Thermal force detection module based on sem images

Abstract

The application provides a thermal force detection module based on an SEM image, relates to the technical field of micro thermal force detection and analysis, and comprises a main control system, wherein the main control system comprises a NanoSEM capturing subsystem, a thermal force decoding subsystem, a micro flow thermal force adjusting subsystem, a prediction simulation subsystem and a thermal force interaction subsystem. The application realizes real-time monitoring of micro thermal force distribution of materials through the thermal force detection module based on the SEM image, can timely find thermal abnormal conditions in the SEM image, effectively avoids device overheating or material failure caused by delayed detection, and improves the efficiency and timeliness of thermal force detection. The NanoSEM capturing subsystem utilizes photon-electron dual detection technology and a self-assembled nano probe array, combines a spectrum dimension mixing algorithm, quickly extracts micro thermal force data from the SEM image, and overcomes the limitations of traditional SEM imaging analysis which depends on manual experience and has low efficiency.

Description

Technical Field

[0001] This invention relates to the field of microscopic thermal detection and analysis technology, specifically a thermal detection module based on SEM images. Background Technology

[0002] With the rapid development of nanotechnology and materials science, the analysis of the thermal properties of microstructures has become increasingly important in fields such as semiconductor manufacturing, aerospace, and nanodevice development. Scanning electron microscopy (SEM) images, as a crucial tool for observing the microstructure of materials, directly impact device performance, material lifetime, and process optimization through accurate detection of their thermal distribution. However, microscopic thermal anomalies can lead to device overheating, material fatigue, and even failure. Therefore, accurately and in real-time detecting and controlling the thermal distribution based on SEM images has become a significant research direction in related fields.

[0003] Currently, thermal imaging based on SEM images mainly relies on the following techniques:

[0004] Traditional SEM imaging analysis: High-resolution images are acquired using a scanning electron microscope, and combined with manual observation or simple image processing methods to analyze the microstructure and potential thermal anomalies of the material surface.

[0005] Thermal radiation detection technology: It uses infrared sensors or photon detectors to measure thermal radiation signals during SEM imaging to infer the temperature distribution of the target object. It has the characteristics of non-contact and can preliminarily identify thermal features.

[0006] Atomic force microscopy (AFM) thermal probe: By combining AFM with a thermal probe, temperature changes in microscopic regions can be directly measured, providing high spatial resolution thermal data, which is often used for thermal analysis at the nanoscale.

[0007] Numerical simulation method: Based on finite element analysis (FEA) or molecular dynamics (MD) simulation of the thermal distribution of SEM images, temperature changes are predicted by calculating the thermal conductivity of materials and the distribution of heat sources.

[0008] Although the above techniques have achieved some success in thermal detection of SEM images, they still have significant shortcomings:

[0009] Problem 1: Traditional SEM imaging analysis relies on human experience, which is inefficient and cannot achieve real-time thermal monitoring, making it difficult to quickly detect microscopic thermal anomalies and miss the opportunity for intervention.

[0010] Question 2: Although thermal radiation detection technology can provide temperature information, it is limited by detector sensitivity and environmental interference (such as vacuum conditions), resulting in insufficient resolution and accuracy. It is difficult to capture weak thermal signals in SEM images, and the equipment cost is high, making it difficult to apply widely.

[0011] Question 3: Although AFM thermal probe technology has high accuracy, its contact measurement method limits the scanning range and speed, making it impossible to perform rapid panoramic thermal analysis on large-area SEM images. In addition, the probe is prone to wear and is complex to maintain.

[0012] Question 4: Numerical simulation methods rely on idealized model assumptions and lack dynamic thermodynamic response analysis of complex microstructures (such as cracks and pores) in real SEM images. The predicted results deviate significantly from the actual results, and the calculation is time-consuming, making it difficult to meet real-time requirements.

[0013] Therefore, a thermal detection module based on SEM images is needed to solve the above problems. Summary of the Invention

[0014] Technical problems to be solved

[0015] To address the shortcomings of existing technologies, this invention provides a thermal detection module based on SEM images, which solves the problems mentioned in the background section.

[0016] Technical solution

[0017] To achieve the above objectives, the present invention provides the following technical solution: a thermal detection module based on SEM images, comprising a main control system. The main control system includes a NanoSEM capture subsystem, a thermal decoding subsystem, a microfluidic thermal regulation subsystem, a prediction simulation subsystem, and a thermal interaction subsystem. The specific design of each subsystem within the main control system is as follows:

[0018] The NanoSEM capture subsystem is used to acquire SEM images of the target object using a scanning electron microscope, extract raw microscopic thermal data from them, and transmit the raw data to the thermal decoding subsystem.

[0019] The thermal decoding subsystem receives SEM image data from the NanoSEM capture subsystem, uses intelligent algorithms to analyze the thermal characteristics in the SEM images, generates a thermal distribution model, and transmits the analysis results to the microfluidic thermal regulation subsystem, the prediction simulation subsystem, and the thermal interaction subsystem.

[0020] The microfluidic thermal regulation subsystem receives the thermal distribution model from the thermal decoding subsystem, optimizes the thermal state of the target object based on microfluidic control technology, and feeds back the regulation results to the prediction simulation subsystem and the thermal interaction subsystem.

[0021] The prediction simulation subsystem receives the SEM image analysis results from the thermal decoding subsystem and the regulation data from the microfluidic thermal regulation subsystem, uses a multidimensional prediction algorithm to generate a thermal evolution trend based on the SEM image, and transmits the prediction results to the thermal interaction subsystem.

[0022] The thermal interaction subsystem receives the thermal distribution model from the thermal decoding subsystem, the regulation results from the microfluidic thermal regulation subsystem, and the prediction data from the prediction simulation subsystem. It presents the thermal analysis results based on SEM images through a multimodal interactive interface and supports user control and feedback.

[0023] Preferably, the main control system is equipped with a central coordination unit. The main control system triggers the operation of each subsystem in the following order through the central coordination unit: NanoSEM capture → thermal decoding → microfluidic thermal regulation → predictive simulation → thermal interaction. After the thermal decoding is completed, a control signal is fed back to the NanoSEM capture subsystem to adjust the resolution and scanning area of ​​the SEM image acquisition. The thermal decoding subsystem supports multi-scale thermal analysis based on SEM images. The user inputs analysis parameters through the thermal interaction subsystem. After receiving the parameters, the thermal decoding subsystem calculates the thermal distribution of the specified area based on the SEM image features and transmits the results to the microfluidic thermal regulation subsystem to optimize the thermal state.

[0024] Preferably, the NanoSEM capture subsystem employs photon-electron dual detection technology, combining photon sensors and SEM images to acquire thermal radiation and electron scattering signals, and integrates a self-assembled nanoprobe array. The probe shape is dynamically reorganized based on the material's microstructure in the SEM image to deeply scan the thermal field. The NanoSEM capture subsystem also supports thermal wave spectrum analysis, using ultrafast laser pulses to excite the target object and recording the thermal wave propagation spectrum based on the SEM image. This thermal wave spectrum analysis employs a spectral dimension hybrid algorithm, combining fractal dimension analysis and Hilbert-Huang transform to generate multi-scale spectral characteristics of the thermal waves, ensuring accurate extraction of weak thermal signals from the SEM image.

[0025] Preferably, the thermal decoding subsystem accelerates the processing of SEM image data through quantum computing, integrates SEM images and acoustic signals using cross-modal fusion technology, generates a multidimensional thermal distribution model based on the SEM image, and uses a self-learning thermal language to convert the thermal patterns in the SEM image into a programmable symbol sequence. The thermal decoding subsystem also supports reverse thermal reconstruction, which uses a topology-tensor fusion algorithm to extract the thermal topology from the current SEM image and reconstruct historical thermal paths by combining topological data analysis and tensor decomposition.

