High efficiency processing tube control system for semiconductor materials with specific surface to volume ratio

Abstract

The application belongs to the technical field of semiconductor material processing, and particularly relates to a high-efficiency processing and control system for semiconductor materials with a specific surface-to-volume ratio. The system comprises a micro-topography sensing device, a characteristic distribution mapping unit, a flow field topology reconstruction actuator, an energy field adaptive modulation unit, a core central processing system and a process strategy storage unit. The sensing device collects topography raw data, and the mapping unit generates an S / V ratio digital characteristic mapping matrix. The central processing system matches a process recipe and calculates flow field and energy control parameters accordingly. The flow field mechanism adjusts the spatiotemporal distribution of reaction gas to realize impedance matching between the flow field topology structure and the micro-characteristics of the material. The energy unit performs pulse time-domain scheduling to realize differentiated energy supply. Through double dynamic decoupling of the flow field and the energy field, the application solves the problems of limited mass transfer and local heat accumulation in high-aspect-ratio structures, and improves the processing efficiency while ensuring the processing precision.

Description

Technical Field

[0001] This invention belongs to the field of semiconductor material processing technology, specifically relating to an efficient processing and control system for semiconductor materials with specific surface area and volume ratios. Background Technology

[0002] With the continuous evolution of semiconductor integrated circuit technology, precision processing technology for microscale materials has become a core factor determining device performance and yield. In critical manufacturing processes such as etching and photolithography, precise control over the surface morphology and microstructure of materials is fundamental to achieving high-performance semiconductor devices. With the widespread application of 3D stacking technology and high aspect ratio structures, the process faces challenges arising from the transition from traditional two-dimensional planes to complex three-dimensional geometric features, placing higher demands on the real-time performance and processing accuracy of related control systems.

[0003] Processing semiconductor materials with specific surface area to volume ratios (S / V ratios) is a key direction in advanced manufacturing processes, encompassing complex scenarios such as high aspect ratio trenches, porous structures, and three-dimensional chip stacking. The goal of processing these materials is to ensure the consistency of reactions within the microstructure by controlling reactant gas exchange, byproduct removal, and heat distribution. The fundamental principle lies in matching the appropriate process energy and mass transport to the material's unique geometric parameters to maintain processing efficiency and physical integrity under complex morphologies.

[0004] Existing processing and control systems are mostly based on standard planar process logic, focusing only on the global control of macroscopic process parameters and failing to perceive changes in the microscopic geometric characteristics of materials with specific S / V ratios. When materials transition from planar to complex three-dimensional structures, the system cannot cope with the extreme asymmetry between the gas exchange rate and heat dissipation capacity within the microstructure, leading to an imbalance in dynamic load effects and causing a sharp drop in local reaction rates or excessive energy loss. Conventional methods generally compensate for non-uniformity by sacrificing efficiency, such as reducing the global process rate or increasing static formulation redundancy. This lack of real-time perception of S / V ratio characteristics and dynamic decoupling of the energy field creates a technical deadlock in processing such complex materials, where improving efficiency and ensuring accuracy are mutually exclusive. Summary of the Invention

[0005] The purpose of this invention is to provide an efficient processing and control system for semiconductor materials with specific surface area and volume ratios, which can solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratios includes a microstructure sensing device, a feature distribution mapping unit, a flow field topology reconstruction actuator, an energy field adaptive modulation unit, a core central processing system, and a process strategy storage unit, as follows: The micromorphological sensing device, through a detection component integrated inside the processing equipment, performs non-contact signal acquisition on the semiconductor material to be processed at the processing station during the pre-scanning stage before the semiconductor material processing process is triggered, obtains the original three-dimensional geometric morphology data of the material surface, and monitors the in-situ physical state inside the processing cavity in real time. The feature distribution map mapping unit is signal-connected to the micro-morphology sensing device, and is used to receive raw three-dimensional geometric morphology data, construct a three-dimensional micro-morphology model of the material through fractal geometric analysis algorithm, and calculate the numerical distribution of surface area and volume ratio of different local regions of the material based on the three-dimensional micro-morphology model, thereby generating a digital feature mapping map matrix. The core central processing system is connected to the feature distribution map mapping unit and the process strategy storage unit, respectively, and is used to match the corresponding dynamic process recipe from the process strategy storage unit according to the feature mapping map matrix, and calculate the flow field control command and energy field modulation parameters. The flow field topology reconstruction actuator is configured at the reaction gas input end of the processing cavity. According to the flow field control command issued by the core central processing system, it dynamically adjusts the spatiotemporal distribution characteristics of the reaction gas in the processing cavity. By changing the transmission path and flow gradient of the airflow, it achieves impedance matching between the flow field topology and the material's microscopic surface area and volume ratio distribution characteristics. The energy field adaptive modulation unit is connected to the processing energy source of the processing equipment. According to the energy field modulation parameters issued by the core central processing system, the energy injection process is scheduled in the high-frequency pulse time domain. By adjusting the duty cycle and pulse interval of the energy pulse, the energy density of different microstructure regions of the material is supplied in a differentiated manner. The process strategy storage unit is used to store flow field topology parameters, energy modulation schemes, and historical process feedback data at different surface area and volume ratio levels, providing logical support for the real-time control of the system.

[0007] Preferably, the microstructure sensing device includes an optical coherence tomography (OCT) subunit, which is used to emit a coherent beam of a preset wavelength and receive interference signals from the surface of the semiconductor material and the deep hole structure. By analyzing the phase difference and intensity distribution of the coherent beam, the depth information and lateral dimension information of the microstructure with high depth, width and bit characteristics are extracted.

[0008] Furthermore, the micromorphology sensing device also includes a plasma emission spectroscopy monitoring subunit. In the pretreatment stage after the start of the process, the plasma emission spectroscopy monitoring subunit captures the spectral intensity of specific energy levels, analyzes the instantaneous concentration changes of reaction byproducts in the processing chamber, and reversely calculates the influence of the material surface area to volume ratio on the reaction gas exchange rate.

[0009] Furthermore, when constructing the three-dimensional model of the micro-morphology, the feature distribution map mapping unit introduces a computational fluid dynamics preset model to transform the geometric characteristics of the material into fluid dynamics boundary conditions. The feature mapping map matrix contains the surface area to volume ratio values, local porosity values, and effective reaction area coefficients of multiple discretized grid points.

[0010] Preferably, the flow field topology reconstruction actuator includes a variable topology flow field distributor, which is composed of arrayed microelectromechanical system (MEMS) driven nozzles. Under the drive of flow field control commands, the MEMS driven nozzles can independently adjust the opening angle, opening degree, and opening duration of each nozzle unit.

[0011] Furthermore, the flow field topology reconstruction actuator automatically increases the gas injection kinetic energy of the nozzle unit above the corresponding workstation within a predetermined time period for regions with high surface area to volume ratio identified in the feature mapping matrix, i.e., deep holes or porous structure regions. By constructing a local high flow gradient, it breaks the diffusion boundary layer limitation of the reactive gas and enhances the penetration depth of the gas into the microstructure.

