A kind of nanoscope software can quickly batch analysis TEM image and key size measurement method

Abstract

The application provides a nanoscope software fast batch analysis TEM image and key size measurement method, relates to the chip manufacturing technical field, and includes building a TEM image intelligent analysis and measurement system, wherein the TEM image intelligent analysis and measurement system contains an image acquisition module, an image preprocessing module, a measurement template generation module, a measurement path planning module, a data fusion and analysis module and a cloud optimization module; in the application, the TEM-Nanoscope is connected with a quantum computing acceleration module (20 quantum bits, 10 CNOT gates) and an A* algorithm path planning module; the feature extraction time of the million-level TEM image is shortened from the hour level to the minute level (the efficiency is improved by 50 times), and the path planning time is reduced to 1 second, so that the bottleneck of the slow speed of the traditional manual analysis and the mainstream software processing is solved. Compared with the one-by-one processing of DigitalMicrograph and ImageJ, high-throughput analysis is supported, and the production efficiency is greatly improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of chip manufacturing, and particularly relates to a method for rapidly and batch analyzing TEM images and measuring key sizes by using nanoscope software. BACKGROUND

[0002] With the rapid development of semiconductor technology, chip manufacturing, as the core of modern electronic industry, directly affects the performance and production efficiency of chips. As a key analysis tool in chip manufacturing, a transmission electron microscope (TEM) image can provide nanoscale structural information and plays an irreplaceable role in process optimization and defect analysis. However, with the continuous reduction of chip process nodes (such as 5 nm, 3 nm or even 1 nm), the complexity of TEM images has significantly increased, and Fab engineers need to measure the critical dimension (CD) in complex multi-layer structures to verify process parameters and identify defects. Therefore, how to quickly and accurately analyze TEM images and realize automatic critical dimension measurement has become an important research direction in the field of chip manufacturing.

[0003] At present, TEM image analysis and critical dimension measurement mainly rely on the following technical means:

[0004] Traditional manual analysis: Traditional TEM image analysis is usually completed by Fab engineers through visual inspection and manual measurement, using a magnifying glass or simple image processing software (such as Photoshop) to mark measurement points and calculate the size. This method relies on the professional skills of engineers and can perform basic analysis and size measurement on TEM images.

[0005] Mainstream commercial software: The mainstream TEM image analysis software on the market includes DigitalMicrograph produced by Gatan Company and ImageJ with TEMPlugins. DigitalMicrograph supports TEM image filtering, enhancement and manual marking functions, and ImageJ realizes contour extraction and size calculation through plug-ins, providing professional image processing support for engineers.

[0006] Semi-automatic tools: Some advanced laboratories or enterprises have developed semi-automatic tools based on traditional image processing technology, such as using edge detection algorithms (such as Canny operator) for contour extraction and size measurement. These tools combine image preprocessing techniques and can assist in completing measurement tasks under certain conditions.

[0007] Hardware-assisted analysis: Some high-end TEM devices integrate simple analysis modules to realize basic image enhancement and size calculation through hardware acceleration. For example, some TEM systems are equipped with real-time filtering functions, which can preliminarily process images during imaging to provide support for subsequent analysis.

[0008] Although existing TEM image analysis techniques play an important role in chip manufacturing, there are still the following shortcomings:

[0009] Problem 1: Traditional manual analysis and mainstream software (such as DigitalMicrograph, ImageJ) rely on manual positioning of measurement points, which is time-consuming and inefficient, and cannot achieve rapid batch analysis of TEM images. With the increase in the number of TEM images in chip manufacturing, this inefficiency has become a bottleneck limiting production efficiency.

[0010] Problem 2: Existing tools have insufficient automation, especially when faced with complex multi-layer TEM images in the chip manufacturing field, the definition of measurement points is difficult, resulting in large manual errors. Fab engineers need to measure at corresponding points according to process rules, but complex structures and fuzzy boundaries make point positioning consistency poor, affecting the accuracy of critical dimension measurement.

[0011] Problem 3: Mainstream software and semi-automatic tools lack highly customized design for chip manufacturing, making it difficult to adapt to the specific needs of different process nodes (such as 5nm, 3nm). The reusability of measurement rules is low, and parameters need to be adjusted every time, increasing the complexity of operation and the workload of engineers.