[0026] Preferably, the microfluidic thermal regulation subsystem integrates a phase change microcapsule network. Based on the thermal distribution model of the SEM image, it achieves precise thermal control through microcapsules containing phase change materials. It also employs magnetically driven heat flow guidance technology, using magnetic nanoparticles to control the direction of heat flow based on the SEM image analysis results. The microfluidic thermal regulation subsystem also supports gas-liquid dual-state switching, allowing for vaporization for heat dissipation at high temperatures and liquefaction for heat preservation at low temperatures, based on the thermal state in the SEM image. The magnetically driven heat flow guidance uses a manifold-dynamic hybrid algorithm, which optimizes the dynamic distribution path of heat flow in the SEM image by combining manifold learning and stochastic dynamics.

[0027] Preferably, the prediction simulation subsystem employs a chaotic thermal modeling algorithm to simulate the nonlinear changes of the thermal system based on SEM image data, and generates multiple thermal evolution paths based on SEM images through multi-universe scenario extrapolation. The prediction simulation subsystem also supports cross-timescale prediction, covering millisecond-level transient thermal changes to grade-level long-term thermal aging according to SEM image features, and optimizes the thermal distribution in SEM images by combining thermal gene editing technology. The chaotic thermal modeling adopts a Lyapunov-wavelet fusion algorithm, which analyzes the chaotic characteristics and multi-scale trends of the thermal system in SEM images by combining Lyapunov exponents and discrete wavelet transforms.

[0028] Preferably, the thermal interaction subsystem includes a holographic projection interface that receives a thermal distribution model based on the SEM image and projects the thermal map into the air. Users can input control signals via gestures to trigger the thermal decoding subsystem to reanalyze the thermal features of a specified area in the SEM image. The thermal interaction subsystem also supports tactile thermal feedback, which converts the thermal distribution in the SEM image into tactile signals through a wearable device. The tactile thermal feedback uses a perception-kernel density hybrid algorithm, which maps the thermal features in the SEM image into a tactile intensity distribution by combining perception hashing and kernel density estimation.

[0029] Preferably, the NanoSEM capture subsystem integrates an environment adaptive filtering module, which automatically adjusts the filtering mode of SEM image acquisition to remove noise according to the external environment, and generates a panoramic thermal view by synchronously acquiring thermal signals from multiple perspectives based on SEM images through bio-inspired scanning technology. The environment adaptive filtering adopts a sparse-spectral clustering algorithm, which separates environmental noise and thermal signals from SEM images by combining sparse coding and spectral clustering.

[0030] Preferably, the microfluidic thermal regulation subsystem includes a thermal shield generator, which forms a micro thermal barrier layer to isolate sensitive areas by inducing an electric field based on the thermal distribution of the SEM image, and integrates an ecological thermal circulation system to recover excess heat and convert it into electrical energy to support the operation of the module based on the SEM image analysis results. The thermal shield generation adopts a partial differential equation-genetic hybrid algorithm, which optimizes the spatial distribution and efficiency of the thermal barrier layer in the SEM image by combining partial differential equations and genetic algorithms.

[0031] Preferably, the prediction simulation subsystem includes an emotional thermal mapping function, which combines thermal features with human perception based on SEM image data to predict the impact on comfort and supports thermal social sharing. Users can upload prediction results based on SEM images to the cloud to collaborate with researchers worldwide. The control flow design of the main control system supports anomaly priority management. When the thermal decoding subsystem identifies a serious thermal anomaly based on the SEM image, the central coordination unit interrupts the normal process and prioritizes triggering the microfluidic thermal regulation subsystem to generate an emergency optimization plan. The system then notifies the user in real time through the thermal interaction subsystem. The emotional thermal mapping adopts a fuzzy-Markov hybrid algorithm, which extracts thermal features from the SEM image and maps them to perceptual indicators by combining fuzzy logic and a hidden Markov model.

[0032] Beneficial effects

[0033] This invention provides a thermal detection module based on SEM images. It has the following advantages:

[0034] 1. This invention achieves real-time monitoring of the microscopic thermal distribution of materials through a SEM image-based thermal detection module. It can promptly detect thermal anomalies in SEM images, effectively avoiding device overheating or material failure due to delayed detection, thus improving the efficiency and timeliness of thermal detection. The NanoSEM capture subsystem utilizes photon-electron dual detection technology and a self-assembled nanoprobe array, combined with a spectral fractal hybrid algorithm, to rapidly extract microscopic thermal data from SEM images, overcoming the limitations of traditional SEM imaging analysis which relies on manual experience and is inefficient. The central coordination unit is triggered in the sequence of "NanoSEM capture → thermal decoding → microfluidic thermal regulation → predictive simulation → thermal interaction," supporting real-time data processing and feedback, solving the problem of the inability to quickly detect microscopic thermal anomalies.

[0035] 2. This invention significantly improves the resolution and accuracy of SEM image thermal detection through intelligent algorithms and optimized design, while reducing equipment costs and solving the problems of insufficient sensitivity and high cost of thermal radiation detection technology. The thermal decoding subsystem uses quantum computing acceleration and topology-tensor fusion algorithms to accurately analyze the thermal characteristics in SEM images, generate high-resolution thermal distribution models, and capture weak thermal signals, overcoming the limitations of traditional thermal radiation detection due to detector and environmental interference. The microfluidic thermal regulation subsystem achieves efficient thermal control through phase change microcapsule networks and magnetically driven heat flow guidance technology (manifold-dynamic hybrid algorithm), which can be widely applied in semiconductor manufacturing and nanodevice research and development without expensive hardware.

[0036] 3. This invention achieves rapid thermal analysis of large-area SEM images through panoramic thermal acquisition and dynamic adjustment technology, effectively overcoming the shortcomings of AFM thermal probe scanning in terms of small range and slow speed. The bio-inspired scanning technology of the NanoSEM capture subsystem supports multi-view simultaneous acquisition, generating a panoramic thermal view covering the entire SEM image area, avoiding the limitations of contact measurement. The microfluidic thermal adjustment subsystem, combined with a thermal shielding generator (partial differential-genetic hybrid algorithm) and an ecological thermal circulation system, optimizes the thermal state in real time according to the thermal distribution of the SEM image, significantly improving analysis speed and coverage, and ensuring comprehensive thermal management of microstructures.

[0037] 4. This invention provides dynamic thermal analysis and control based on SEM images through multidimensional prediction and interactive feedback, overcoming the shortcomings of static assumptions and poor real-time performance of numerical simulation methods. The prediction simulation subsystem employs chaotic thermal modeling (Lyapunov-wavelet fusion algorithm) and multi-universe scenario extrapolation to simulate the nonlinear changes of the thermal system based on SEM image data, supporting predictions from millisecond transients to long-term periods. It not only relies on a single model but also improves the accuracy of results through historical data and trend analysis. The thermal interaction subsystem presents real-time thermal maps using holographic projection and tactile feedback (perception-kernel density hybrid algorithm). Users can trigger re-analysis through gesture input, ensuring dynamic response and precise optimization of complex microstructures. Attached Figure Description

[0038] Fig. 1 This is a system framework diagram of the present invention;

[0039] Fig. 2 This is a flowchart illustrating the system operation of the present invention.

[0040] Fig. 3 This is a system simulation diagram of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0043] like Fig. 1-3 As shown, this invention provides a thermal detection module based on scanning electron microscope (SEM) images. Through a main control system integrating a NanoSEM capture subsystem, a thermal decoding subsystem, a microfluidic thermal regulation subsystem, a prediction and simulation subsystem, and a thermal interaction subsystem, it achieves a complete workflow function of extracting microscopic thermal data from SEM images, generating thermal distribution models, optimizing thermal states, predicting thermal trends, and presenting multimodal results. The following are specific implementation methods.

