damage.

[0055] Hidden cutting process:

[0056] Laser-induced stress wave technology is used for chip implicit cutting. We use a linearly polarized laser to align the polarization direction with the lattice direction of graphene. This method can use the structural anisotropy of graphene to induce stress wave propagation along specific lattice directions (such as sawtooth or armchair edges) to achieve atomic-level precision cutting. The laser scanning speed is set to 10mm / s, which ensures sufficient energy deposition to achieve cutting without causing excessive heat accumulation.

[0057] Surface finish:

[0058] Surface polishing is performed using femtosecond laser technology. We use circularly polarized lasers, adjust the pulse width to 30fs, and reduce the single pulse energy to 0.5nJ. Circular polarization can achieve more uniform energy distribution and avoid preferential etching directions. By precisely controlling the laser energy density (~0.1J / cm 2 ), we can achieve material removal at the single atomic layer level. Using raster scanning mode, the overlap rate of each scan is set to 50% to ensure uniform polishing effect.

[0059] Real-time monitoring:

[0060] The processing process is monitored in real time by a Raman spectrometer with high time resolution (~1ms). We mainly focus on the following characteristic peaks:

[0061] G peak (~1580cm -1 ): reflects the in-plane vibration mode of sp2 carbon atoms;

[0062] 2D peak (~2700cm -1 ): used to determine the number of layers and stress state;

[0063] D peak (~1350cm -1 ): Indicates a defect or edge effect.

[0064] By analyzing the intensity ratio of the G peak and the 2D peak (2D / G), we can evaluate the change in the number of graphene layers in real time. The ideal 2D / G ratio of single-layer graphene should be greater than 2. At the same time, the half-width (FWHM) of the 2D peak is monitored. The FWHM of the 2D peak of single-layer graphene should be less than 30cm -1 .

[0065] In addition, we also pay attention to the position shift of the G peak, every 1cm -1 The red shift corresponds to a strain of about 0.003. In this way, we can precisely control the stress introduced during processing to ensure that the fracture limit of graphene (~25%) is not exceeded.

[0066] Adaptive Optimization:

[0067] The adaptive machine learning algorithm dynamically optimizes the processing parameters based on real-time monitoring data. We use a reinforcement learning algorithm, with Raman spectroscopy data as state input and laser parameters (power, scanning speed, repetition frequency, etc.) as action output. The reward function design considers the following factors:

[0068] Minimize D peak intensity and keep defect density low;

[0069] Maintaining the ideal I2D / IG ratio and 2D peak FWHM to ensure graphene quality;

[0070] Control the G peak position deviation within a reasonable range to avoid excessive stress.

[0071] The algorithm optimizes processing efficiency while ensuring processing quality through multiple iterations of learning. For example, when the D peak intensity increases, the algorithm will automatically reduce the laser power or increase the scanning speed; when a multi-layer area (2D / G<2) is found, the laser energy density will be appropriately increased to achieve layer-by-layer peeling.

[0072] Through this fine parameter control and real-time optimization, we can achieve high-precision processing of graphene chips while maintaining their unique electrical, thermal and mechanical properties to the greatest extent possible. This method is not only applicable to single-layer graphene, but can also be extended to the precise processing of multi-layer graphene and other two-dimensional materials, providing important technical support for the development of new carbon-based electronic devices and quantum devices.

[0073] Embodiment 2:

[0074] This embodiment takes cutting of carbon nanotube chips as an example, and the working process is as follows:

[0075] Laser wavelength selection:

[0076] Using a multi-wavelength laser emission module, we select the optimal laser wavelength for the unique optical properties of carbon nanotubes. Considering that the optical absorption properties of carbon nanotubes strongly depend on their chirality and diameter, we use a tunable femtosecond laser system with a wavelength range of 400-1600nm. This system can select the optimal absorption wavelength for different types of carbon nanotubes (metallic or semiconductor). For example, for semiconductor carbon nanotubes with a diameter of 1.2nm, we choose a laser with a wavelength of about 980nm to match its first exciton transition energy level; for metallic carbon nanotubes, we choose a wavelength of about 700nm, corresponding to its plasma resonance.