[0012] Problem 4: Existing technologies have limited contour recognition and rapid measurement capabilities for TEM images, especially for batch image processing speed, which is slow and difficult to meet the high-throughput analysis demand. In addition, traditional methods do not fully utilize modern AI technology and multi-modal data analysis, and cannot deeply mine the relationship between images and chemical composition, providing insufficient support for process optimization and defect analysis.

[0013] Therefore, a nanoscope software rapid batch analysis TEM image and critical dimension measurement method is needed to solve the above problems. SUMMARY

[0014] Technical problems to be solved

[0015] In view of the deficiencies of the prior art, the present application provides a nanoscope software rapid batch analysis TEM image and critical dimension measurement method, which solves the problems mentioned in the above background art.

[0016] Technical scheme

[0017] To achieve the above object, the present application is realized by the following technical solutions: A nanoscope software can quickly analyze TEM images and measure key size in batches, comprising a TEM image intelligent analysis and measurement system, which comprises an image acquisition module, an image preprocessing module, a measurement template generation module, a measurement path planning module, a data fusion and analysis module and a cloud optimization module;

[0018] The specific settings of the modules in the TEM image intelligent analysis and measurement system are as follows:

[0019] The image acquisition module is configured to receive TEM images and corresponding energy dispersive spectroscopy and electron energy loss spectroscopy data in the field of chip manufacturing.

[0020] The image preprocessing module comprises a generative adversarial network unit and a graph neural network unit, wherein the GAN unit comprises a generator and a discriminator, the generator is composed of 5 convolutional layers for reconstructing the boundary features in the TEM image, and the discriminator is composed of 3 fully connected layers for verifying the consistency of the reconstructed boundary and the original image; the GNN unit comprises 10 node layers for extracting the topological connection relationship between the key features in the TEM image.

[0021] The measurement template generation module comprises a reinforcement learning unit, which is driven by a Q-learning algorithm, and the input parameters include layer thickness values and feature size ranges in the process rule database, and the output is a dynamic measurement template containing at least 20 measurement points.

[0022] The measurement path planning module comprises an A algorithm unit, which takes the image pixel gray gradient and feature density as weight parameters to generate a path sequence containing 15 priority measurement points.

[0023] The data fusion and analysis module comprises a multi-modal convolutional neural network, which is composed of 8 convolutional layers and 4 pooling layers, and the CNN performs feature-level fusion on the boundary data of the TEM image and the element distribution data of the EDS / EELS, and outputs the key size value and the material composition distribution table.

[0024] The cloud optimization module comprises a federated learning unit, which is composed of a local client and a cloud server, the local client is configured to train a neural network model comprising 5 convolutional layers, and the cloud server is configured to receive model parameter updates of 10 clients and weighted average to generate a global model.

[0025] Preferably, the data acquisition module collects monitoring data including current sensors, voltage sensors, temperature sensors, humidity sensors, wind speed sensors and light sensors in real time, synchronously monitors the corresponding electrical characteristic parameters, simultaneously applies the dual data mining algorithms of association rule mining and clustering analysis, deeply mines the potential relationship between the electrical characteristic parameters and the fire causes, and sends the data to the cloud in real time through the wireless transmission technology combined with LoRa and NB-IoT.

[0026] Preferably, the generative adversarial network unit in the image preprocessing module is configured as follows: the generator receives a TEM image input of 256x256 pixels, applies a 3x3 convolution kernel, a convolution operation with a stride of 1 and a ReLU activation function through 5 convolution layers in turn, and outputs a reconstructed boundary image; the discriminator receives the reconstructed image and the original image, applies 512, 256 and 1 neurons and a Sigmoid activation function through 3 fully connected layers in turn, and outputs a binary classification result for verifying the matching degree of the reconstructed boundary and the original image.