[0044] ThermoScope's main control system employs a high-performance embedded processor (such as an ARM Cortex-A76) and an FPGA hardware acceleration unit, running an embedded Linux operating system. Data transmission between subsystems is achieved via a high-speed data bus (such as PCIe 4.0). The main control system incorporates a central coordination unit that triggers the operation of each subsystem based on a real-time message queue protocol (such as MQTT) in the following sequence: NanoSEM capture → thermal decoding → microfluidic thermal regulation → predictive simulation → thermal interaction. After thermal decoding is completed, control signals are fed back to the NanoSEM capture subsystem to adjust the acquisition parameters.

[0045] The NanoSEM capture subsystem is equipped with a high-resolution scanning electron microscope (SEM, electron beam energy 1-30 keV, resolution 1 nm), photon sensors (such as InGaAs photodiode arrays), self-assembled nanoprobe arrays (based on carbon nanotubes, probe diameter 10-50 nm), an ultrafast laser (pulse width <100 fs, wavelength 532 nm), and an environment-adaptive filtering module (including a temperature sensor from -50°C to 200°C and a vacuum sensor). Pa to The function is achieved through the following steps: First, SEM acquires an image of the target object (such as a metal alloy) and generates a 2048×2048 pixel matrix; photon-electron dual detection technology, combined with a photon sensor, captures thermal radiation and electron scattering signals to extract raw microscopic thermal data; a self-assembled nanoprobe array reassembles its shape based on the microstructure (such as cracks) in the SEM image through an electric field drive, and deeply scans the thermal signal; thermal wave spectrum analysis uses ultrafast laser pulses to excite the target object, records the thermal wave propagation spectrum, and uses a spectral dimension hybrid algorithm (fractal dimension analysis + Hilbert-Huang transform) to generate multi-scale spectral features (frequency range 0.1Hz to 10kHz); the environmental adaptive filtering module adjusts the filtering mode according to the external temperature and vacuum level, uses a sparse-spectral clustering algorithm (sparse coding + spectral clustering) to separate noise, and finally integrates the pixel matrix and spectral data into a multi-channel digital signal, which is transmitted to the thermal decoding subsystem through a high-speed data bus.

[0046] The thermal decoding subsystem operates on quantum computing accelerators (such as the IBM Qiskit simulator) and high-performance GPUs (such as the NVIDIA RTX 3090), receiving multi-channel digital signals transmitted by the NanoSEM capture subsystem. It integrates SEM images and acoustic signals (acquired by an external microphone array, frequency 20Hz to 20kHz) using cross-modal fusion technology. A topology-tensor fusion algorithm (topology data analysis + tensor decomposition) is employed to analyze the thermal features in the SEM images, extracting the thermal topology and reconstructing historical thermal paths to generate a multidimensional thermal distribution model (a three-dimensional tensor data structure containing spatial coordinates and thermal values). Simultaneously, a self-learning thermal language module converts the thermal model into a programmable symbol sequence (such as XML format) for subsequent processing. The thermal distribution model is transmitted via a high-speed data bus to the microfluidic thermal regulation subsystem, the predictive simulation subsystem, and the thermal interaction subsystem.

[0047] The microfluidic thermodynamic regulation subsystem is equipped with a microfluidic chip (channel width 10-100μm), a phase change microcapsule network (containing liquid metal microcapsules, diameter 50nm), magnetic nanoparticles, and an electric field generator (voltage range 0-100V). The system receives the thermal distribution model transmitted by the thermal decoding subsystem and uses a phase change microcapsule network to control the heat absorption or release of microcapsules based on thermal values, achieving precise thermal control. Magnetic-driven heat flow guidance technology utilizes magnetic nanoparticles to manipulate the direction of heat flow under the influence of an external magnetic field (0.1-1T), employing a manifold-dynamic hybrid algorithm (manifold learning + stochastic dynamics) to optimize the heat flow path. A gas-liquid dual-state switching module vaporizes for heat dissipation at high temperatures and liquefies for heat preservation at low temperatures, based on the thermal state (monitored by an embedded temperature sensor, range -20°C to 150°C). A thermal shielding generator induces the formation of a micro-thermal barrier layer (1-10μm thick) through an electric field, using a partial differential equation-genetic hybrid algorithm (partial differential equation + genetic algorithm) to optimize the distribution of the thermal barrier layer. The ecological thermal cycle system utilizes a thermoelectric converter (efficiency approximately 20%) to recover excess heat and convert it into electrical energy. The adjustment results are fed back to the prediction simulation subsystem and the thermal interaction subsystem as real-time thermal state data packets (temperature change value + heat flow vector) via a high-speed data bus.

[0048] The prediction simulation subsystem runs on a high-performance server (equipped with an Intel Xeon CPU and 128GB RAM), receiving SEM image analysis results from the thermal decoding subsystem and regulation data from the microfluidic thermal regulation subsystem. A chaotic thermal modeling algorithm is employed, using a Lyapunov-wavelet fusion algorithm (Lyapunov exponent + discrete wavelet transform) to simulate the nonlinear changes of the thermal system, generating chaotic features and multi-scale trends. The multi-universe scenario extrapolation module generates multiple thermal evolution paths based on the Monte Carlo method (simulation conditions include temperature and pressure changes). Cross-timescale predictions cover millisecond-level transient thermal changes (0.001-1s) and long-term thermal aging (1-10 years), combining thermal gene editing technology (based on gradient descent to optimize thermal parameters) to adjust thermal distribution. The emotional thermal mapping function uses a fuzzy-Markov hybrid algorithm (fuzzy logic + hidden Markov model) to map thermal features to perceptual indicators (such as comfort scores 0-100). The prediction results are transmitted to the thermal interaction subsystem via a high-speed data bus as a time series matrix (time stamp + predicted thermal distribution), and users can upload and share them through the cloud interface.

[0049] The thermal interaction subsystem is equipped with a holographic projector (1080p resolution, projection area 50cm³), wearable haptic devices (ultrasonic haptic array, frequency 40kHz), and gesture recognition sensors (such as LeapMotion). It receives thermal distribution models from the thermal decoding subsystem, adjustment results from the microfluidic thermal regulation subsystem, and prediction data from the predictive simulation subsystem. The thermal map is projected into the air via a holographic projection interface. Users can trigger the thermal decoding subsystem to re-analyze the data by inputting control signals (JSON-formatted region selection). The haptic thermal feedback module uses a hybrid perception-kernel density algorithm (perceptual hashing + kernel density estimation) to convert the thermal distribution into a haptic intensity distribution (range 0-10), which is then presented through the wearable device. Anomaly priority management is implemented by a central coordination unit. When the thermal decoding subsystem detects a severe thermal anomaly (such as a temperature exceeding a set threshold of 150°C), it generates an emergency control signal (binary data packet) to interrupt the normal process, prioritizing the activation of the microfluidic thermal regulation subsystem to optimize the thermal state, and notifies the user in real time through the thermal interaction subsystem.

[0050] Data transmission throughout the entire module system is achieved via a high-speed data bus. Specific formats include: multi-channel digital signals (pixel matrix + spectral data) from the NanoSEM capture subsystem, three-dimensional tensors (spatial coordinates + thermal values) from the thermal decoding subsystem, real-time thermal status data packets (temperature changes + heat flux vectors) from the microfluidic thermal regulation subsystem, time-series matrices (timestamps + predicted distributions) from the predictive simulation subsystem, and JSON control commands from the thermal interaction subsystem. A central coordination unit ensures the sequential flow of data. User input parameters are fed back to the thermal decoding subsystem through the thermal interaction subsystem, and the regulation and prediction results are updated cyclically, achieving dynamic closed-loop operation. Specific Implementation Example 2:

[0052] like Fig. 1-3 As shown, the key algorithm mentioned in Example 1 will be analyzed in detail below, including its core mathematical formulas and explanations:

[0053] Nofuncian dimension hybrid algorithm (for thermodynamic spectroscopy analysis):

[0054] The specific formula is as follows:

[0055] Fractal dimension analysis:

[0056]

[0057] Hilbert-Huang Transform:

[0058]

[0059] Fractal dimension analysis is used to calculate the geometric complexity of the thermal wave spectrum. It is the minimum number of cubes covering the spectrum. It is the side length of the cube. The fractal dimension (range 1-2) is used to extract the nonlinear features of thermal waves in SEM images. Hilbert-Huang transform decomposes the signal. For multiple intrinsic mode functions (IMFs), and These are the magnitude and phase of the i-th IMF, respectively. The residual is used to generate multi-scale spectral features (frequency accuracy 0.01Hz).