[0077] Parameter optimization:

[0078] The intelligent control device optimizes the laser parameters according to the physical characteristics of carbon nanotubes. Considering the one-dimensional structure and high thermal conductivity of carbon nanotubes (~3000W / mK along the axial direction), we use a laser parameter setting with low repetition rate (1kHz) and high single pulse energy (~100μJ). This configuration allows sufficient heat dissipation between each pulse to prevent damage to the tube wall structure caused by the heat accumulation effect. The laser power density is set at 10-13W / cm 2 This range is sufficient to induce multiphoton absorption and tunneling ionization processes, achieving precise material removal while minimizing the heat-affected zone.

[0079] Hidden cutting process:

[0080] Chip hidden cutting is performed using laser induced stress wave technology. We use circularly polarized lasers to ensure uniform action on all oriented carbon nanotubes. The laser focus diameter is adjusted to 5μm, slightly larger than the typical carbon nanotube length (1-3μm) to ensure complete cutting. The laser scanning speed is set to 0.1mm / s. This relatively slow speed ensures sufficient energy deposition to achieve cutting while controlling the heat-affected zone. We use a multiple scanning strategy with a gradual increase in the energy density of each scan. This method can gradually weaken the wall structure of the carbon nanotubes, ultimately achieving precise segmentation without causing large-scale thermal damage.

[0081] Surface finish:

[0082] Surface polishing was performed using femtosecond laser technology. We used a spiral scanning mode, adjusted the pulse width to 50 fs, and reduced the single pulse energy to 10 μJ. The spiral scanning mode can evenly process carbon nanotubes with different orientations and avoid the generation of preferential etching directions. By precisely controlling the laser energy density (~0.5 J / cm 2 ), we can achieve selective removal of surface defects and amorphous carbon without destroying the intrinsic tubular structure of carbon nanotubes. The scan overlap rate is set to 75% to ensure uniform polishing effect.

[0083] Real-time monitoring:

[0084] The processing process is monitored in real time by a Raman spectrometer with high time resolution (~10ms). We mainly focus on the following characteristic peaks:

[0085] G peak (~1590cm -1 ): reflects the tangential vibration mode of sp2 carbon atoms;

[0086] D peak (~1350cm -1 ): indicates defects or disorder;

[0087] RBM peak (100-350cm -1): reflects the radial breathing pattern of carbon nanotubes and is used to determine the tube diameter and chirality.

[0088] By analyzing the position of the RBM peak, we can evaluate the diameter change of carbon nanotubes in real time. The RBM frequency is inversely proportional to the tube diameter, and the relationship is approximately ωRBM = 248 / d (d is the tube diameter, unit: nm). At the same time, the intensity ratio of the G peak and the D peak (G / D) is monitored to evaluate the defect density introduced during processing. Ideally, G / D should be greater than 100, indicating that a high-quality tubular structure is maintained.

[0089] In addition, we also focus on the splitting of the G peak (G+ and G- peak), which can be used to distinguish between metallic and semiconducting carbon nanotubes to ensure that the processing does not selectively destroy one type of nanotube.

[0090] Adaptive Optimization:

[0091] Adaptive machine learning algorithms dynamically optimize processing parameters based on real-time monitoring data. We use a deep reinforcement learning algorithm that takes Raman spectroscopy data and electrical measurement data of the processing area (such as conductivity changes) as state inputs and laser parameters (wavelength, power, scanning speed, repetition frequency, etc.) as action outputs. The function design considers the following factors:

[0092] Minimize D peak intensity and keep defect density low;

[0093] Maintain the intensity and position of the RBM peak to ensure the structural integrity of the carbon nanotubes;

[0094] Control the degree of G peak splitting to maintain the balance between metallic and semiconductor carbon nanotubes;

[0095] Optimize electrical properties, such as maintaining or improving conductivity.

[0096] The algorithm optimizes processing efficiency while ensuring processing quality through multiple iterations of learning. For example, when the D peak intensity is detected to increase, the algorithm will automatically adjust the laser wavelength or reduce the power density; when it is found that some RBM peaks disappear, the scanning strategy will be appropriately adjusted to avoid selectively destroying nanotubes of a specific diameter.

[0097] Through this fine parameter control and real-time optimization, we can achieve high-precision processing of carbon nanotube chips while maintaining their unique one-dimensional quantum confinement effect and excellent electrical and thermal properties to the greatest extent. This method is not only applicable to single-walled carbon nanotubes, but can also be extended to the precise processing of multi-walled carbon nanotubes and carbon nanotube arrays, providing important technical support for the development of new carbon-based electronic devices, sensors and quantum devices.