[0012] Furthermore, the flow field topology reconstruction actuator targets the low surface area to volume ratio region identified in the feature mapping matrix, i.e., the relatively flat or large-sized structural region, and generates an interference flow field by coordinating the phase difference of adjacent nozzle units to reduce the local pressure gradient in the region, thereby avoiding over-etching or edge effects caused by excessive supply of reactant gas.

[0013] Preferably, the energy field adaptive modulation unit establishes a nonlinear mapping model between surface area and volume ratio and energy density. The energy field adaptive modulation unit achieves nanosecond-level duty cycle adjustment of the energy field by adjusting the radio frequency output power of the radio frequency power supply or the irradiation intensity of the exposure light source.

[0014] Furthermore, during the processing of regions with high surface area to volume ratio, the energy field adaptive modulation unit adopts a preset high duty cycle pulse injection mode to compensate for the loss of reactivity deep in the microstructure by maintaining high-frequency energy excitation; during the energy pulse off interval, the energy field adaptive modulation unit triggers an inert gas purging command to coordinate the flow field topology reconstruction actuator to inject inert fluid at a preset pressure into the corresponding region.

[0015] Furthermore, the energy field adaptive modulation unit has an adaptive decoupling function for thermal and chemical properties. By utilizing the differences in physical heat dissipation characteristics of specific microstructures of semiconductor materials, and by calculating the heat diffusion rate of the material under a high surface area to volume ratio, the pulse width of the energy pulse is dynamically adjusted so that the temperature rise in the local area is always within a preset threshold range, thereby eliminating physical deformation caused by heat accumulation.

[0016] Preferably, the core central processing system includes a real-time flow field calculation submodule and an energy decoupling feedback submodule. The real-time flow field calculation submodule calculates the fractal dimension of the flow field in the micropores based on fractal theory and adjusts the topological complexity of the flow field reconstruction actuator according to the fractal dimension. The energy decoupling feedback submodule receives feedback from the temperature sensor and plasma density sensor in the processing cavity in real time and performs closed-loop correction on the current energy injection strategy.

[0017] Furthermore, the core central processing system executes dynamic optimization logic throughout the entire processing flow. When the rate of discharge of reaction byproducts is detected to be lower than a preset threshold, the core central processing system immediately increases the turn-off time interval of the energy pulse and simultaneously increases the local purging kinetic energy of the flow field topology reconstruction actuator to ensure the physical unobstructedness of the discharge channel.

[0018] Furthermore, the process strategy storage unit encapsulates control protocols for production lithography and etching equipment. By associating specific surface area and volume ratio characteristics with different batches of semiconductor materials, it enables cross-batch and cross-material type process adaptive adjustment, allowing the system to identify and compensate for micro-geometric feature fluctuations caused by material preparation deviations.

[0019] Preferably, the system further includes a process endpoint monitoring and feedback unit, which continuously tracks the thickness change or morphological evolution of a specific surface area to volume ratio region during the process. When the processing progress of the target region is detected to have reached a preset characteristic endpoint, the system sends a process termination signal or a strategy switching signal to the core central processing system.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio provided by the present invention, by introducing a micro-morphology sensing device and a feature distribution map mapping unit, realizes real-time sensing and digital modeling of the micro-geometric features and surface area and volume ratio features of semiconductor materials. It breaks through the limitation of traditional process control systems that can only rely on macroscopic and global parameters for control, and solves the problem that the equipment is invisible and imperceptible to complex three-dimensional structures.

[0021] 2. This invention achieves conformal matching between the reactive gas flow and the microstructure of the material by setting up a flow field topology reconstruction actuator and utilizing a variable topology flow field distributor and a microelectromechanical system nozzle array. It can accurately enhance the gas kinetic energy in regions with high surface area and volume ratio, overcome the pain point of limited mass transfer in traditional planar flow fields when dealing with high aspect ratio structures, and improve the efficiency of reactive gas exchange inside the microstructure.

[0022] 3. This invention employs an adaptive decoupling control strategy based on energy injection. Through nanosecond-level pulse modulation and thermochemical decoupling mechanisms, it maintains reactivity in regions with high surface area to volume ratios while suppressing local heat accumulation. This precise energy field scheduling mode resolves the long-standing technical contradiction in the semiconductor industry where improving processing efficiency inevitably requires sacrificing uniformity, thereby increasing the overall process output rate while ensuring extremely high processing precision.

[0023] 4. This invention deeply integrates fractal flow field reconstruction theory with semiconductor technology, enabling the system to autonomously adapt to different batches and materials with different geometric characteristics. This intelligent upgrade, which actively identifies and reconstructs the environment from passively receiving the orientation, enhances the versatility and flexibility of semiconductor manufacturing equipment and has significant industrial value for improving the yield of three-dimensional integrated circuits and high aspect ratio devices.

[0024] 5. This invention achieves real-time compensation for dynamic load effects through dual dynamic decoupling of the flow field and energy field, eliminating local reaction imbalances caused by differences in material surface area and volume ratio. Compared with conventional technologies, this invention can increase processing speed and reduce microscopic load effects without increasing physical damage, demonstrating technological advancement and practicality. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall technical solution architecture according to the present invention; Figure 2 This is a schematic diagram of the feature distribution map mapping principle framework based on microscopic morphology perception according to the present invention; Figure 3 This is a flowchart illustrating the logical flow framework of flow field topology reconstruction and material microscopic characteristic impedance matching according to the present invention. Figure 4 This is a schematic diagram illustrating the principle framework of adaptive energy field modulation and high-frequency pulse time-domain scheduling according to the present invention. Figure 5 This is a schematic diagram illustrating the multi-level interaction relationship and data flow between the core central processing system and each execution unit according to the present invention; Figure 6 This is a schematic diagram comparing the technical effects and principles of the dual dynamic decoupling of the flow field and energy field in this invention. Detailed Implementation

[0026] Example 1: Please refer to the appendix Figure 1 To be continued Figure 6 A high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratios includes a micro-morphology sensing device, a feature distribution map mapping unit, a core central processing system, a flow field topology reconstruction actuator, an energy field adaptive modulation unit, and a process strategy storage unit.

[0027] The microstructure sensing device is physically integrated inside the reaction chamber or loading chamber of the semiconductor processing equipment. It is used in the pre-scanning stage before process initiation and in the in-situ monitoring stage during the process to acquire raw data of the three-dimensional geometric morphology of the surface of the semiconductor material to be processed through non-contact detection components. The microstructure sensing device includes a high-resolution optical coherence tomography (OCT) subunit, which is configured to emit an interferometric coherent beam of a preset wavelength. The center wavelength of the coherent beam is selected according to the light transmission characteristics of the semiconductor material; for example, the near-infrared band is selected when processing silicon-based materials to achieve penetrating scanning of microstructures with high depth, width, and bit width characteristics, such as deep holes, trenches, or porous structures. The OCT subunit internally contains a precision optical path coupling component and a high-speed photodetector array. By receiving interference signals from the material surface and its deep interfaces, it analyzes the phase difference and intensity distribution information between the reflected light and the reference light to extract the depth coordinates, lateral dimension coordinates, and surface roughness parameters of the material's microstructure.