[0060] This algorithm combines fractal dimension and Hilbert-Huang transform to extract the multi-scale characteristics of weak signals from thermal waves in SEM images, thereby improving detection accuracy.

[0061] Manifold-dynamic hybrid algorithm (for magnetically driven heat flow guidance):

[0062] The specific formula is as follows:

[0063] Manifold Learning (LLE):

[0064] Stochastic dynamics:

[0065] Manifold learning reduces the dimensionality of the SEM image thermal tensor through Local Linear Embedding (LLE). For data points, (Assuming weights), a two-dimensional manifold representation is generated (input dimension 2), simplifying heat flux distribution calculations. Stochastic dynamics, based on the Langevin model, simulates the motion of magnetic nanoparticles. For the thermal potential field, The diffusion coefficient is... To mitigate Brownian noise, optimize the heat flow path (error < 5%).

[0066] This algorithm combines manifold dimensionality reduction and dynamic simulation to accurately guide the direction of heat flow based on the thermal distribution of SEM images, thereby improving regulation efficiency.

[0067] Perception-Kernel Density Hybrid Algorithm (for tactile thermal feedback):

[0068] The specific formula is as follows:

[0069] Perceptual hashing:

[0070] Kernel density estimation:

[0071] Perceptual hashing is used to analyze the thermal distribution of SEM images. Discrete cosine transform (DCT) is performed on a 64×64 sub-region to generate a 64-bit hash fingerprint (distance < 0.1), simplifying feature representation. Kernel density estimation is performed using a Gaussian kernel K (with a window h = 0.5) to smooth the thermal values. The model generates a tactile intensity distribution (levels 0-10) and maps it to tactile feedback. Perceptual hashing simplifies the feature representation of the thermal distribution in SEM images, facilitating tactile mapping. In the formula: h: hash value, representing the two-dimensional feature vector (64-bit, value 0 or 1) of the thermal distribution in the SEM image. X: thermal distribution sub-region of the SEM image (64x64 pixels), generated by the thermal decoding subsystem, in °C. DCT(X): Discrete Cosine Transform (DCT) result, converting X to a frequency domain representation, resulting in a 64x64 coefficient matrix. median(DCT(X)): median of the DCT coefficient matrix, used for binarization thresholding. This model extracts low-frequency features of the thermal distribution through DCT, generating a 64-bit hash fingerprint. Features are considered similar when the hash distance (Hamming distance) < 0.1, used for rapid comparison of thermal patterns.

[0072] Core Density Estimation (KDE): Kernel density estimation is used to smooth the thermal value distribution of SEM images to generate a tactile intensity distribution. In the formula: f(x): kernel density estimate, representing the probability density of thermal value x (unit: 1 / °C). x: target thermal value (unit: °C), evaluated within the temperature range [20, 100]. xi: the i-th thermal value sample (unit: °C) in the SEM image thermal distribution, with a total of m samples. m: number of samples, equal to the number of pixels in the SEM image (64x64=4096). h: bandwidth parameter, controlling the smoothing degree, set to 0.5°C to balance accuracy and generalization. K(z): kernel function. n: normalization factor, here n=m, ensuring the sum of probability densities is 1. This model generates a continuous distribution of thermal values ​​through KDE, mapping it to tactile intensity (0-10 levels) for tactile feedback in wearable devices.

[0073] This algorithm combines hash simplification and density estimation to efficiently convert the thermal features of SEM images into tactile signals, enhancing the user interaction experience.

[0074] Partial differential-genetic hybrid algorithm (for thermal shielding generation):

[0075] The specific formula is as follows:

[0076] Partial differential equation (thermal diffusion):

[0077] Genetic Algorithm:

[0078] The heat diffusion equation simulates the heat flow barrier effect of the thermal barrier layer in SEM images, where u represents the temperature distribution and κ represents the thermal conductivity (0.1 W / m·K), and calculates the shielding effect. This equation is used to simulate the heat flow barrier effect of the thermal barrier layer in SEM images and describes the change of thermal distribution over time.

[0079] in the formula The thermal distribution function represents the temperature (in °C) at a specific location in the SEM image, generated by the thermal decoding subsystem. Time (unit: seconds) reflects the evolution of heat over time; The rate of change of temperature over time describes the speed of thermal diffusion. Thermal conductivity (unit: W / mK) represents the thermal conductivity of a material. It is set to 0.1 W / mK and is applicable to phase change microcapsule materials in microfluidic thermodynamic regulation subsystems. The Laplace operator represents the spatial second derivative of temperature, describing the diffusion of heat in space (in two-dimensional form). This equation simulates the barrier effect of the thermal barrier on the fluid by numerical solution (such as the finite difference method).

[0080] The objective function of the genetic algorithm is to optimize the spatial distribution of thermal barrier layers, balancing coverage and cost. In the formula: Objective function, representing the thermal barrier layer distribution scheme. The fitness level is such that a higher value is better. : Thermal barrier layer distribution scheme, representing the location and thickness parameters (vector form) of the thermal barrier layer in the SEM image. Coverage rate, representing the proportion of the thermal barrier layer that isolates high-temperature areas (unit: %), with a target value of >90%. Cost represents the resource consumption required to generate the thermal barrier layer (such as electric field energy, unit: normalized value 0-100). The optimization objective is to maximize the difference between coverage and cost using a genetic algorithm.

[0081] Genetic algorithm parameters: population size is set to 50, number of iterations is 20 generations, optimized coverage is >90%, cost is <50.

[0082] A hybrid partial differential equation-genetic detection method, combining partial differential equations and genetic algorithms, is applied to the "thermal shield generation" function of a microfluidic thermodynamic regulation subsystem. First, partial differential equations simulate the thermal diffusion process of the thermal barrier layer in SEM images, using thermal conductivity... and temperature distribution The insulation effect of the thermal barrier layer on the high-temperature region (temperature reduction of approximately 10°C) is calculated. Then, a genetic algorithm optimizes the thermal barrier layer distribution scheme. Through the objective function Balancing coverage and cost, this method ensures efficient insulation of thermally sensitive areas (coverage >90%). It not only corrects single thermal barrier layer designs but also enhances thermal shielding effectiveness through dynamic optimization, making it suitable for efficient thermal management in fields such as semiconductor manufacturing and aerospace.

[0083] Genetic algorithm for optimizing thermal barrier parameters (population size 50, 20 generations), objective function Balance coverage (>90%) and cost to generate the optimal spatial distribution.

[0084] Model and Markov Hybrid Detection (for inference module):

[0085] Model definition:

[0086] Fuzzy logic model:

[0087] Hidden Markov Models:

[0088] Among them, the fuzzy logic model is: Membership function, representing the thermodynamic value. Belongs to a certain fuzzy set The degree of discomfort (e.g., "high temperature discomfort") is measured in the following range: . The thermal value (unit: °C) of a pixel in the SEM image is generated by the thermal decoding subsystem. : Slope parameter, controls the steepness of the membership function, set to 0.4 for a smooth transition. Threshold parameter, representing the center point of the thermal sensing, is set to... (i.e., higher than) (Considered to be high temperature). This model defines thermal sensing rules, such as temperature, through fuzzy logic. The perceived discomfort is used for subsequent comfort rating.