[0098] Example 3

[0099] This embodiment takes cutting of diamond film chips as an example, and the working process is as follows:

[0100] Laser wavelength selection:

[0101] Using a multi-wavelength laser emission module, we select the optimal laser wavelength for the unique optical properties of diamond. Considering the wide bandgap characteristics of diamond (~5.5eV), we choose a deep ultraviolet femtosecond laser with a wavelength of 213nm (fifth harmonic Nd:YAG). The photon energy corresponding to this wavelength (5.8eV) is slightly higher than the bandgap of diamond, which can achieve efficient single-photon absorption while minimizing the heat-affected zone. In addition, short-wavelength lasers can also provide smaller focused spots, which is conducive to improving processing accuracy.

[0102] Parameter optimization:

[0103] The intelligent control device optimizes the laser parameters according to the physical properties of diamond. Considering the high thermal conductivity (~2200W / mK), high Young's modulus (~1050GPa) and high melting point (~3550℃) of diamond, we use laser parameter settings with high repetition frequency (100kHz) and medium single pulse energy (~10μJ). The pulse duration is set to 100fs. This ultrashort pulse can complete energy deposition before the material heat diffuses, effectively reducing the heat affected zone. The laser power density is set at 10-14W / cm 2 This range is sufficient to induce multiphoton ionization and avalanche ionization processes, achieving efficient material removal.

[0104] Hidden cutting process:

[0105] Laser induced stress wave technology is used for chip hidden cutting. We use linearly polarized lasers to make the polarization direction parallel to the {111} crystal plane of diamond. This can take advantage of the cleavage characteristics of diamond to achieve precise cutting along a specific crystal plane. Laser focusing uses a high numerical aperture (NA = 0.8) ultraviolet objective lens to control the focal diameter to about 300nm. A multiple scanning strategy is used, and the energy density of each scan is gradually increased. The initial energy density is set to 2J / cm 2 , and eventually increased to 5J / cm 2 The scanning speed was set to 1 mm / s, which ensured sufficient energy deposition to achieve cutting while controlling the heat accumulation effect.

[0106] Surface finish:

[0107] Surface polishing is performed using femtosecond laser technology. We use random scanning mode, reduce the pulse width to 50fs, and reduce the single pulse energy to 1μJ. Random scanning mode can avoid the generation of periodic surface structures and achieve a more uniform polishing effect. By precisely controlling the laser energy (~0.8J / cm 2), we can achieve precise desorption of surface atoms without causing damage to the deeper material. The scan overlap rate is set to 90% to ensure full coverage. The entire polishing process is divided into two stages: rough polishing and fine polishing, gradually reducing the pulse energy and increasing the scanning speed to ultimately achieve atomic-level flatness.

[0108] Real-time monitoring:

[0109] The processing process is monitored in real time by a Raman spectrometer with high time resolution (~1ms). We mainly focus on the following characteristic peaks:

[0110] Diamond characteristic peak (~1332cm -1 ): reflects the vibration mode of sp3-bonded carbon atoms;

[0111] G peak (~1580cm -1 ): indicates the formation of sp2 carbon structure;

[0112] D peak (~1350cm -1 ): Indicates disordered carbon structure or defects.

[0113] By analyzing the intensity, position and full width at half maximum (FWHM) of diamond characteristic peaks, we can evaluate the diamond quality and stress state in real time. Ideally, the peak position should be at 1332±0.5cm -1 , FWHM should be less than 2cm -1 At the same time, the appearance and intensity changes of the G and D peaks were monitored to evaluate the graphitized or amorphous carbon that may have been produced during the processing.

[0114] In addition, we introduce photoluminescence (PL) spectroscopy analysis to monitor the formation of NV centers (zero phonon line at 637 nm) in real time, which is crucial for developing quantum sensing and quantum computing applications.