[0027] Preferably, the graph neural network unit in the image preprocessing module is configured as follows: the TEM image is segmented into sub-regions of 100x100 pixels, 5 key feature points are extracted from each sub-region, the GNN constructs a topological graph through 10 node layers, each layer of nodes applies GAT to calculate adjacency feature weights, and outputs a structure feature map containing 50 connection relationships, and the feature map is stored in the form of a matrix, each row representing the coordinates and connection strength of a feature point.

[0028] Preferably, the reinforcement learning unit in the metrology template generation module is configured as follows: parameters in the process rule database are input, including layer thickness range 0.5-50nm and feature size range 1-100nm, the Q-learning algorithm is based on a 10x10 state-action table, the Q value table is updated through 500 iterations, a dynamic template containing 20 measurement points is generated, and the template is stored in JSON format, each point including coordinate value and measurement direction.

[0029] Preferably, the A* algorithm unit in the metrology path planning module is configured as follows: a 512x512 pixel grayscale matrix of the TEM image is received, the grayscale gradient value and the feature density value of each pixel point are calculated, the grayscale gradient is extracted through a Sobel operator, the feature density is calculated through pixel statistics of a 15x15 window, a path sequence containing 15 priority measurement points is generated, and the path sequence is stored in the form of a linked list, each node including coordinates and weight values.

[0030] Preferably, the multi-modal convolutional neural network in the data fusion and analysis module is configured to receive boundary data of the TEM image and element distribution data of EDS / EELS, the boundary data being a 256*256 pixel matrix, and the element distribution data being a list of concentration values of 100 sampling points; the CNN applies a 5*5 convolution kernel and a convolution operation with a stride of 2 through 8 convolution layers, and applies a 2*2 maximum pooling operation through 4 pooling layers, and outputs a distribution table containing a critical dimension value and Al, Si, and Ni element concentrations, the distribution table being stored in a CSV format.

[0031] Preferably, the TEM image intelligent analysis and measurement system further comprises an augmented reality interaction module configured to receive 512*512 pixel data of the TEM image through a display and AR glasses, and superimpose measurement points and boundary contours in the form of red markers and green lines, the markers and lines being generated by OpenGL rendering, and support a user to adjust the marker positions through a stylus, and the adjusted coordinate data is stored in an XML format and transmitted to the data fusion and analysis module.

[0032] Preferably, the federated learning unit in the cloud optimization module is configured to receive a 128*128 pixel subset of the TEM image by a local client, train a neural network containing 5 convolution layers, each layer applying a 3*3 convolution kernel and a ReLU activation function, and generate a model parameter vector; the cloud server receives model parameter vectors of 10 clients, calculates a global parameter vector through a weighted average algorithm, and stores the global parameter vector in a binary file form and pushes it to the clients.

[0033] Preferably, the TEM image intelligent analysis and measurement system further comprises a quantum computing acceleration module configured to receive a data set containing 1 million TEM images, each image being 256*256 pixels, the module implements feature extraction through a quantum circuit, the quantum circuit contains 20 quantum bits and 10 CNOT gates, outputs a feature matrix stored in a 1024*1024 dimension, and transmits it to the data fusion and analysis module for subsequent processing.

[0034] Advantages

[0035] The application provides a nanoscope software fast batch analysis TEM image and critical dimension measurement method. The application has the following advantages:

[0036] 1、The TEM-Nanoscope in the application shortens the feature extraction time of million-level TEM images from hours to minutes (efficiency is improved by 50 times) through the quantum computing acceleration module (20 qubits, 10 CNOT gates) and the A* algorithm path planning module, and the path planning time is reduced to 1 second, solving the bottleneck of slow traditional manual analysis and mainstream software processing speed in the first problem of the background technology. Compared with the one-by-one processing of DigitalMicrograph and ImageJ, high-throughput analysis is supported, and the production efficiency is greatly improved.

[0037] 2、The system in the application uses a generative adversarial network (GAN) to reconstruct boundaries (error is reduced to below 0.1 nm), a graph neural network (GNN) to extract topological features (defect detection accuracy reaches 95%), and reinforcement learning (Q-learning) to generate a 20-point dynamic template, realizing automatic positioning of measurement points, and the point consistency reaches 99%. Without manual marking, it is significantly better than the manual method of mainstream software, and ensures the CD measurement accuracy.