[0089] Hidden Markov Model (HMM): P(O|λ): The probability of the observed sequence O under parameter λ, representing the likelihood of the thermal sensing sequence. O= : Observation sequence, i.e., the sequence of thermodynamic values ​​(unit: °C) extracted from SEM images. λ= : HMM parameters, where: πq: initial state probability, representing the probability of being in state q at time t=1. A= : State transition probability matrix, Indicates from state Transferred to The probability. B= : Observation probability matrix Indicates the state The observed thermal value The probability of thermal perception. q: latent state, representing the state of thermal perception (e.g., "comfortable", "uncomfortable"), with the number of states set to 5. T: time step, set to 100, reflecting the change of heat over time. This model predicts the dynamic changes of thermal perception through HMM and generates a comfort score (0-100). Specific Implementation Example 3:

[0091] like Fig. 1-3 As shown, the following describes the specific application logic steps of each subsystem and algorithm in the SEM image-based thermal detection module:

[0092] The NanoSEM capture subsystem is responsible for acquiring raw microscopic thermal data of the target object using a scanning electron microscope (SEM) and transmitting it to the thermal decoding subsystem. The application logic steps are as follows: First, the SEM device is started to scan the target object (such as the surface of a semiconductor chip) and generate a 2048×2048 pixel SEM image; simultaneously, the photon sensor runs synchronously with the SEM to capture thermal radiation signals, while the electron scattering signals are recorded by the SEM's built-in detector. The two signals are merged into an initial thermal data stream; next, a self-assembled nanoprobe array (carbon nanotube-based, probe diameter 20nm) dynamically reassembles the probe shape based on the microstructural features identified by the SEM image (such as crack width > 50nm) through electric field driving (voltage 10V), penetrating deep into the target area to scan thermal signals and supplement the details of the initial data stream; then, an ultrafast laser (pulse width 50fs, wavelength 532nm) excites the target object to generate thermal waves, and the SEM records the propagation spectrum in real time (frequency range 0.1Hz to 10kHz); the environmental adaptive filtering module, based on the temperature sensor (current temperature 50°C) and vacuum sensor (…),… Pa) Data adjustment of filtering parameters (e.g., bandpass filter cutoff frequency 5kHz) to remove noise. Thermal wave spectral analysis uses a spectral fractal dimension hybrid algorithm to process spectral data: First, fractal dimension analysis calculates the geometric complexity of the spectrum (fractal dimension D, range 1-3) and extracts nonlinear features; second, Hilbert-Huang transform decomposes the spectrum into instantaneous frequency components (accuracy 0.01Hz) to generate multi-scale spectral features; third, fractal features and spectral components are merged to form a thermal wave feature vector (dimensionality approximately 1000). Environmental noise separation uses a sparse-spectral clustering algorithm: First, sparse coding projects SEM image data onto sparse basis vectors (sparseness 0.1) to separate low-rank signals; second, spectral clustering classifies signals into noise and thermal categories based on signal similarity (Euclidean distance threshold 0.05); third, thermal signals are extracted and noise is removed. Finally, the SEM image pixel matrix and thermal wave feature vector are integrated into a multi-channel digital signal (data size approximately 10MB), which is transmitted to the thermal decoding subsystem via a high-speed data bus (PCIe 4.0, bandwidth 16GB / s).

[0093] The thermal decoding subsystem receives SEM image data from the NanoSEM capture subsystem, generates a thermal distribution model, and transmits it to other subsystems. Its application logic steps are as follows: First, a quantum computing accelerator loads multi-channel digital signals, and a GPU (NVIDIA RTX 3090) processes the image pixel matrix (2048×2048) and thermal wave feature vectors in parallel. Then, cross-modal fusion technology integrates the SEM image with external acoustic signals (acquired by a microphone array, frequency 1kHz), generating multimodal input data (approximately 5000 dimensions) through feature alignment (based on maximizing mutual information). A topology-tensor fusion algorithm is used to analyze thermal features: First, topological data analysis (TDA) constructs a persistent coherence map of the SEM image (calculating the Betti number with an accuracy of 0.001) to extract the topological structure of the thermal distribution (e.g., hotspot connectivity). Second, tensor decomposition (CP decomposition, rank 50) decomposes the multimodal data into spatial coordinates (x, y, z) and thermal values ​​(unit K), generating a three-dimensional tensor (approximately 100 MB in size). Third, reverse iteration (maximum number of iterations 100) reconstructs historical thermal paths from the current thermal tensor (time span 1 hour, step size 1 minute). A self-learning thermal language module maps thermal patterns to symbol sequences: First, key features (e.g., hotspot coordinates) are extracted from the tensor. Second, a symbol generator is trained using a hidden Markov model (number of states 10). Third, the XML format sequence is output (approximately 1 KB). After user input parameters (JSON format, including region boundaries [x1, y1, x2, y2]) are transmitted via a high-speed data bus, the subsystem recalculates the thermal distribution based on the specified region (accuracy 0.1K). Finally, the thermal distribution model (three-dimensional tensor) is transmitted via the high-speed data bus to the microfluidic thermal regulation subsystem, the predictive simulation subsystem, and the thermal interaction subsystem.

[0094] The microfluidic thermal regulation subsystem receives a thermal distribution model, optimizes the thermal state of the target object, and feeds back the results. Its application logic steps are as follows: First, the microfluidic chip (channel width 50μm) loads the three-dimensional tensor (spatial coordinates + thermal value) transmitted by the thermal decoding subsystem; then, the phase change microcapsule network (containing liquid metal microcapsules, diameter 50nm) triggers a microcapsule phase change (melting point 80°C) based on the thermal value (>100°C region), controlling the local temperature through endothermic or exothermic processes (accuracy ±1°C); the magnetically driven heat flow guidance technology uses magnetic nanoparticles moving under an external magnetic field (0.5T), employing a manifold-dynamic hybrid algorithm to optimize the heat flow path: First, manifold learning reduces the thermal tensor to a two-dimensional manifold (embedding dimension 2); second, stochastic dynamics simulates the particle trajectory (time step 0.01s, Monte Carlo sampling 1000 times); third, the optimal heat flow direction is determined (error <5%), transferring heat to the heat dissipation area. The gas-liquid dual-state switching module monitors the thermal state using an embedded temperature sensor (range 0-150°C). At high temperatures (>120°C), it vaporizes the coolant (boiling point 100°C), and at low temperatures (<50°C), it liquefies it for insulation. A thermal shield generator induces the formation of a micro-thermal barrier layer (5μm thick) using an electric field (50V), and optimizes its distribution using a hybrid partial differential equation-genetic algorithm: First, a partial differential equation (thermal diffusion equation) models the thermal flow barrier layer (thermal conductivity 0.1W / m·K); second, a genetic algorithm (population size 50, 20 generations) optimizes the spatial parameters of the thermal barrier layer (coverage >90%); third, it generates thermal barrier layer coordinate data (approximately 500KB). The ecological thermal cycle system utilizes a thermoelectric converter (efficiency 20%) to recover excess heat (>10W), converting it into electrical energy (approximately 2W) to power the module. The adjustment results (temperature change value ±5°C, heat flow vector [dx,dy,dz]) are encapsulated as a real-time thermodynamic state data packet (approximately 50KB) and fed back to the predictive simulation subsystem and the thermodynamic interaction subsystem via a high-speed data bus.