[0115] Adaptive Optimization:

[0116] The adaptive machine learning algorithm dynamically optimizes the processing parameters based on real-time monitoring data. We use a deep reinforcement learning algorithm, taking Raman spectral data, PL spectral data, and electrical measurement data of the processing area (such as breakdown voltage) as state inputs and laser parameters (power, scanning speed, repetition frequency, polarization direction, etc.) as action outputs. The reward function design considers the following factors:

[0117] Maximize the intensity of diamond characteristic peaks and maintain high-quality sp3 structure;

[0118] Minimize the intensity of G and D peaks to avoid graphitization and defect formation;

[0119] Control the position deviation of diamond characteristic peak within ±0.5cm -1 manage residual stress within the range;

[0120] Optimize NV center formation (if necessary);

[0121] Maintain or increase breakdown voltage to ensure device performance.

[0122] The algorithm optimizes processing efficiency while ensuring processing quality through multiple iterations of learning. For example, when the G peak intensity is detected to increase, the algorithm will automatically reduce the single pulse energy or increase the scanning speed; when the diamond characteristic peak is observed to broaden, the laser polarization direction will be adjusted to optimize the stress distribution.

[0123] Through this fine parameter control and real-time optimization, we can achieve high-precision processing of diamond film chips while maintaining their unique wide bandgap characteristics, high breakdown voltage and excellent thermal properties to the greatest extent. This method is not only applicable to ordinary diamond films, but can also be extended to the precise processing of doped diamond, single crystal diamond and nanostructured diamond, providing important technical support for the development of new high-power electronic devices, quantum sensors and quantum computing platforms.

[0124] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0125] (1) Precise control and optimization: The present invention uses machine learning technology to predict and adjust the chip cutting process, which can achieve precise control and optimization of the process such as laser parameters, processing conditions and cutting paths. Compared with traditional mechanical cutting and chemical etching methods, it can more accurately control the cutting process and improve the consistency and accuracy of cutting.

[0126] (2) Improved surface quality: The application of laser hidden cutting technology and laser polishing technology can significantly improve the surface quality of the chip after cutting. Laser hidden cutting technology reduces the size of the heat-affected zone, reduces the surface roughness and the possibility of residue generation. Laser polishing technology further improves the smoothness and quality of the surface through light excitation and chemical reaction. The precisely controllable hidden cutting technology not only maintains the intrinsic electrical, thermal and mechanical properties of carbon-based materials, but also provides micro-nano processing methods for the design of new carbon-based devices, opening up new ways for the performance optimization and function expansion of carbon-based chips. Compared with traditional methods, a flatter and smoother chip surface can be obtained.

[0127] (3) High efficiency and reliability: The present invention uses laser technology for cutting, which is characterized by high speed and high efficiency. The laser cutting process is fast and precise, which can improve production efficiency. Highly precise atomic-level polishing technology not only improves the surface quality of carbon-based chips, but also provides a key processing method for regulating their electronic structure, optimizing interface characteristics and designing new quantum devices, opening up new possibilities for the application of carbon-based chips in the fields of nanoelectronics, optoelectronics and quantum computing.

[0128] (4) Flexibility and adjustability: The laser parameters, processing conditions, cutting paths, etc. in the present invention can be flexibly adjusted according to different chip materials and cutting requirements. With the support of machine learning technology, adaptive control and optimization can be performed based on real-time data and feedback. This makes the present invention applicable to a variety of chip materials and different cutting requirements, with greater adaptability and flexibility.

[0129] (5) Real-time monitoring: Highly sensitive and real-time monitoring methods not only ensure the integrity of the structure and performance of carbon-based materials during processing, but also provide precise control means for optimizing processing strategies and achieving specific functional structures (such as controlling edge states and inducing specific defects), further promoting the application and development of carbon-based chips in cutting-edge fields such as quantum devices, high-performance sensors and new electronic devices.

[0130] (6) Programmability: The present invention uses machine learning technology to automate and programmably control the laser cutting process. Through training models and real-time feedback, it can be optimized and adjusted according to specific cutting requirements and material properties. Not only does it take into account the anisotropy and thermal conductivity of the material, but it can also adapt to the phase change behavior of carbon-based materials at high temperatures, thereby achieving optimal processing accuracy and efficiency while maintaining the unique properties of carbon-based materials to the greatest extent.

[0131] (7) Cost savings: Compared with traditional cutting methods, the present invention can bring cost savings in some aspects. First, laser cutting technology can achieve high-speed and efficient cutting, improve production efficiency, and reduce production time and labor costs. Secondly, since laser cutting is non-contact, it reduces damage and waste, reduces yield and material waste, and helps reduce production costs.