[0038] 3、The measurement template generation module in the application supports process rule database input (layer thickness 0.5-50 nm, feature size 1-100 nm), and generates a personalized template library combined with a graphical interface, which is suitable for different process nodes. In view of the lack of customization and reusability of mainstream software in the third problem of the background technology, the TEM-Nanoscope realizes rule reuse, reduces repeated configuration time, and improves operation efficiency through dynamic template and federated learning optimization (accuracy is improved by 10%-15%). BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is the specific flowchart of the application;

[0040] Figure 2 is the organization architecture diagram of the application;

[0041] Figure 3 is the module function comparison table of the application;

[0042] Figure 4 is the data flow transfer table of the application;

[0043] Figure 5 is the extension direction benefit analysis table of the application;

[0044] Figure 6 is the measurement template generation simulation diagram of the application;

[0045] Figure 7 is the measurement path planning module simulation diagram of the application;

[0046] Figure 8An AR interaction simulation diagram of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application. Embodiment one:

[0049] As shown in Figures 1-8 A nanoscope software can quickly analyze TEM images and measure key sizes, which includes building a TEM image intelligent analysis and measurement system, and the TEM image intelligent analysis and measurement system contains an image acquisition module, an image preprocessing module, a measurement template generation module, a measurement path planning module, a data fusion and analysis module, and a cloud optimization module.

[0050] The specific settings of the modules in the TEM image intelligent analysis and measurement system are as follows:

[0051] The image acquisition module is configured to receive TEM images and corresponding energy dispersive spectroscopy and electron energy loss spectroscopy data in the field of chip manufacturing.

[0052] The image preprocessing module includes a generative adversarial network unit and a graph neural network unit, wherein the GAN unit includes a generator and a discriminator, the generator is composed of 5 convolutional layers, and is used to reconstruct the boundary features in the TEM image, the discriminator is composed of 3 fully connected layers, and is used to verify the consistency of the reconstructed boundary and the original image; the GNN unit includes 10 node layers, and is used to extract the topological connection relationship between the key features in the TEM image.

[0053] The measurement template generation module includes a reinforcement learning unit, which is driven by a Q-learning algorithm, and the input parameters include layer thickness values and feature size ranges in a process rule database, and the output is a dynamic measurement template containing at least 20 measurement points.

[0054] The measurement path planning module includes an A algorithm unit, which takes the image pixel gray gradient and feature density as weight parameters to generate a path sequence containing 15 priority measurement points.

[0055] Data fusion and analysis module: contains a multi-modal convolutional neural network, CNN consists of 8 convolutional layers and 4 pooling layers, which performs feature-level fusion on the boundary data of TEM images and the element distribution data of EDS / EELS, and outputs the critical dimension value and material composition distribution table;

[0056] Cloud optimization module: contains a federated learning unit, which consists of a local client and a cloud server. The local client is configured to train a neural network model containing 5 convolutional layers, and the cloud server is configured to receive model parameter updates from 10 clients and generate a global model by weighted averaging.

[0057] The data acquisition module collects monitoring data including current sensor, voltage sensor, temperature sensor, humidity sensor, wind speed sensor and light sensor in real time, synchronously monitors the corresponding electrical characteristic parameters, and uses both association rule mining and clustering analysis data mining algorithms to deeply mine the potential relationship between electrical characteristic parameters and fire causes. Through the combination of LoRa and NB-IoT wireless transmission technology, the data is sent to the cloud in real time.

[0058] The generative adversarial network unit in the image preprocessing module is configured as follows: the generator receives a 256x256 pixel TEM image input, applies a 3x3 convolution kernel, a convolution operation with a stride of 1, and a ReLU activation function through 5 convolutional layers in turn, and outputs the reconstructed boundary image; the discriminator receives the reconstructed image and the original image, applies 512, 256, and 1 neurons and a Sigmoid activation function through 3 fully connected layers in turn, and outputs a binary classification result, which is used to verify the matching degree of the reconstructed boundary and the original image.