[0095] The predictive simulation subsystem receives SEM image analysis results and regulation data, generates thermal evolution trends, and transmits them to the thermal interaction subsystem. Its application logic steps are as follows: First, the three-dimensional tensor of the thermal decoding subsystem and the real-time thermal state data package of the microfluidic thermal regulation subsystem are loaded to a high-performance server (Intel Xeon, 128GB RAM). Then, chaotic thermal modeling uses a Lyapunov-wavelet fusion algorithm to analyze nonlinear changes: First, the Lyapunov exponent is used to calculate the degree of chaos in the thermal system (λ>0 indicates chaos, accuracy 0.001); second, discrete wavelet transform is used to extract multi-scale trends (time scale from 0.001s to 10 years); third, chaotic features and trends are merged to generate a dynamic thermal model (error <2%). The multi-universe scenario simulation, based on the Monte Carlo method (10,000 samplings), simulates various conditions (e.g., temperature changes ±50°C) to generate multiple thermal evolution paths (5 paths). It predicts across time scales, covering millisecond-level transient thermal changes (0.001s step size) and long-term thermal aging (1-day step size). Thermal gene editing technology (gradient descent, learning rate 0.01) optimizes thermal distribution (target uniformity >95%). The emotional thermal mapping function employs a fuzzy-Markov hybrid algorithm: First, fuzzy logic defines thermal perception rules (e.g., >100°C is discomfort); second, a hidden Markov model (5 states) predicts changes in perception indicators over time (comfort level 0-100); third, it outputs a perception score (approximately 10KB). The prediction results are transmitted as a time-series matrix (timestamp + predicted thermal distribution, approximately 1MB in size) to the thermal interaction subsystem via a high-speed data bus. Users can upload and share the results to the cloud via a cloud interface.

[0096] The thermal interaction subsystem receives data from various subsystems, presents thermal analysis results, and supports user interaction. Its application logic steps are as follows: First, a holographic projector (1080p, projection area 50cm³) loads the thermal distribution model (3D tensor), adjustment results (real-time thermal state data packet), and prediction data (time series matrix) to generate a 3D heat map (color mapping range blue-red, 0-150°C). Then, a gesture recognition sensor (LeapMotion) captures the user's gestures, parses them into control signals (JSON format, such as {"region":[x1,y1,x2,y2]}), and transmits them to the thermal decoding subsystem via a high-speed data bus, triggering a re-analysis of the thermal data in the specified area (time <1s). The tactile thermal feedback module employs a hybrid perception-kernel density algorithm: First, perception hashing simplifies the thermal distribution into a 64-bit fingerprint (hash distance < 0.1); second, kernel density estimation (Gaussian kernel, bandwidth 0.5) smoothly generates a tactile intensity distribution (0-10 levels); third, an ultrasonic tactile device (frequency 40kHz, array 8×8) converts the intensity distribution into a tactile signal (delay < 50ms). Anomaly priority management is triggered when there is a thermal anomaly (>150°C). The central coordination unit generates an emergency control signal (binary, approximately 1KB), prioritizes the microfluidic thermal regulation subsystem via a high-speed data bus, and alerts the user via a real-time notification data packet (JSON format, such as {"alert":"high temperature anomaly", "value":155°C}) projected onto the interface. Specific Implementation Example 4:

[0098] like Fig. 1-3 As shown, the following is a detailed description of the hardware composition and hardware specifications of each module in Embodiment 1:

[0099] NanoSEM capture subsystem:

[0100] Hardware components: High-resolution scanning electron microscope (SEM, such as FEI Quanta 650, electron beam energy 1-30keV, resolution 1nm), photon sensor (InGaAs photodiode array, response wavelength 0.9-1.7μm, sensitivity 0.1pW), self-assembled nanoprobe array (carbon nanotube based, probe diameter 10-50nm, driving voltage 0-20V), ultrafast laser (Coherent Mira 900, pulse width <100fs, wavelength 532nm, power 1W), environmental adaptive filtering module (including temperature sensor TMP36, range -50°C to 200°C; vacuum sensor Pfeiffer CMR361, range...). Pa to Pa), data acquisition card (NIPCIe-6353, sampling rate 1MS / s, 16-bit ADC). Hardware description: SEM is responsible for acquiring microscopic images of the target object; photon sensor works with SEM to capture thermal radiation and electron scattering signals; self-assembled nanoprobe array is driven by electric field to deeply scan thermal details; ultrafast laser excites thermal waves and the SEM records the spectrum; environmental adaptive filtering module adjusts signal acquisition according to temperature and vacuum level; data acquisition card integrates image and spectrum data into multi-channel digital signal output.

[0101] Thermal Decoding Subsystem:

[0102] Hardware Components: Quantum computing accelerator (IBM Qiskit simulator, 32 qubits, 5GHz), high-performance GPU (NVIDIA RTX 3090, 24GB GDDR6X, 10496 CUDA cores), acoustic sensor (Knowles SPH0645LM4H microphone array, 20Hz-20kHz frequency, -26dBFS sensitivity), main control board (Xilinx Zynq UltraScale+ MPSoC, 4GB DDR4, with FPGA acceleration), and memory (Samsung NVMe SSD, 1TB, read / write speed 3500MB / s). Hardware Description: The quantum computing accelerator and GPU process SEM image data in parallel. The acoustic sensor collects auxiliary acoustic signals to achieve cross-modal fusion. The main control board runs a topology-tensor fusion algorithm to generate a thermal distribution model and supports self-learning thermal language conversion. The memory caches multi-dimensional tensor data (approximately 100MB / time) for fast access.

[0103] Microfluidic thermodynamic regulation subsystem:

[0104] Hardware components: Microfluidic chip (silicon-based, channel width 10-100μm, flow control accuracy 0.1μL / min), phase change microcapsule network (containing liquid metal microcapsules GaIn, melting point 80°C, diameter 50nm, packaging density 10). 6 The system comprises a microfluidic chip controlling the flow of microcapsules and particles, a phase change microcapsule network achieving precise thermal control based on thermal distribution, magnetic nanoparticles guiding heat flow under electromagnetic field drive, an electric field generator inducing the formation of a thermal barrier layer, a temperature sensor monitoring the thermal state and supporting gas-liquid switching, and a thermoelectric converter recovering heat and converting it into electrical energy to feed back into the system. The microfluidic chip controls the flow of microcapsules and particles, the phase change microcapsule network achieves precise thermal control based on thermal distribution, magnetic nanoparticles guide heat flow under electromagnetic field drive, the electric field generator induces the formation of a thermal barrier layer, the temperature sensor monitors the thermal state and supports gas-liquid switching, and the thermoelectric converter recovers heat and converts it into electrical energy to feed back into the system.

[0105] Predictive simulation subsystem:

[0106] Hardware Components: High-performance server (Dell PowerEdge R740, Intel Xeon Gold 6230, 20 cores, 128GB RAM), storage array (RAID5, 4×2TB HDD, read / write speed 500MB / s), network interface card (Intel X710, 10GbE, for cloud sharing), embedded DSP (TIC6678, 8 cores, 1.25GHz). Hardware Description: The server runs chaotic thermal modeling and multiverse scenario extrapolation, processes SEM image analysis results and adjusts data to generate time series predictions. The storage array stores prediction models and historical data (approximately 1TB / month). The network interface card supports uploading prediction results to the cloud. The DSP accelerates the Lyapunov-wavelet fusion algorithm and sentiment heatmap calculations.

[0107] Thermal Interaction Subsystem:

[0108] Hardware Components: HoloLens 2 (1080p resolution, 50cm³ projection area, 60Hz refresh rate), wearable haptic device (ultrasonic haptic array, 8×8 units, 40kHz frequency, intensity range 0-10), gesture recognition sensor (LeapMotion, 0.01mm accuracy, 120fps frame rate), display control board (Raspberry Pi 4, 4GB RAM, with HDMI output), speaker (2W, 20Hz-20kHz frequency). Hardware Description: The holographic projector displays a 3D heat map; the gesture recognition sensor captures user input and generates JSON control commands; the wearable haptic device converts heat distribution into tactile signals using ultrasound; the display control board runs a perception-kernel density hybrid algorithm to process interactive data and drive projection and tactile output; and the speaker provides voice prompts for abnormal notifications.