[0132] (8) Sustainability: Compared with some traditional cutting methods, the laser cutting process of the present invention does not require the use of chemicals, reducing environmental pollution and the need for waste disposal. In addition, since laser cutting can be precisely controlled and optimized, it can reduce material waste and energy consumption, improve resource utilization, and have a positive impact on sustainable development.

[0133] Method Embodiment

[0134] According to an embodiment of the present invention, a method for laser processing a carbon-based chip is provided, and a system for laser processing a carbon-based chip is provided. Figure 2 Flow chart of the method for laser processing carbon-based chips according to an embodiment of the present invention. Figure 2 As shown, the method for laser processing a carbon-based chip according to an embodiment of the present invention specifically includes:

[0135] Step S201, dynamically adjusting laser parameters according to thermal and optical properties of the carbon-based chip through an intelligent control device based on a processing strategy, and sending the laser parameters to the multi-wavelength laser emission module; specifically comprising:

[0136] Adjust the power density, pulse width and repetition frequency of the laser in real time according to the thermal conductivity, thermal expansion coefficient and light absorption characteristics of the carbon-based chip, as well as the changes in material thickness, density or composition;

[0137] Step S202, emitting laser beams suitable for carbon-based chips with different crystal structures according to laser parameters through a multi-wavelength laser emission module; specifically comprising:

[0138] According to the different allotropes of carbon-based chips, the quantum confinement effect of carbon-based materials, the band structure and the optical response characteristics, the optimal laser wavelength is selected, and the specific layers of the multi-layer carbon-based chip structure are selectively processed;

[0139] Among them, for graphene, it can take advantage of its high absorption rate in the visible to infrared spectrum range and select the appropriate wavelength for precise processing; for carbon nanotubes, considering its chirality and diameter-dependent optical absorption characteristics, select the wavelength that matches the specific chirality; for diamond film, take advantage of its wide band gap characteristics and select the ultraviolet band for efficient processing.

[0140] Step S203, using a cutting mechanism to perform hidden chip cutting using laser induced stress wave technology; specifically comprising:

[0141] Laser induced stress wave technology is used to adjust the direction and intensity of the laser induced stress wave according to the structural anisotropy of the carbon-based material; and the high strength and toughness of the carbon-based material are used to achieve clear fracture without generating edge microcracks; at the same time, the atomic configuration of the cutting edge is precisely controlled, the edge electronic state is optimized, and selective and precise segmentation of the multi-layer carbon-based structure is achieved.

[0142] By precisely controlling the laser pulse energy, repetition frequency, wavelength and spatial distribution, a directional local stress field is generated inside the carbon-based chip to achieve high-precision implicit cutting. For graphene, the anisotropy of its two-dimensional structure is utilized to induce stress wave propagation along a specific lattice direction to achieve atomic-level precision cutting. For carbon nanotubes, considering the mechanical properties of its tubular structure, precise segmentation is achieved by regulating the stress wave intensity without destroying the tube wall structure. For diamond film, its high Young's modulus characteristics are utilized to achieve precise cutting through the cumulative effect of high-frequency and low-energy pulses. This method minimizes the heat-affected zone, avoids sp2-sp3 hybridization transformation and defect formation, and uses the high thermal conductivity of carbon materials to quickly dissipate heat and prevent local thermal damage. By adjusting the laser parameters and scanning path in real time, the atomic configuration of the cutting edge can be controlled, the edge electronic state can be optimized, and even functionalized edge structure can be achieved.

[0143] Step S204, using a micro-nano laser polishing device to perform atomic-level polishing on the cut surface using femtosecond laser technology; specifically includes:

[0144] Femtosecond laser technology is used to achieve atomic-level surface flatness without destroying the sp2 or sp3 hybrid structure of the carbon-based chip. By precisely controlling the laser pulse energy and duration, the hexagonal lattice structure of graphene or the tubular structure of carbon nanotubes is avoided from being damaged. The ultrashort pulse characteristics of femtosecond laser are used to selectively act on surface defects or impurities without affecting the strong bonding between carbon atoms. Considering the high thermal conductivity and low heat capacity characteristics of carbon-based materials, special scanning strategies and thermal management technologies are used to prevent structural deformation caused by local overheating. While maintaining the integrity of the basic structure, the functional modification of specific areas of the surface is achieved through laser-induced local chemical reactions. According to the needs of different types of carbon-based chips, surface features from nanometer to micrometer scale are processed simultaneously. Through special laser scanning modes, the residual stress introduced during the processing is minimized and the intrinsic electrical and mechanical properties of carbon-based materials are maintained. The ultra-high peak power and ultra-short pulse width of femtosecond laser are used to achieve precise manipulation of single or a few atoms and achieve atomic-level surface flatness.