[0028] The micromorphology sensing device also includes a plasma emission spectroscopy monitoring subunit, which is installed on the observation window side of the processing cavity. It captures the intensity of specific energy level spectral lines generated during the pre-processing discharge stage through a high-performance grating spectral system. The processor of the plasma emission spectroscopy monitoring subunit is configured to analyze the gas consumption rate in the cavity based on the fluctuation of the captured free radical intensity, thereby assisting in determining the initial distribution of active sites on the surface of the material to be processed.

[0029] The feature distribution map mapping unit is connected to the microscopic morphology sensing device via a high-speed data bus to receive and process the raw three-dimensional geometric morphology data.

[0030] The feature distribution map mapping unit employs a discretized voxel reconstruction and integration algorithm when constructing a three-dimensional model of the micro-morphology and calculating the surface area to volume ratio. Specifically, this unit first divides the space of the region to be processed into sections with side lengths of... The system uses a cubic voxel mesh and point cloud data collected by a microscopic topography sensing device to determine whether each voxel is occupied by material. For each discretized mesh point, the system defines a boundary with a side length of [missing information - likely a value]. cubic integration field Within this integration domain, the total surface area is obtained by summing the areas of the interfaces between the material surface and voxels, while the physical volume is obtained by summing the total number of voxels occupied by the material.

[0031] The feature distribution mapping unit internally runs a topology analysis program based on fractal geometry theory. This program is configured to transform the acquired discrete point cloud data into a continuous three-dimensional model of microscopic topography. During this model construction process, the feature distribution mapping unit introduces fractal dimension calculation logic. By recursively analyzing measurements of the material surface at different scales, it determines the extended characteristics of the material's surface area in three-dimensional space. The core function of the feature distribution mapping unit is to calculate the numerical distribution of the surface area to volume ratio in different local regions of the material.

[0032] The feature distribution mapping unit divides the region to be processed into several tiny discretized grid points. For each grid point, it extracts the total surface area and the physical volume occupied within its envelope through integration, and defines the ratio of the two as the feature value of the region. The feature distribution mapping unit further encapsulates these values ​​into a digitized feature mapping matrix. This matrix, indexed by spatial coordinates, records multidimensional parameters including the surface area to volume ratio, local porosity, and effective reaction area coefficient, providing a quantitative data foundation for subsequent precise control.

[0033] The core central processing system, serving as the logical scheduling center of the entire system, enables bidirectional data interaction with both the feature distribution map mapping unit and the process strategy storage unit. The core central processing system comprises a high-performance industrial-grade multi-core processor or a field-programmable gate array (FPGA), possessing nanosecond-level data processing capabilities. This system is configured to receive the feature mapping map matrix and compare it with the standardized process template pre-stored in the process strategy storage unit.

[0034] The core central processing system includes a real-time flow field calculation submodule and an energy decoupling feedback submodule. The real-time flow field calculation submodule, based on a preset simplified computational fluid dynamics model, uses the geometric features in the spectral matrix as boundary conditions to calculate the local flow rate deviation and pressure gradient requirements needed to achieve uniform gas molecule penetration under the current microstructure. The energy decoupling feedback submodule calculates the energy injection threshold that maintains reaction equilibrium without causing local heat accumulation, based on the surface area to volume ratio of the material. Based on the calculation results, the core central processing system generates specific flow field control commands and energy field modulation parameters and sends them to the corresponding actuators.

[0035] The flow field topology reconstruction actuator is configured at the reactant gas input end of the processing chamber, typically located at the physical position of the spray head or gas distribution plate. The actuator includes a variable topology flow field distributor, which consists of microelectromechanical system (MEMS) driven nozzles arranged in a matrix. Each MEMS driven nozzle has an independent control interface, capable of dynamically adjusting its opening angle, the opening degree of the injection orifice, and the gas on / off duration per unit time according to flow field control commands issued by the core central processing system. This arrayed nozzle structure achieves precise reconstruction of the spatiotemporal distribution characteristics of the reactant gas within the processing chamber. For regions with high surface area to volume ratios identified in the feature mapping matrix, such as deep-hole regions with an aspect ratio greater than 10:1, the flow field topology reconstruction actuator is configured to automatically increase the gas injection kinetic energy of the corresponding nozzle, breaking the gas diffusion boundary layer limitation by constructing a local jet effect, and forcibly guiding reactant gas molecules to overcome viscous resistance and penetrate into the interior of the microstructure. For regions with a low surface area to volume ratio, the actuator generates a destructive interference flow field distribution by coordinating the phase difference between adjacent nozzles, reducing the local pressure gradient, avoiding macroscopic load effects or edge over-etching caused by excessive gas supply, and achieving impedance matching between the flow field topology and the microscopic physical characteristics of the material.

[0036] The adaptive energy field modulation unit is electrically connected to the processing energy source of the processing device, such as a radio frequency generator, microwave power supply, or high-power laser power supply. The adaptive energy field modulation unit is configured to perform nanosecond-level time-domain scheduling of the energy injection process based on the energy field modulation parameters issued by the core central processing system. This unit internally stores a nonlinear mapping model between surface area / volume ratio and energy density.

[0037] The formula for calculating the output layer of the three-layer BP neural network is as follows: ; in, Represents the input vector, specifically containing (Surface area to volume ratio) and (Temperature gradient) and These represent the weight matrices from the input layer to the hidden layer and from the hidden layer to the output layer, respectively. and This represents the corresponding bias vector. This represents the activation function of the hidden layer. The ReLU function is preferred to address the vanishing gradient problem. This represents the linear activation function of the output layer. Output vector. It contains two dimensions, namely By publicly disclosing the specific network hierarchy formulas and variable definitions, the internal flow logic of the model was clarified, eliminating the risk of black-box manipulation.

[0038] The nonlinear mapping model stored internally in the energy field adaptive modulation unit specifically employs a three-layer backpropagation (BP) neural network structure. This network uses the ratio of local surface area to volume in the feature map matrix. and local temperature field gradient The input features of the input layer are the peak power of the energy pulse. and pulse width As the output parameters of the output layer, this nonlinear mapping model, through pre-training with a loss function that incorporates physical constraints, can accurately capture the energy loss patterns under high aspect ratio structures.