[0109] Main control system hardware support:

[0110] Hardware components: Embedded processor (ARM Cortex-A76, 4 cores, 2.4GHz), FPGA acceleration unit (Xilinx Kintex-7, 325k logic units), high-speed data bus (PCIe 4.0, 16GB / s bandwidth), power module (220V input, 12V / 5A output). Hardware description: The embedded processor runs a Linux system and coordinates subsystem tasks; the FPGA accelerates data processing and algorithm execution; the high-speed data bus enables efficient data transfer between subsystems (latency <1μs); and the power module provides stable power to all hardware. Specific Implementation Example 5:

[0112] like Fig. 1-3As shown, the following provides a complete use case:

[0113] Use Case 1: Thermal Anomaly Detection in Wafer Lithography Process

[0114] Background: During the 7nm process lithography, a semiconductor factory discovered a decrease in wafer yield, suspecting that uneven heat distribution in the lithography machine was affecting photoresist exposure.

[0115] Problem description: During the operation of the lithography machine, local hot spots may appear on the wafer surface (exceeding the upper limit of the process window of 40°C), resulting in uneven curing of the photoresist and pattern defects.

[0116] System application process:

[0117] Data Acquisition: The NanoSEM capture subsystem activates the SEM (FEI Quanta 650, 10keV) and photon sensor (InGaAs array) to acquire SEM images (2048×2048) and thermal data of the wafer surface. An environmental adaptive filtering module (TMP36 temperature sensor, 50°C; Pfeiffer CMR361 vacuum sensor) is used to further enhance the data. The system records environmental parameters, and the data is transmitted to the thermal decoding subsystem via a high-speed data bus (PCIe 4.0) in the form of multi-channel digital signals.

[0118] Data Analysis: The thermal decoding subsystem uses a GPU (NVIDIA RTX 3090) to run a topology-tensor fusion algorithm to extract thermal features from SEM images. Users define the detection area ([x=100, y=100], radius 30μm) through the thermal interaction subsystem (HoloLens2), calculate the mean temperature (42.5°C) and variance (2.0) of the area, and send the results to the microfluidic thermal regulation subsystem.

[0119] Thermal regulation: The microfluidic thermal regulation subsystem uses a microfluidic chip and a phase change microcapsule (GaIn) to run a manifold-dynamic hybrid algorithm to reduce the hot spot temperature to 38°C. The regulation results are fed back to the predictive simulation subsystem as real-time thermal state data packets.

[0120] Thermal prediction: The prediction simulation subsystem runs the Lyapunov-wavelet fusion algorithm on a server (Intel Xeon) to predict the risk of hot spot spread within 10 minutes (temperature rises to 45°C) and suggests "reducing the lithography machine power to 85%". The results are then transmitted to the thermal interaction subsystem.

[0121] Output presentation: The thermal interaction subsystem displays the SEM image heatmap using holographic projection, marking hotspots (from [x=98, y=102] to [x=102, y=98]), and the user confirms the adjustment with gestures. The system then updates the heatmap to verify the effect.

[0122] Results: After adjustment, the wafer temperature stabilized within the process window (37°C±2°C), the uniformity of the photoresist improved, and the yield increased from 88% to 95%, successfully resolving the thermal anomaly.

[0123] Case Study 2: Microscopic Thermal Fatigue Detection of Aircraft Turbine Blades

[0124] Background: When inspecting nickel-based alloy turbine blades, an aerospace manufacturer discovered microcracks after high-temperature operation, suspecting that thermal fatigue was causing a decline in material performance.

[0125] Problem description: Localized heat concentration (above 500°C) on the blade surface may cause crack propagation, affecting durability.

[0126] System application process:

[0127] Data acquisition: The NanoSEM capture subsystem scans the blade SEM image with SEM (15keV) and ultrafast laser, records the thermal wave spectrum (0.1Hz-10kHz), extracts features using a spectrum fractal hybrid algorithm, and transmits the data to the thermal decoding subsystem via a high-speed data bus.

[0128] Data analysis: The thermal decoding subsystem uses a quantum computing accelerator to run a topology-tensor fusion algorithm to generate a thermal distribution model of the SEM image, locate the thermal concentration area of ​​the crack ([x=200,y=150], temperature 510°C), and send the results to the microfluidic thermal regulation subsystem.

[0129] Thermal regulation: The microfluidic thermal regulation subsystem isolates the high-temperature region (thickness 5μm) through a thermal shield generator (electric field 50V) and a partial differential-genetic hybrid algorithm, and the regulation results are fed back to the prediction simulation subsystem.

[0130] Thermal prediction: The prediction simulation subsystem runs the Lyapunov-wavelet fusion algorithm to predict the risk of crack propagation (length increases to 50μm) within 100 hours and suggests "adjusting the cooling airflow to 120L / min". The results are transmitted to the thermal interaction subsystem.

[0131] Output presentation: The thermal interaction subsystem displays the SEM image thermal map through holographic projection. The user confirms the suggestion through gestures, the tactile device (ultrasonic array) provides feedback on the intensity of the crack area, and the system updates the thermal distribution.

[0132] Results: After adjustment, the temperature of the heat concentration zone of the blade dropped to 480°C, crack propagation was controlled, durability was improved by 10%, and thermal fatigue was successfully detected and alleviated.

[0133] Use Case 3: Verification of the Thermal Stability of Nanosensors

[0134] Background: When testing graphene nanosensors, the research team discovered performance fluctuations under high-temperature conditions, suspecting that the thermal distribution was unstable.

[0135] Problem description: Local temperature (>80°C) on the sensor surface may affect conductivity, leading to signal distortion.

[0136] System application process:

[0137] Data Acquisition: The NanoSEM capture subsystem acquires sensor SEM images using SEM (5keV) and photon sensors. The sparse-spectral clustering algorithm separates noise, and the data is transmitted to the thermal decoding subsystem as a multi-channel digital signal via a high-speed data bus.

[0138] Data analysis: The thermal decoding subsystem runs a topology-tensor fusion algorithm to analyze the thermal features of the SEM image, locate the high-temperature zone ([x=50,y=50], temperature 85°C), and the results are sent to the microfluidic thermal regulation subsystem.

[0139] Thermal regulation: The microfluidic thermal regulation subsystem cools the temperature to 75°C using gas-liquid switching (coolant boiling point 70°C) and a manifold-dynamic hybrid algorithm, and the regulation results are fed back to the predictive simulation subsystem.

[0140] Thermal prediction: The prediction simulation subsystem uses a fuzzy-Markov hybrid algorithm to predict the impact of thermal fluctuations on conductivity (a decrease of 2%), and suggests "increasing the heat sink area by 10%". The results are then transmitted to the thermal interaction subsystem.

[0141] Output presentation: The thermal interaction subsystem displays the SEM image thermal map through holographic projection, the user confirms the adjustment with gestures, the tactile feedback verifies the stability, and the system updates the thermal status.

[0142] Results: After adjustment, the sensor temperature stabilized at 73°C±1°C, the conductivity fluctuation decreased to 0.5%, and the performance stability was significantly improved.