[0145] By using low-energy, high-repetition-rate femtosecond laser pulses, precise manipulation of carbon atoms can be achieved. For graphene, by utilizing the characteristics of its two-dimensional structure and adjusting the direction of laser polarization, atoms can be selectively removed along a specific lattice direction to maintain the integrity of the hexagonal lattice. For carbon nanotubes, considering their cylindrical structure, a spiral scanning mode is used to evenly remove surface defects without destroying the tube wall structure. For diamond films, by utilizing their wide bandgap characteristics, appropriate photon energy is selected to achieve precise surface atomic desorption. This method ensures that heat diffusion is limited to the nanoscale by precisely controlling the pulse duration at the femtosecond level, effectively avoiding the heat accumulation effect and maintaining the sp2 or sp3 hybrid structure of the carbon material to the greatest extent. At the same time, by adjusting the laser parameters and scanning strategies in real time, precise layer-by-layer removal of surface atoms can be achieved, not only achieving sub-nanometer flatness, but also being able to selectively retain or remove specific surface functional groups to achieve surface chemical modification.

[0146] Step S205, monitoring the structural integrity of the carbon-based chip during processing through a multimodal sensing and analysis system, and sending the monitoring data to the adaptive machine learning module; specifically comprising:

[0147] Real-time monitoring of structural changes and defect formation of carbon-based chips through Raman spectrometer; monitoring of changes in the degree of graphitization of carbon-based materials; drawing stress distribution maps around the cutting area; distinguishing and quantifying different types of defects; real-time monitoring of changes in the number of layers of multi-layer carbon-based materials; identifying impurities or contaminants that may be introduced; combined with micro-electrical measurement devices, real-time evaluation of the impact of the cutting process on the electrical properties of the material; monitoring of local temperature changes during processing;

[0148] Through high time resolution Raman spectrum acquisition, real-time analysis of characteristic peak changes, including but not limited to G peak (~1580cm -1 )、D peak (~1350cm -1 ) and 2D peak (~2700cm -1 For graphene, the intensity ratio of the G peak and the 2D peak and the shape of the 2D peak are monitored to evaluate the change in the number of layers and the stress state in real time. For carbon nanotubes, the displacement of the radial breathing mode (RBM) peak is analyzed to determine the change in tube diameter and chirality. For diamond films, the sp3 carbon characteristic peak (~1332cm -1 ) to evaluate the purity and stress state; by analyzing the intensity ratio of the G peak and the D peak, the change in defect density can be quantified in real time, where an increase in the D peak intensity indicates an increase in defects or disorder; at the same time, by monitoring the position shift of the G peak, the stress state introduced by processing can be evaluated; based on these real-time data, the system can timely adjust laser parameters such as power, pulse width, scanning speed, etc. to minimize structural damage; in addition, by analyzing the shape and intensity of the G' peak (also called the 2D peak), the change in interlayer coupling strength can be evaluated, providing guidance for the precise processing of multilayer carbon-based structures.

[0149] Step S206, optimizing the processing strategy according to the monitoring data and the processing characteristics of the carbon-based chip through the adaptive machine learning module, and sending the optimization result to the intelligent control device. Specifically including:

[0150] A reinforcement learning algorithm is used to predict the behavior of different carbon-based materials under various processing conditions, and to perform real-time quality control, identify and repair potential defects, and detect and handle abnormal situations. By simulating the response of carbon-based chips under different laser parameters, the processing strategy is optimized to improve cutting accuracy and efficiency.

[0151] The adaptive machine learning algorithm can form a unique response based on the unique crystal structure and physical and chemical properties of different carbon-based chip materials, accurately control the interaction between laser and material, and automatically optimize laser power, pulse width, repetition frequency, scanning speed and cutting path.