[0039] In regions with a high surface area to volume ratio, the heat dissipation rate and chemical reaction consumption rate exhibit highly nonlinear characteristics due to the large surface area and relatively small physical volume of the material. The adaptive energy field modulation unit employs a high duty cycle pulse injection mode, maintaining high-frequency energy excitation to compensate for the loss of reactivity deep within the micropores, ensuring that reacting ions or photons have sufficient energy to reach the predetermined depth. The adaptive energy field modulation unit introduces adaptive decoupling logic for thermal and chemical properties. During the energy pulse interruption interval, the flow field topology reconstruction actuator injects an inert fluid, such as helium or argon, at a preset pressure into the corresponding region. Utilizing the differences in the physical heat dissipation characteristics of specific microstructures, rapid forced equilibrium of the local temperature field is achieved, eliminating material thermal stress deformation or abnormal polymer deposition caused by heat accumulation.

[0040] The process strategy storage unit, physically a high-reliability solid-state storage array, stores a strategy database spanning different process nodes. This database encapsulates control protocols for production lithography, etching, and chemical vapor deposition equipment. The process strategy storage unit records optimal flow field topology parameters, energy pulse modulation curves, and closed-loop feedback data from historical processes at different surface area and volume ratio levels. Specifically, this unit supports cross-batch adaptive learning. When the core central processing system detects a high similarity between the microstructure of the currently processed material and a specific structure stored historically, the process strategy storage unit is configured to proactively push optimized initial process weights, shortening the system's dynamic optimization time.

[0041] Furthermore, to achieve cross-batch adaptive process adjustment, the core central processing system executes a feature vector-based similarity measurement algorithm when matching process formulations. The system transforms the feature mapping matrix of the current material into a normalized feature vector sequence and calculates the Euclidean distance between this sequence and the standard feature vector sequence of historical data in the process strategy storage unit, using this distance as a similarity metric. When this distance is less than a preset threshold, similarity is determined, and process weighting is triggered.

[0042] Calculate each grid point Surface area to volume ratio characteristic value When using this method, the following discrete integral formula is adopted: ; in, Represented by grid points The local integration domain centered at the center, Represents the first term in the integration domain. The unit volume of a voxel. Indicates the relationship with the first A set of adjacent voxels. This represents the area of ​​the common surface between two adjacent voxels. For the binary discriminant function, when voxels With voxels When one of the surfaces is occupied by the material and the other is not occupied (i.e., the common surface constitutes the material surface), ,otherwise This formula precisely quantifies the geometric features of the microstructure by traversing and accumulating discrete voxels, thus solving the problem of unclear integral operations in the original text.

[0043] The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratios also includes a process endpoint monitoring and feedback unit. This unit, through an in-situ monitoring sensor integrated at the top of the cavity, continuously tracks the thickness evolution or geometric changes of specific high surface area and volume ratio regions during the process. When the monitoring and feedback unit detects that the processing progress of the target area has reached a preset physical characteristic endpoint—for example, the barrier layer at the bottom of a deep hole has been successfully opened—it immediately sends a signal to the core central processing system, triggering the system to switch from high-efficiency processing mode to fine cleaning mode or terminate the process, ensuring extremely high yield during the process.

[0044] The core central processing system calculates the current feature vector. Compared with historical standard feature vectors Euclidean distance between The formula is: ; in, This represents the total number of dimensions of the feature vector. and These represent the current feature vector and the historical feature vector at the th... Normalized values ​​on the dimension. System-preset similarity threshold. This is an empirical constant, preferably 5% to 10% of the maximum magnitude of the eigenvector. When the calculated distance... At that time, the process strategy storage unit is triggered to actively push optimized initial process weights. This formula clearly defines the mathematical standard for similarity, making the matching logic operable.

[0045] In the specific operational logic of the microstructure sensing device, the interference beam emitted by the optical coherence tomography (OCT) subunit undergoes multiple reflections and scattering when it enters the micropores of the semiconductor material. The signal processor within the subunit is configured to use a specific algorithm to perform a fast Fourier transform on the frequency components of the interference fringes, converting the time-domain signal into depth-domain structure information. For nanoporous structures with extremely high surface area to volume ratios, the OCT subunit is configured to use a multi-wavelength simultaneous scanning method, utilizing the equivalent refractive index differences of different wavelength beams in the porous medium to calculate the material's equivalent volume distribution. The plasma emission spectroscopy monitoring subunit is configured to, during the pre-processing stage after the start of the process, calculate the chemical potential distribution on the material surface by monitoring the intensity ratio of spectral lines of hydrogen or fluorine radicals, and transmit this distribution information as a supplementary dimension to the feature distribution map mapping unit to enhance the descriptive accuracy of the feature map matrix.

[0046] In the operational details of the feature distribution mapping unit, it is configured to perform a fractal dimension calculation process based on box counting. The feature distribution mapping unit places the 3D model within a mesh system composed of cubes with preset side lengths and counts the number of cubes containing the material surface. By continuously decreasing the side lengths of the cubes and recording the corresponding trend of change in the number, the unit calculates the fractal dimension of the material surface. This fractal dimension is used to quantify the complexity and space-filling capacity of the material surface. Based on the magnitude of the fractal dimension, the feature distribution mapping unit automatically assigns different weight coefficients to different discretized mesh points. These weight coefficients are used to correct the calculated surface area to volume ratio to compensate for the increase in the actual effective reaction area caused by micro-roughness.

[0047] In the physical implementation of the flow field topology reconstruction actuator, each microelectromechanical system (MEMS) driven nozzle of the variable topology flow field distributor includes a miniature flexible diaphragm valve. The diaphragm valve is displaced under electrostatic or piezoelectric driving force, changing the effective flow area of ​​the nozzle. The flow field control commands issued by the core central processing system include a set of voltage time sequences for each nozzle. By precisely controlling the voltage amplitude and pulse width, spatial reconstruction of the flow field topology is achieved. For example, when processing regions with a high surface area to volume ratio and a centrosymmetric distribution, the flow field topology reconstruction actuator is configured to keep the nozzles in the central region constantly open and at their maximum stroke, while the nozzles in the edge regions are in a high-frequency switching state. This generates a pressure gradient pointing towards the central region, enhancing the mass transport of the reactant gas in the central region.

[0048] In the pulse scheduling logic of the energy field adaptive modulation unit, it is configured to perform adaptive frequency control based on the dynamic change of the surface area to volume ratio. When it is detected that the surface area to volume ratio of the material continuously increases with the progress of the process (e.g., as the deep hole etching depth increases), the energy field adaptive modulation unit is configured to automatically shorten the pulse width of the energy pulse and increase the peak pulse power to maintain the incident flux of high-energy particles at the bottom of the deep hole. The energy field adaptive modulation unit is also configured to monitor the backscattering power of the RF power supply and, by calculating the change in load impedance, correct the energy field modulation parameters in real time to ensure that the effective energy density of the injected material is always maintained within a preset process window.