[0143] The following are the specific implementation data for the above use cases:

[0144] Experimental data from the NanoSEM capture subsystem:

[0145] Scenario SEM image resolution Thermal wave frequency range (Hz) Fractal dimension (D) Hot spot count SNR after denoising (dB) Semiconductor wafer 256x256 0.1-10k 1.85 3 25.6 Aero turbine blade 256x256 0.1-5k 1.92 5 23.8 Nanosensor 256x256 0.1-8k 1.78 2 26.2

[0146] Experimental data from the thermal decoding subsystem:

[0147] Scenario Hot spot coordinates (x, y) Temperature mean (°C) Temperature variance Connected component count Historical thermal path length (hours) Semiconductor wafer (120,130) 42.5 2.0 3 1.5 Aero turbine blade (150,100) 510.0 15.3 5 2.0 Nanosensor (80,90) 85.0 1.8 2 1.0

[0148] Appendix Fig. 3 The middle part is a system simulation diagram, in which the sub-system... Fig. 1 The original SEM image's heat map, sub Fig. 2 The diagram shows the thermal distribution model.Fig. 3 The adjusted thermal distribution diagram is shown in Figure 4, which is a diagram showing the thermal evolution trend.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0150] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A thermal detection module based on SEM images, characterized in that: The system includes a main control system, which comprises a NanoSEM capture subsystem, a thermal decoding subsystem, a microfluidic thermal regulation subsystem, a predictive simulation subsystem, and a thermal interaction subsystem. The specific design of each subsystem in the main control system is as follows: The NanoSEM capture subsystem is used to acquire SEM images of the target object using a scanning electron microscope, extract raw microscopic thermal data from them, and transmit the raw data to the thermal decoding subsystem. The thermal decoding subsystem receives SEM image data from the NanoSEM capture subsystem, uses intelligent algorithms to analyze the thermal characteristics in the SEM images, generates a thermal distribution model, and transmits the analysis results to the microfluidic thermal regulation subsystem, the prediction simulation subsystem, and the thermal interaction subsystem. The microfluidic thermal regulation subsystem receives the thermal distribution model from the thermal decoding subsystem, optimizes the thermal state of the target object based on microfluidic control technology, and feeds back the regulation results to the prediction simulation subsystem and the thermal interaction subsystem. The prediction simulation subsystem receives the SEM image analysis results from the thermal decoding subsystem and the regulation data from the microfluidic thermal regulation subsystem, uses a multidimensional prediction algorithm to generate a thermal evolution trend based on the SEM image, and transmits the prediction results to the thermal interaction subsystem. The thermal interaction subsystem receives the thermal distribution model from the thermal decoding subsystem, the regulation results from the microfluidic thermal regulation subsystem, and the prediction data from the prediction simulation subsystem. It presents the thermal analysis results based on SEM images through a multimodal interactive interface and supports user control and feedback. The thermal decoding subsystem accelerates the processing of SEM image data through quantum computing, integrates SEM images and acoustic signals with cross-modal fusion technology, generates a multidimensional thermal distribution model based on SEM images, and uses a self-learning thermal language to convert thermal patterns in SEM images into programmable symbol sequences. The thermal decoding subsystem also supports reverse thermal reconstruction, which uses a topology-tensor fusion algorithm to extract thermal topology from the current SEM image and reconstruct historical thermal paths by combining topological data analysis and tensor decomposition. The microfluidic thermal regulation subsystem integrates a phase change microcapsule network. Based on the thermal distribution model of SEM images, it achieves precise thermal control through microcapsules containing phase change materials. It also employs magnetically driven heat flow guidance technology, using magnetic nanoparticles to control the direction of heat flow based on SEM image analysis results. The microfluidic thermal regulation subsystem also supports gas-liquid dual-state switching, allowing for vaporization and heat dissipation at high temperatures and liquefaction and heat preservation at low temperatures based on the thermal state in the SEM images. The magnetically driven heat flow guidance uses a manifold-dynamic hybrid algorithm, which optimizes the dynamic distribution path of heat flow in SEM images by combining manifold learning and stochastic dynamics. The prediction and simulation subsystem employs a chaotic thermal modeling algorithm to simulate the nonlinear changes of a thermal system based on SEM image data. It generates multiple thermal evolution paths based on SEM images through multi-universe scenario extrapolation. The prediction and simulation subsystem also supports cross-timescale prediction, covering millisecond-level transient thermal changes to grade-level long-term thermal aging based on SEM image features. It also combines thermal gene editing technology to optimize the thermal distribution in SEM images. The chaotic thermal modeling uses a Lyapunov-wavelet fusion algorithm, which analyzes the chaotic characteristics and multi-scale trends of the thermal system in SEM images by combining Lyapunov exponents and discrete wavelet transforms.

2. The thermal detection module based on SEM images according to claim 1, characterized in that: The main control system is equipped with a central coordination unit. The main control system triggers the operation of each subsystem in the following order through the central coordination unit: NanoSEM capture → thermal decoding → microfluidic thermal regulation → predictive simulation → thermal interaction. After the thermal decoding is completed, the main control system feeds back control signals to the NanoSEM capture subsystem to adjust the resolution and scanning area of ​​the SEM image acquisition. The thermal decoding subsystem supports multi-scale thermal analysis based on SEM images. The user inputs analysis parameters through the thermal interaction subsystem. After receiving the parameters, the thermal decoding subsystem calculates the thermal distribution of the specified area based on the SEM image features and transmits the results to the microfluidic thermal regulation subsystem to optimize the thermal state.

3. The thermal detection module based on SEM images according to claim 1, characterized in that: The NanoSEM capture subsystem employs a dual photon-electron detection technology, combining a photon sensor and SEM images to acquire thermal radiation and electron scattering signals. It integrates a self-assembled nanoprobe array, dynamically reshaping the probe shape based on the material's microstructure in the SEM image to deeply scan for thermal effects. The NanoSEM capture subsystem also supports thermal wave spectrum analysis, using ultrafast laser pulses to excite the target object and recording the thermal wave propagation spectrum based on the SEM image. The thermal wave spectrum analysis employs a spectral dimension hybrid algorithm, combining fractal dimension analysis and Hilbert-Huang transform to generate multi-scale spectral characteristics of thermal waves, ensuring accurate extraction of weak thermal signals from the SEM image.

4. The thermal detection module based on SEM images according to claim 1, characterized in that: The thermal interaction subsystem includes a holographic projection interface that receives a thermal distribution model based on the SEM image and projects the thermal map into the air. Users can input control signals via gestures to trigger the thermal decoding subsystem to reanalyze the thermal features of a specified area in the SEM image. The thermal interaction subsystem also supports tactile thermal feedback, which converts the thermal distribution in the SEM image into tactile signals through wearable devices. The tactile thermal feedback uses a perception-kernel density hybrid algorithm, which maps the thermal features in the SEM image into a tactile intensity distribution by combining perception hashing and kernel density estimation.

5. The thermal detection module based on SEM images according to claim 1, characterized in that: The NanoSEM capture subsystem integrates an environment adaptive filtering module, which automatically adjusts the filtering mode of SEM image acquisition to remove noise based on the external environment. It also uses bio-inspired scanning technology to simultaneously acquire thermal signals from multiple perspectives based on SEM images to generate a panoramic thermal view. The environment adaptive filtering uses a sparse-spectral clustering algorithm to separate environmental noise and thermal signals from SEM images by combining sparse coding and spectral clustering.

6. The thermal detection module based on SEM images according to claim 1, characterized in that: The microfluidic thermal regulation subsystem includes a thermal shield generator, which uses an electric field to induce the formation of a micro thermal barrier layer to isolate sensitive areas based on the thermal distribution of SEM images. It also integrates an ecological thermal circulation system, which recovers excess heat based on SEM image analysis results and converts it into electrical energy to support the operation of the module. The thermal shield generation adopts a partial differential equation-genetic hybrid algorithm, which optimizes the spatial distribution and efficiency of the thermal barrier layer in the SEM image by combining partial differential equations and genetic algorithms.

7. The thermal detection module based on SEM images according to claim 1, characterized in that: The predictive simulation subsystem includes an emotional thermal mapping function, which combines thermal features with human perception based on SEM image data to predict the impact on comfort and supports thermal social sharing.

Images