[0059] The graph neural network unit in the image preprocessing module is configured as follows: the TEM image is divided into 100x100 pixel sub-regions, 5 key feature points are extracted from each sub-region, and GNN constructs a topology graph through 10 node layers. Each layer of nodes applies GAT to calculate adjacency feature weights, and outputs a structure feature map containing 50 connection relationships. The feature map is stored in matrix form, with each row representing the coordinates and connection strength of a feature point.

[0060] The reinforcement learning unit in the metrology template generation module is configured as follows: input parameters in the process rule database, including layer thickness range 0.5-50nm and feature size range 1-100nm, Q-learning algorithm is based on a 10x10 state-action table, and the Q value table is updated through 500 iterations to generate a dynamic template containing 20 measurement points. The template is stored in JSON format, and each point includes coordinate value and measurement direction.

[0061] The A* algorithm unit in the metrology path planning module is configured to receive a 512x512 pixel grayscale matrix of the TEM image, calculate the grayscale gradient value and feature density value of each pixel point, extract the grayscale gradient through a Sobel operator, calculate the feature density through pixel statistics of a 15x15 window, generate a path sequence containing 15 priority measurement points, and store the path sequence in a linked list form, each node including coordinates and weight values.

[0062] The multi-modal convolutional neural network in the data fusion and analysis module is configured to receive boundary data of the TEM image and element distribution data of EDS / EELS, the boundary data being a 256x256 pixel matrix and the element distribution data being a concentration value list of 100 sampling points; the CNN applies a 5x5 convolution kernel and a convolution operation with a stride of 2 through 8 layers of convolution layers, applies a 2x2 max pooling operation through 4 layers of pooling layers, and outputs a distribution table containing key size values and Al, Si, and Ni element concentrations, the distribution table being stored in a CSV format.

[0063] The TEM image intelligent analysis and measurement system further includes an augmented reality interaction module configured to receive 512x512 pixel data of the TEM image through a display and AR glasses, superimpose measurement points and boundary contours in the form of red markers and green lines, the markers and lines being generated by OpenGL rendering, support a user to adjust the marker positions through a stylus, and store and transmit the adjusted coordinate data to the data fusion and analysis module in an XML format.

[0064] The federated learning unit in the cloud optimization module is configured to receive a 128x128 pixel subset of the TEM image by a local client, train a neural network containing 5 layers of convolution layers, each layer applying a 3x3 convolution kernel and a ReLU activation function, and generate a model parameter vector; the cloud server receives model parameter vectors of 10 clients, calculates a global parameter vector through a weighted average algorithm, and stores and pushes the global parameter vector to the clients in a binary file format.

[0065] The TEM image intelligent analysis and measurement system further includes a quantum computing acceleration module configured to receive a data set containing 1 million TEM images, each image being 256x256 pixels, the module extracts features through a quantum circuit, the quantum circuit containing 20 quantum bits and 10 CNOT gates, outputs a feature matrix stored in a 1024x1024 dimension, and transmits the feature matrix to the data fusion and analysis module for subsequent processing.

[0066] The overall working principle is as follows:

[0067] TEM-Nanoscope is a TEM image intelligent analysis and critical dimension (CD) measurement software designed for the chip manufacturing field, aiming to achieve fast batch analysis, automatic measurement of critical dimensions, improve processing efficiency, reduce human error, and provide reliable data support for process optimization and defect analysis. The system integrates image acquisition, preprocessing, template generation, path planning, data fusion, cloud optimization, augmented reality interaction, and quantum computing acceleration modules, using cutting-edge AI technology, quantum computing, and extended functions to build an efficient, accurate, and highly scalable TEM image analysis platform. The system not only processes TEM images and related spectral data, automatically identifies complex structural features, and outputs critical dimensions and material composition, but also meets future chip manufacturing advanced needs through multi-modal fusion, environmental perception, real-time monitoring, and other extended capabilities.

[0068] System architecture and working principle

[0069] The core of TEM-Nanoscope consists of eight modules: image acquisition module, image preprocessing module, measurement template generation module, measurement path planning module, data fusion and analysis module, cloud optimization module, augmented reality (AR) interaction module, and quantum computing acceleration module. Each module works together, and data flows are sequentially transmitted between modules to form a complete analysis process. The system is also equipped with powerful extension capabilities through multi-modal extension, environmental perception enhancement, real-time monitoring, blockchain verification, quantum algorithm optimization, and user customization.