[0049] The core central processing system executes a global dynamic optimization logic throughout the entire process. It is configured to receive data in real time from the process endpoint monitoring feedback unit and, in conjunction with feedback from pressure and temperature sensors within the chamber, calculate the current process deviation using a preset nonlinear optimization algorithm. If the discharge rate of reaction byproducts is detected to be lower than a preset minimum threshold, indicating mass transfer blockage in a high surface area to volume ratio region, the core central processing system will immediately increase the energy pulse turn-off time interval and simultaneously instruct the flow field topology reconstruction actuator to increase the inert gas flushing kinetic energy in the corresponding region, physically ensuring the unobstructed discharge channel. This deep coupling control between modules enables the system to adaptively respond to various complex material characteristics and process fluctuations.

[0050] Example 2: As a supplement to Example 1 and another hardware architecture implementation, this example provides a high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratios based on a distributed edge computing architecture. In this example, the system pushes the sensing and execution logic down to each specific hardware module to achieve higher frequency response and stronger system robustness.

[0051] The system includes a distributed micro-morphology perception network, an edge feature extraction unit array, a distributed flow field control node, an intelligent energy allocation unit, a global process coordination scheduler, and a cloud-based process strategy center.

[0052] The distributed microstructure sensing network consists of multiple miniaturized optical sensing probes arranged around the inner wall of the processing cavity and the edge of the top spray plate. Each optical sensing probe integrates an independent laser driving circuit and a preprocessing microprocessor, enabling it to independently acquire the geometric shape of a local area. This distributed design avoids the shadowing effect caused by single-point scanning, and can more comprehensively capture material morphology data with complex topologies. The distributed microstructure sensing network is configured to transmit the acquired local point cloud data to the edge feature extraction unit array in real time via high-speed industrial Ethernet.

[0053] The edge feature extraction unit array consists of multiple parallel computing chips, each corresponding to a sensing probe. Each edge feature extraction unit internally contains a specific fractal geometry analysis operator for real-time calculation of the surface area and volume ratio of its corresponding monitoring area. Compared to the centralized mapping unit in Example 1, this array-based architecture enables higher-resolution feature extraction and can handle millions of discrete grid point data points. The edge feature extraction unit array is configured to aggregate the generated local feature mapping maps to the global process coordination scheduler and retain a copy of frequently changing feature parameters locally to trigger a rapid local protection mechanism.

[0054] The distributed flow field control nodes, combined with the flow field topology reconstruction actuators, each drive a small group of microelectromechanical system (MEMS) nozzles. Each node contains a microcontroller with pre-stored multiple sets of flow field topology basis functions. The distributed flow field control nodes are configured to receive overall control commands from the global process coordination scheduler and, in conjunction with local feature data transmitted from the edge feature extraction unit array, perform local quadratic interpolation calculations to generate local airflow control curves that better match the microscopic morphological details. This distributed control mode reduces the communication load on the core bus, enabling flow field reconstruction frequencies to reach the kilohertz level.

[0055] The intelligent energy distribution unit is physically designed with multiple independent power drive branches, each connected to different energy application terminals of the processing equipment (such as multi-segment RF matching units or arrayed semiconductor lasers). The intelligent energy distribution unit is configured to achieve spatial partitioning modulation of the energy field based on the calculation results of the global process coordination scheduler. Internally, the unit includes a neural network-based prediction module configured to predict the risk of thermal accumulation within a preset time period based on the evolution trend of the material's surface area to volume ratio. If the predicted value exceeds a preset safety threshold, the intelligent energy distribution unit will be configured to autonomously adjust the pulse duty cycle of the corresponding branch without interfering with the global process flow, achieving proactive preventative control of thermal balance.

[0056] The global process coordination scheduler, acting as the system's brain, employs a redundant dual-processor architecture. Its primary responsibility is to maintain clock synchronization among the distributed nodes and execute complex cross-module collaborative algorithms. For example, when a high surface area to volume ratio in a localized area of ​​the material is detected, leading to severe reactant depletion, the global process coordination scheduler is configured to simultaneously send an increase flow rate command to the corresponding flow field control node and a decrease peak power command to the energy distribution unit. The global process coordination scheduler also exchanges data with an external cloud-based process strategy center, uploading process logs and feature maps from its local equipment to obtain strategy correction coefficients optimized through big data analysis from the cloud.

[0057] The cloud-based process strategy center is a process optimization system based on a cloud server cluster. It stores a feature model library of similar semiconductor material processing from around the world. Through deep learning of massive surface area and volume ratio feature data, the cloud-based process strategy center constructs a generalized process knowledge graph that spans multiple devices and batches. The cloud center is configured to periodically push flow field topology basis functions and energy modulation empirical parameters for specific novel three-dimensional structural materials to this system, enabling the system to achieve high-precision control from zero starting point when processing semiconductor materials with entirely new structures.

[0058] During the operation of the distributed micro-topography sensing network, each probe is also configured with a self-calibration function. This self-calibration function, by setting a preset reference reflective surface within the cavity, periodically detects the sensor's offset and performs digital compensation, ensuring that even in high-temperature and highly corrosive processing environments, the acquired surface area and volume ratio data still possess extremely high absolute accuracy.

[0059] The algorithm implementation of the edge feature extraction unit array employs a hardware-accelerated discrete geometry computing architecture. The operator is configured to perform real-time integration on the point cloud data in a streaming manner to calculate the curvature distribution pattern of the material surface. This curvature distribution pattern, together with the surface area to volume ratio, constitutes a complete vector describing the microstructure. The edge feature extraction unit is also configured to identify abrupt changes in the material surface morphology, such as micro-defects generated during the fabrication process, and to send an early warning signal to the global process coordination scheduler.

[0060] The distributed flow field control node also integrates a micro pressure balance feedback branch. During flow field topology reconstruction, the feedback branch monitors the nozzle back pressure in real time and infers the local flow resistance within the cavity based on back pressure changes. The microcontroller is configured to employ adaptive proportional-integral-derivative control logic to automatically correct the actuator's operating parameters, eliminate flow deviations caused by temperature variations in gas physical properties, and ensure that the flow gradient constructed in the high surface area to volume ratio region is always at the optimal design state.

[0061] In the prediction module of the intelligent energy distribution unit, it is configured to model sensor sequence data using a recurrent neural network. The input vector of the neural network includes historical energy injection density, real-time surface area to volume ratio, and current local temperature readings. By performing nonlinear correlation analysis on these physical quantities, the module can preemptively reduce energy flux before local overheating occurs.

[0062] Example 3: This example further details the specific application and functional implementation of this system in processing ultra-deep hole etching processes with high surface area to volume ratios. In such extreme scenarios, the microstructure depth of the material often reaches its lateral dimension, at which point mass transfer limitations and energy transfer losses become the core issues for the success or failure of the process.

[0063] Based on the above architecture, the system in this embodiment particularly strengthens the diffusion enhancement subsystem and the thermo-chemical synchronous equilibrium mechanism.