[0152] The embodiment of the present invention is a method embodiment corresponding to the above-mentioned system embodiment. The specific operation of each step can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for laser processing carbon-based chips, characterized in that: include: A multi-wavelength laser emission module is used to emit laser beams suitable for carbon-based chips with different crystal structures according to laser parameters; An intelligent control device, connected to the multi-wavelength laser emission module, for dynamically adjusting laser parameters according to thermal and optical properties of the carbon-based chip based on a processing strategy, and sending the laser parameters to the multi-wavelength laser emission module; An adaptive machine learning module, connected to the intelligent control device, for optimizing the processing strategy according to the monitoring data and the processing characteristics of the carbon-based chip, and sending the optimization result to the intelligent control device; Micro-nano laser polishing device, used to process the surface of carbon-based chips; A cutting mechanism for performing invisible cutting of carbon-based chips; A multimodal sensing and analysis system is connected to the adaptive machine learning module, and is used to monitor the structural integrity of the carbon-based chip during processing and send the monitoring data to the adaptive machine learning module.

2. The system according to claim 1, characterized in that The multi-wavelength laser emission module is specifically used for: According to the different allotropes of carbon-based chips, the quantum confinement effect, band structure and optical response characteristics of carbon-based materials, the optimal laser wavelength is selected, and specific layers of the multi-layer carbon-based chip structure are selectively processed.

3. The system according to claim 1, characterized in that The intelligent control device is specifically used for: The power density, pulse width and repetition frequency of the laser are adjusted in real time according to changes in the thermal conductivity, thermal expansion coefficient and light absorption characteristics of the carbon-based chip, material thickness, density or composition.

4. The system according to claim 1, characterized in that The adaptive machine learning module is specifically used for: A reinforcement learning algorithm is used to predict the behavior of different carbon-based materials under various processing conditions, and to perform real-time quality control, identify and repair potential defects, and detect and handle abnormal situations. By simulating the response of carbon-based chips under different laser parameters, the processing strategy is optimized to improve cutting accuracy and efficiency.

5. The system according to claim 1, characterized in that The micro-nano laser polishing device is specifically used for: Femtosecond laser technology is used to achieve atomic-level surface flatness without destroying the sp2 or sp3 hybrid structure of the carbon-based chip. By precisely controlling the laser pulse energy and duration, the hexagonal lattice structure of graphene or the tubular structure of carbon nanotubes is avoided from being damaged. The ultra-short pulse characteristics of femtosecond laser are used to selectively act on surface defects or impurities without affecting the strong bonding between carbon atoms. Considering the high thermal conductivity and low heat capacity characteristics of carbon-based materials, special scanning strategies and thermal management technologies are used to prevent structural deformation caused by local overheating. While maintaining the integrity of the basic structure, functional modification of specific areas of the surface is achieved through laser-induced local chemical reactions. According to the needs of different types of carbon-based chips, surface features from nanometer to micrometer scales are processed simultaneously. Through special laser scanning modes, the residual stress introduced during the processing is minimized and the intrinsic electrical and mechanical properties of carbon-based materials are maintained. The ultra-high peak power and ultra-short pulse width of femtosecond laser are used to achieve precise manipulation of single or a few atoms and achieve atomic-level surface flatness.

6. The system according to claim 1, characterized in that The cutting mechanism is specifically used for: Laser induced stress wave technology is used to adjust the direction and intensity of the laser induced stress wave according to the structural anisotropy of the carbon-based material; and the high strength and toughness of the carbon-based material are used to achieve clear fracture without generating edge microcracks; at the same time, the atomic configuration of the cutting edge is precisely controlled, the edge electronic state is optimized, and selective and precise segmentation of the multi-layer carbon-based structure is achieved.

7. The system according to claim 1, characterized in that The multimodal sensing and analysis system is specifically: a Raman spectrometer; The Raman spectrometer is specifically used for: real-time monitoring of structural changes and defect formation of carbon-based chips; monitoring changes in the degree of graphitization of carbon-based materials; drawing stress distribution maps around cutting areas; distinguishing and quantifying different types of defects; real-time monitoring of changes in the number of layers of multi-layer carbon-based materials; identifying impurities or contaminants that may be introduced; combined with a micro-electrical measurement device, real-time evaluation of the impact of the cutting process on the electrical properties of the material; and monitoring local temperature changes during processing.