[0070] The system starts with the image acquisition module, which is configured to receive TEM images (512x512 or 256x256 pixels, formats such as.tif or.dm3) and corresponding energy dispersive spectroscopy and electron energy loss spectroscopy data (100-sample concentration list) in the chip manufacturing field. These data reflect the multi-layer structure and chemical composition of the chip. The module also has extension capabilities to collect environmental parameters in real time, including monitoring imaging conditions through sensor arrays (current sensor, voltage sensor, temperature sensor, humidity sensor, wind speed sensor, light sensor, pressure sensor, and gas composition analyzer) to generate an environmental parameter table (CSV format). The collected electrical characteristics (such as voltage and current) and environmental data are processed using association rule mining and clustering analysis algorithms to establish a prediction model of imaging quality and environment. The data is transmitted to the cloud through LoRa and NB-IoT wireless technology. Raw images and spectral data directly flow into the image preprocessing module, while environmental data is transmitted to the cloud optimization module for subsequent analysis.

[0071] The image preprocessing module enhances the TEM image, including a generative adversarial network (GAN) unit and a graph neural network (GNN) unit. The GAN unit consists of a generator (5-layer convolutional layer, 3x3 convolution kernel, stride 1, ReLU activation) and a discriminator (3-layer fully connected layer, 512-256-1 neurons, Sigmoid activation). The generator receives a 256x256 pixel TEM image and reconstructs boundary features (such as interlayer boundaries and hole edges). The discriminator verifies the consistency of the reconstructed image with the original image to ensure the quality of the repair. The GNN unit divides the image into 100x100 pixel sub-regions, extracts 5 feature points (based on gray gradient peak value) from each region, constructs a topology graph through 10 node layers, applies GAT (graph attention mechanism) to calculate the connection weight between features, and outputs a structured feature map (matrix form) containing 50 connection relationships. This process generates high-quality reconstructed images and structured feature data, which are transmitted to the metrology template generation module and the data fusion and analysis module, respectively, laying the foundation for subsequent metrology and defect detection. In the future, the preprocessing module can be extended to support scanning electron microscope (SEM) or atomic force microscope (AFM) data input, fused with TEM images, and cross-scale analysis can be achieved.

[0072] The metrology template generation module receives the preprocessed image data and generates a dynamic metrology template using a reinforcement learning unit. The unit uses the Q-learning algorithm, inputs parameters in the process rule database (layer thickness range 0.5-50 nm, feature size range 1-100 nm), and generates a template containing 20 measurement points (JSON format, each point including coordinates [x, y] and measurement direction) based on a 10x10 state-action table through 500 iterations to update the Q value table. The template reflects the requirements of the process rules for the measurement points and is transmitted to the metrology path planning module to optimize the execution order. As an extended function, the module can add a graphical interface, allowing users to define process rules (such as specifying a specific layer thickness or feature area) through drag-and-drop operations, generate personalized templates, and save them as a template library to improve user friendliness. The generated template and image data are fed back to the cloud optimization module for iterative optimization of the algorithm.

[0073] The metrology path planning module includes an A* algorithm unit that receives a 512x512 pixel grayscale matrix, calculates the gray gradient using the Sobel operator, and calculates the feature density using a 15x15 window. The gradient and density are used as weight parameters to generate a path sequence containing 15 priority measurement points (linked list format, each node including coordinates and weight values). The path preferentially covers high-risk areas (such as dense pattern areas or high-defect areas), and the optimized measurement point sequence and template data are transmitted to the data fusion and analysis module. In the future, the module can be connected with real-time TEM imaging equipment to plan the path while imaging, enabling dynamic metrology and being suitable for immediate adjustments during process development.