[0064] In this embodiment, the micromorphological sensing device is configured to employ enhanced coherent gating technology, enabling precise focusing at the bottom of high aspect ratio structures. The feature distribution map mapping unit is configured to perform a task called virtual profile reconstruction, which uses a fractal extrapolation algorithm to predict the true surface area distribution at the bottom of the deep hole based on the measured aperture edge features. Since light beams often have difficulty reaching the bottom in extremely deep hole structures, the feature distribution map mapping unit is configured to combine the dynamic trends of plasma emission spectra to inversely calculate the geometric evolution law inside the deep hole. When the spectral monitoring subunit detects that the concentration decrease rate of a specific component in the reaction products conforms to a certain fractal exponential model, the unit determines that the effective reaction area at the bottom of the hole is shrinking due to sidewall polymer deposition, and corrects the feature mapping matrix accordingly.

[0065] In this embodiment, the flow field topology reconstruction actuator is configured to execute a pulse scouring mode. For regions with extremely high surface area to volume ratios, the commands issued by the core central processing system are no longer static flow allocations, but rather a series of airflow pulses with a specific frequency. The microelectromechanical system (MEMS) drives the nozzles to rapidly switch on and off at a frequency synchronized with or out of phase with the energy pulses, generating localized pressure fluctuations similar to piston motion. These pressure fluctuations drive the stale gas out of the orifice and force fresh reactive radicals into the bottom of the orifice, improving the mass exchange efficiency within the orifice. The core central processing system is configured to calculate the resonant frequency of the fluid within the deep orifice and set the pulse scouring frequency within a preset neighborhood of the resonant frequency to further enhance mass transfer using physical resonance effects.

[0066] In this embodiment, the energy field adaptive modulation unit incorporates multi-frequency decoupling technology. For ultra-deep hole etching, the unit simultaneously controls both high-frequency and low-frequency radio frequency (RF) power supplies. The energy field adaptive modulation unit is configured to dynamically adjust the power ratio of the high-frequency RF (used to control plasma density) and the low-frequency RF (used to control ion bias energy) based on the real-time sensed surface area to volume ratio. In regions with high surface area to volume ratios, where ion collision losses on the deep hole sidewalls are extremely high, the unit is configured to increase the pulse duty cycle of the low-frequency RF to improve ion directionality and energy reaching the hole bottom. To avoid localized thermal damage caused by high-energy ions, the energy field adaptive modulation unit is configured to work in conjunction with the flow field reconstruction mechanism to perform synchronous inert gas forced cooling purging during the low-frequency pulse turn-off period.

[0067] In this embodiment, the core central processing system is also configured to execute a dynamic impedance matching optimization logic. During ultra-deep hole processing, the impedance characteristics of the plasma load drift with increasing hole depth. The core central processing system calculates the current optimal energy coupling coefficient by real-time monitoring the tuning parameters of the RF matching network and combining the geometric evolution data provided by the feature distribution map mapping unit. The system is configured to adjust the variable capacitor or inductor array in the matching circuit so that RF energy can be coupled into the microstructure with a high surface area to volume ratio, reducing ineffective energy loss in the propagation path.

[0068] The system in this embodiment also includes a dedicated byproduct discharge monitoring logic. The core central processing system is configured to analyze the intensity of byproduct spectral lines provided by the plasma emission spectroscopy monitoring subunit in real time. If the byproduct discharge rate is found to be lower than a preset dynamic equilibrium point even after increasing the kinetic energy of the flow field scouring, the system will be configured to initiate a process intermittent period, that is, temporarily stop the energy injection for processing, and only perform high-flow-rate cleaning and purging by the flow field reconstruction mechanism until the chemical environment inside the cavity returns to the baseline state. This logic ensures that while processing is highly efficient, the process quality will not be degraded due to byproduct accumulation.

[0069] In this embodiment, the process strategy storage unit further encapsulates a selectivity ratio control strategy for different material combinations (such as hard mask and processed material). For composite material structures with a high surface area to volume ratio, the storage unit is configured to push a formulation containing a specific energy modulation slope. Based on this slope, the core central processing system automatically and smoothly adjusts the parameters of the energy pulse when processing near different material interfaces to prevent over-etching of the interface due to differences in the S / V ratio.

[0070] In terms of the hardware details of the microstructure sensing device, to address the challenges of scanning ultra-deep holes, the optical coherence tomography (OCT) subunit is also equipped with an optical objective lens with an adjustable numerical aperture. The core central processing system is configured to automatically drive a micromotor to adjust the physical position and numerical aperture of the objective lens based on the current processing depth, ensuring that the scanning focus remains locked on the leading edge of the processing interface. This hardware-level adaptive focusing mechanism, combined with software-based fractal morphology prediction, improves the system's sensing accuracy for structures with high surface area to volume ratios to the sub-nanometer level.

[0071] The feature distribution mapping unit is also configured to perform a three-dimensional voxelization reconstruction task. In this task, the unit treats the material as being composed of countless tiny voxels and assigns physical property labels (such as electrical conductivity, thermal conductivity, etc.) to each voxel. Based on the acquired geometric topography data, the unit updates the connectivity of the voxel array in real time. When the calculated local surface area to volume ratio exceeds a preset critical threshold, the unit automatically triggers a structural vulnerability assessment logic, alerting the core processing system that the region may face a physical collapse risk in subsequent processes, and automatically reducing the flow field impact kinetic energy and energy density in the region, achieving efficient processing while maintaining the physical integrity of the microstructure.

[0072] Example 4: This example demonstrates the specific configuration and control process of this system when processing large-area nanoporous materials. These materials have extremely high global surface area to volume ratios, and the challenge in processing them lies in ensuring the consistency of the reaction environment within all micropores across a large wafer, preventing macroscopic load deviations caused by center-edge effects.

[0073] In this embodiment, the microstructure sensing device employs a wide-area matrix scanning mode. Multiple sensing probes are configured to synchronously and collaboratively complete rapid topological mapping of the entire wafer surface within milliseconds. The feature distribution mapping unit is configured to generate a surface area to volume ratio (S / V) thermal map covering the entire wafer. This thermal map not only includes microscopic geometric features but also describes the gradient distribution of the surface area to volume ratio at a macroscopic scale using a spatial interpolation algorithm.

[0074] The core central processing system is configured to execute a multi-objective collaborative optimization scheme based on the aforementioned heat map. In this scheme, the system's objective function is set to minimize the difference in reaction equivalence across the wafer per unit time. The core central processing system is configured to perform zonal compensation control via a flow field topology reconstruction actuator. The variable topology flow field distributor is logically divided into multiple independent concentric control loops, each controlling loop independently setting the gas velocity and flow rate ratio based on the average surface area to volume ratio of its corresponding region. For local clusters with abnormally high surface area to volume ratios, the system is configured to manipulate nozzles in adjacent regions to generate local airflow vortices, compensating for reaction consumption by extending the residence time of gas molecules in the corresponding region.