8. A method for laser processing a carbon-based chip, used in the system for laser processing a carbon-based chip according to any one of claims 1 to 7, the method specifically comprising: Dynamically adjusting laser parameters according to thermal and optical properties of the carbon-based chip based on a processing strategy through an intelligent control device, and sending the laser parameters to the multi-wavelength laser emission module; The laser beam suitable for carbon-based chips with different crystal structures is emitted according to laser parameters through a multi-wavelength laser emission module; The laser induced stress wave technology is used to perform hidden chip cutting through the cutting mechanism; The cut surface is polished at the atomic level using a micro-nano laser polishing device using femtosecond laser technology; Monitoring the structural integrity of the carbon-based chip during processing through a multimodal sensing and analysis system, and sending the monitoring data to the adaptive machine learning module; Through the adaptive machine learning module, the processing strategy is optimized according to the monitoring data and the processing characteristics of the carbon-based chip, and the optimization results are sent to the intelligent control device.

9. The method according to claim 8, characterized in that Based on the processing strategy, the intelligent control device dynamically adjusts the laser parameters according to the thermal and optical properties of the carbon-based chip, including: Adjust the power density, pulse width and repetition frequency of the laser in real time according to the thermal conductivity, thermal expansion coefficient and light absorption characteristics of the carbon-based chip, as well as the changes in material thickness, density or composition; The laser beam suitable for carbon-based chips with different crystal structures is emitted according to the laser parameters through the multi-wavelength laser emission module, specifically including: According to the different allotropes of carbon-based chips, the quantum confinement effect of carbon-based materials, the band structure and the optical response characteristics, the optimal laser wavelength is selected, and the specific layers of the multi-layer carbon-based chip structure are selectively processed; The specific process of chip hidden cutting using laser induced stress wave technology through cutting mechanism includes: Laser induced stress wave technology is used to adjust the direction and intensity of the laser induced stress wave according to the structural anisotropy of the carbon-based material; and the high strength and toughness of the carbon-based material are used to achieve clear fracture without generating edge microcracks; at the same time, the atomic configuration of the cutting edge is precisely controlled, the edge electronic state is optimized, and selective and precise segmentation of the multi-layer carbon-based structure is achieved.

10. The device according to claim 8, characterized in that The atomic-level polishing of the cut surface using femtosecond laser technology through a micro-nano laser polishing device specifically includes: Femtosecond laser technology is used to achieve atomic-level surface flatness without destroying the sp2 or sp3 hybrid structure of the carbon-based chip. By precisely controlling the laser pulse energy and duration, the hexagonal lattice structure of graphene or the tubular structure of carbon nanotubes is avoided from being damaged. The ultrashort pulse characteristics of femtosecond laser are used to selectively act on surface defects or impurities without affecting the strong bonding between carbon atoms. Considering the high thermal conductivity and low heat capacity characteristics of carbon-based materials, special scanning strategies and thermal management technologies are used to prevent structural deformation caused by local overheating. While maintaining the integrity of the basic structure, the functional modification of specific areas of the surface is achieved through laser-induced local chemical reactions. According to the needs of different types of carbon-based chips, surface features from nanometer to micrometer scale are processed simultaneously. Through special laser scanning modes, the residual stress introduced during the processing is minimized and the intrinsic electrical and mechanical properties of carbon-based materials are maintained. The ultra-high peak power and ultra-short pulse width of femtosecond laser are used to achieve precise manipulation of single or a few atoms and achieve atomic-level surface flatness. Monitoring the structural integrity of carbon-based chips during processing through a multimodal sensing and analysis system includes: Real-time monitoring of structural changes and defect formation of carbon-based chips through Raman spectrometer; monitoring of changes in the degree of graphitization of carbon-based materials; drawing stress distribution maps around the cutting area; distinguishing and quantifying different types of defects; real-time monitoring of changes in the number of layers of multi-layer carbon-based materials; identifying impurities or contaminants that may be introduced; combined with micro-electrical measurement devices, real-time evaluation of the impact of the cutting process on the electrical properties of the material; monitoring of local temperature changes during processing; Through the adaptive machine learning module, according to the monitoring data, the processing strategy is optimized according to the processing characteristics of carbon-based chips, including: A reinforcement learning algorithm is used to predict the behavior of different carbon-based materials under various processing conditions, and to perform real-time quality control, identify and repair potential defects, and detect and handle abnormal situations. By simulating the response of carbon-based chips under different laser parameters, the processing strategy is optimized to improve cutting accuracy and efficiency.

Images