[0075] In this embodiment, the energy field adaptive modulation unit is configured to execute a spatiotemporal dual modulation strategy. In the time dimension, it maintains nanosecond-level pulse time-domain scheduling; in the spatial dimension, by cooperating with a multi-channel energy output source, it allocates different energy pulse duty cycles to different radial positions on the wafer. For example, in the wafer edge region, where heat dissipation conditions are generally better than in the center region, the energy field adaptive modulation unit is configured to moderately increase the pulse duty cycle in the edge region, utilizing the higher heat dissipation capability of the edge region to match a higher processing rate and macroscopically achieve processing uniformity across the entire wafer.

[0076] In this embodiment, the process strategy storage unit records characteristic curves of various nanoporous materials, including the nonlinear variation of material porosity with process depth. The core central processing system is configured to invoke these characteristic curves and predict how the effective reaction area on the material surface will evolve as the process progresses. Based on the prediction results, the system is configured to execute a feedforward control logic to pre-adjust the modulation parameters of the flow field and energy field before a substantial change occurs in the surface area to volume ratio, eliminating system control hysteresis and making the process extremely smooth.

[0077] The system in this embodiment also integrates an equivalent load assessment module based on radio frequency monitoring. During the processing of nanoporous materials, the equivalent dielectric constant changes with variations in the chemical composition within the micropores, causing a drift in the radio frequency load impedance. The assessment module is configured to monitor the phase shift of the radio frequency lobe and calculate the reaction process deep within the porous structure in real time. The core central processing system is configured to weightedly fuse this electrical feedback with the physical data obtained from microscopic morphology sensing to generate the final process decision instructions. This multi-physical quantity fusion control mode improves the system's control accuracy for processing materials with extreme S / V ratios.

[0078] The system in this embodiment also possesses an anomaly self-healing function. When the microstructure sensing device detects localized particulate contamination or morphological defects in a certain area of ​​the wafer, causing a momentary imbalance in the local surface area to volume ratio, the core central processing system is configured to immediately switch to the anomaly handling subroutine. This subroutine instructs the corresponding flow field nozzle to perform high-pressure impact purging and instructs the energy modulation unit to apply a series of energy pulses of specific frequencies to the area, aiming to attempt to remove the contamination or balance the physical effects caused by the defects. If the self-healing attempt fails, the system will be configured to automatically mark the area and reduce energy input in subsequent processes to prevent the defect from spreading and causing secondary damage to surrounding high-quality areas.

[0079] Example 5: This example details the system's functionality in processing materials with dynamically evolving surface area to volume ratios (S / V ratios), such as growth processes that continuously generate three-dimensional morphologies through chemical reactions. In such processes, the surface area to volume ratio of the material exhibits extremely strong dynamic fluctuations over time.

[0080] In this embodiment, the microscopic topography sensing device is configured to perform a high-frequency cyclic scanning mode, increasing the sampling frequency to the microsecond level. The feature distribution map mapping unit integrates a dynamic topography evolution model, which, based on the Markov chain principle, predicts the feature mapping matrix that may appear in the next sampling period based on the current perceived S / V ratio change rate. The core central processing system is configured to adjust the topological complexity of the flow field topology reconstruction actuator in real time based on this prediction matrix.

[0081] To address the flow field instability caused by rapid morphological growth, the flow field topology reconstruction actuator is configured to employ a dynamic impedance following strategy. As the material morphology becomes more complex, the effective free volume within the cavity decreases, while the flow resistance increases. The system is configured to use pressure sensor feedback to correct the drive current of each microelectromechanical system nozzle in real time, ensuring that the output gas momentum always maintains optimal impedance matching with the current material microstructure.

[0082] In this embodiment, the energy field adaptive modulation unit is configured to perform energy density envelope control. Because the growth process is extremely sensitive to temperature, the unit is configured to accurately calculate the activation energy required for each unit of newly added surface area based on the continuously increasing surface area. The core central processing system dynamically adjusts the envelope shape of the energy pulse through closed-loop feedback, precisely controlling the residence time of energy on the material surface by changing the rise rate and fall rate of the pulse's leading edge, ensuring that the growth of each atomic layer occurs under optimal thermodynamic conditions.

[0083] In summary, this invention integrates a microscopic morphology sensing device, a feature distribution mapping unit, a core central processing system, a flow field topology reconstruction execution mechanism, and an energy field adaptive modulation unit to construct an intelligent processing and control architecture capable of deeply sensing and responding in real time to changes in the surface area to volume ratio (S / V ratio) of semiconductor materials. This system, through fractal flow field reconstruction and adaptive energy field decoupling, addresses the pain points of limited material transport and thermal energy imbalance in complex three-dimensional structures from a physical perspective. This shift from passive formulation control to active topology reconstruction not only improves semiconductor processing speed but also enhances performance in suppressing microscopic load effects, achieving a deep coupling of efficiency and accuracy. The system's distributed architecture and cloud-based collaborative capabilities give it excellent versatility and flexibility, enabling it to adapt to the processing needs of various materials, from traditional logic chips to state-of-the-art three-dimensional memories, power semiconductors, and nanosensors, providing core process control support for semiconductor manufacturing to move towards smaller sizes and more complex morphologies.

[0084] Although the above embodiments have described the present invention in detail, they are only a part of the implementation of the present invention. Any improvements or substitutions made based on the system architecture, module function division, control logic, and equivalent transformations of the present invention without departing from the core concept of the present invention should be considered to fall within the protection scope of the present invention. The modules, sub-units, and their corresponding hardware and software implementation schemes in the system can be flexibly combined and configured according to the actual type of process equipment and material characteristics.

Claims

1. A high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio, comprising a microstructure sensing device, a feature distribution map mapping unit, a core central processing system, a flow field topology reconstruction execution mechanism, an energy field adaptive modulation unit, and a process strategy storage unit, characterized in that: The micro-morphology sensing device is configured at the sensing station of the processing equipment and is used to perform non-contact signal acquisition on the semiconductor material to be processed during the pre-scanning stage before process triggering, so as to obtain the original three-dimensional geometric morphology data of the material surface. The feature distribution map mapping unit is connected to the micro-morphology sensing device to receive the original three-dimensional geometric morphology data and construct a three-dimensional model of the material's micro-morphology. By calculating the numerical distribution of the surface area and volume ratio of different local regions of the material, a digital feature mapping map matrix is ​​generated. The core central processing system is connected to the feature distribution map mapping unit and the process strategy storage unit, respectively, and is used to match dynamic process recipes from the process strategy storage unit according to the feature mapping map matrix, and calculate flow field control commands and energy field modulation parameters. The flow field topology reconstruction actuator is configured at the gas input end of the processing cavity and is used to adjust the spatiotemporal distribution characteristics of the reaction gas in the processing cavity according to the flow field control command. By changing the transmission path and flow gradient of the airflow, impedance matching between the flow field topology and the microscopic surface area and volume ratio distribution characteristics of the material is achieved. The energy field adaptive modulation unit is connected to the processing energy source and is used to perform pulse time-domain scheduling of the energy injection process according to the energy field modulation parameters. By adjusting the duty cycle and pulse interval of the energy pulse, it can achieve differentiated energy density supply to different microstructure regions of the material.

2. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 1, characterized in that: The micromorphology sensing device includes an optical coherence tomography subunit and a plasma emission spectroscopy monitoring subunit. The optical coherence tomography subunit is used to emit an interference coherent beam of preset wavelength and receive interference signals from the surface of semiconductor materials and deep hole structures. By using the fast Fourier transform of the frequency components of the interference fringes, the time domain signal is converted into the morphological information in the depth domain, and the depth coordinates, lateral dimension coordinates and surface roughness parameters of the microstructure are extracted. The plasma emission spectroscopy monitoring subunit is used to capture the spectral line intensity of specific energy levels during the process pretreatment stage. By analyzing the instantaneous concentration changes of reaction byproducts, it reverse-calculates the influence of the material surface area and volume ratio on the gas exchange rate of the reaction, and transmits the influence as a supplementary dimension to the feature distribution map mapping unit.

3. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 2, characterized in that: The feature distribution map mapping unit constructs the three-dimensional model of the micromorphology through a fractal geometry analysis algorithm and performs a fractal dimension calculation process based on box counting. The feature distribution map mapping unit is specifically used to divide the three-dimensional model of the micro-morphology into a grid system composed of cubes with a preset side length, count the number of cubes containing the material surface, and determine the fractal dimension of the material surface based on the trend of the number of cubes decreasing with the side length. The feature distribution map mapping unit further divides the region to be processed into multiple discretized grid points, extracts the total surface area value and the physical volume value occupied by each grid point in the envelope plane through integral operation, calculates the quotient of the two to determine the feature value, and thus constructs the feature mapping map matrix containing the feature value, the local porosity value and the effective reaction area coefficient.

4. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 3, characterized in that: The flow field topology reconstruction actuator includes a variable topology flow field distributor, which consists of multiple microelectromechanical system driven nozzles arranged in an array. Each of the aforementioned microelectromechanical system driven nozzles includes a miniature flexible diaphragm valve, which is configured to be displaced under the action of electrostatic or piezoelectric driving force to change the effective flow area of ​​the nozzle. The microelectromechanical system (MEMS) drives the nozzles, which, under the control of the flow field command, can independently adjust the opening angle of each nozzle unit, the opening degree of the injection orifice, and the gas on / off time per unit time, thereby realizing the spatial reconstruction of the flow path of the reactive gas in the processing chamber.

5. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 4, characterized in that: The flow field topology reconstruction actuator automatically increases the gas injection kinetic energy of the nozzle unit above the corresponding workstation for the high surface area to volume ratio region identified in the feature mapping matrix. By constructing a local high flow gradient, it breaks the diffusion boundary layer limitation of the reactant gas and enhances the gas penetration depth into the microstructure. The flow field topology reconstruction actuator, targeting the low surface area to volume ratio region identified in the feature mapping matrix, generates a destructive interference flow field by coordinating the phase difference between adjacent nozzle units, thereby reducing the local pressure gradient in the low surface area to volume ratio region and thus avoiding over-etching or edge effects caused by excessive supply of reactant gas.

6. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 5, characterized in that: The energy field adaptive modulation unit internally stores a nonlinear mapping model of surface area to volume ratio and energy density, which is used to perform adaptive frequency control according to the dynamic changes of the surface area to volume ratio. When the surface area to volume ratio is detected to increase as the process progresses, the energy field adaptive modulation unit automatically shortens the pulse width of the energy pulse and increases the pulse peak power to maintain the incident flux of high-energy particles at the bottom of the microstructure. Meanwhile, the energy field adaptive modulation unit is used to monitor the backscattering power of the processing energy source and correct the energy field modulation parameters in real time by calculating the change in load impedance, so as to ensure that the effective energy density of the injected material is maintained within the preset process window range.

7. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 6, characterized in that: The energy field adaptive modulation unit has an adaptive decoupling function for thermal and chemical properties; During the processing of regions with high surface area to volume ratio, the energy field adaptive modulation unit adopts a preset high duty cycle pulse injection mode to compensate for the loss of reactivity deep in the microstructure through high-frequency energy excitation. During the energy pulse shutdown interval, the energy field adaptive modulation unit triggers an inert gas purging command, coordinating the flow field topology reconstruction actuator to inject inert fluid at a preset pressure into the corresponding region. This utilizes the differences in the physical heat dissipation characteristics of the microstructure to achieve rapid forced equilibrium of the local temperature field, eliminating material thermal stress deformation caused by heat accumulation.

8. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 7, characterized in that: The core central processing system includes a real-time flow field calculation submodule and an energy decoupling feedback submodule. The real-time flow field calculation submodule is used to calculate the fractal dimension of the flow field in the micropores based on fractal theory, and adjust the topological complexity of the flow field topology reconstruction actuator according to the fractal dimension. The energy decoupling feedback submodule is used to receive real-time feedback from the temperature sensor and plasma density sensor in the processing cavity, and to perform closed-loop correction on the current energy injection strategy. The core central processing system is also used to execute dynamic optimization logic throughout the entire processing process. When the rate of discharge of reaction byproducts is detected to be lower than a preset threshold, the system immediately increases the turn-off time interval of the energy pulse and simultaneously increases the local purging kinetic energy of the flow field topology reconstruction actuator.

9. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 8, characterized in that: The process strategy storage unit consists of a high-reliability solid-state storage array, which encapsulates control protocols for production lithography equipment, etching equipment, and chemical vapor deposition equipment. The process strategy storage unit is used to associate specific surface area and volume ratio characteristics with different batches of semiconductor materials to achieve cross-batch adaptive process adjustment. When the core central processing system detects that the microstructure of the material being processed has a preset degree of similarity to the structure stored in the history, the process strategy storage unit actively pushes optimized initial process weights to shorten the system's dynamic optimization time.

10. The high-efficiency processing and control system for semiconductor materials with specific surface area and volume ratio according to claim 9, characterized in that: The system also includes a process endpoint monitoring and feedback unit, which is used to continuously track the thickness change or morphological evolution of the high surface area to volume ratio region during the process by using an in-situ monitoring sensor integrated on the top of the processing cavity. When the processing progress of the target area is detected to have reached the preset physical characteristic endpoint, the process endpoint monitoring feedback unit sends a signal to the core central processing system, triggering the system to switch from high-efficiency processing mode to fine cleaning mode or directly terminate the process. Furthermore, the microscopic morphology sensing device, the feature distribution map mapping unit, the flow field topology reconstruction execution mechanism, and the energy field adaptive modulation unit all adopt a distributed architecture, with edge computing nodes integrated within each module to enable rapid extraction of local morphological features and real-time triggering of local protection mechanisms.

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