[0074] The data fusion and analysis module is the core analysis unit, containing a multi-modal convolutional neural network (CNN) composed of 8 convolutional layers (5x5 kernel, stride 2) and 4 pooling layers (2x2 max pooling). This module receives the boundary data of TEM images (256x256 pixel matrix), the elemental distribution data of EDS / EELS (100-point concentration list), and the sequence of measurement points, performs feature-level fusion of image features and spectral data, and outputs critical dimension values (such as layer thickness, aperture) and material composition distribution tables (including Al, Si, Ni, etc. element concentration, CSV format). The analysis results correlate size changes with chemical composition, providing a basis for defect analysis. The results are transmitted to the AR interaction module for user viewing and adjustment, and uploaded to the cloud optimization module for model improvement. In the expansion direction, this module can integrate SEM / AFM data to form a multi-modal cross-scale analysis capability, further revealing the physical and chemical properties of nano-scale structures.

[0075] The augmented reality (AR) interaction module receives 512x512 pixel data of TEM images through a display or AR glasses, superimposes measurement points and boundary contours in the form of red markers and green lines (rendered by OpenGL), supports user adjustment of marker positions through a stylus, and stores the adjusted coordinate data in XML format and feeds back to the data fusion and analysis module for recalculation. This module realizes human-computer collaboration, ensuring that the results meet user needs. Extended functions include interfacing with real-time TEM imaging equipment, supporting edge imaging and edge analysis, allowing users to dynamically adjust measurement points and view results in real time, suitable for process verification and debugging scenarios.

[0076] The cloud optimization module contains a federated learning unit, with local clients receiving 128x128 pixel subsets of TEM images, training a neural network containing 5 convolutional layers (3x3 kernel, ReLU activation), and generating a model parameter vector; the cloud server receives model parameter vectors from 10 clients, calculates a global parameter vector (in binary file format) through a weighted average algorithm, and pushes it back to the client to improve subsequent analysis accuracy. This mechanism protects data privacy while optimizing GAN, GNN, and CNN models. In the expansion direction, blockchain technology can be introduced to record the hash values of analysis results and model updates, generate a certificate of credibility, and ensure the traceability and authority of data and models, meeting industrial standard requirements. Environmental parameters (obtained from the image acquisition module) can also be used in the cloud to optimize imaging quality prediction models.

[0077] For very large-scale datasets (e.g., containing 1 million TEM images), the quantum computing acceleration module implements feature extraction through a quantum circuit (20 qubits, 10 CNOT gates), receives a 256x256 pixel image dataset, and outputs a 1024x1024 dimensional feature matrix, which is transmitted to the data fusion and analysis module for subsequent processing. This process significantly shortens the analysis time for large-scale tasks and is suitable for high-throughput requirements. In the future, the module can develop quantum convolution algorithms to directly replace traditional CNNs, improving the speed and accuracy of fusion analysis and further advancing the system to the next generation of computing architecture.

[0078] Module connections and data flow:

[0079] The data flow starts from the image acquisition module, with TEM images and EDS / EELS data flowing into the image preprocessing module. After GAN reconstruction and GNN topology analysis, enhanced images and structural feature maps are generated, which enter the measurement template generation module to form dynamic templates, and are then optimized by the measurement path planning module into a measurement path sequence. The data fusion and analysis module integrates image, path, and spectral data to output critical dimensions and composition distribution. The results are adjusted through the AR interaction module and uploaded to the cloud optimization module to iterate the model. For large-scale tasks, the quantum computing acceleration module intervenes to accelerate feature extraction and return the results. Each module communicates information through standard data formats (JSON, CSV, XML, binary files) to ensure seamless collaboration. Environmental parameters are directly transmitted from the acquisition module to the cloud as auxiliary input for model optimization.

[0080] The entire TEM-Nanoscope acquires high-quality TEM images and environmental data through the image acquisition module, enhances features through the image preprocessing module, and realizes automated measurement through the measurement template generation module and path planning module. The data fusion and analysis module integrates multi-source information to output results, the AR interaction module provides a user adjustment interface, the cloud optimization module continuously improves the model, and the quantum computing acceleration module supports large-scale tasks. Each module closely collaborates, with data flowing in an orderly manner, achieving the goals of rapid batch analysis, automatic CD measurement, efficient processing, and reliable data support. Through the expansion of multi-modalities (SEM / AFM fusion), environmental perception enhancement (multi-sensor and prediction model), real-time monitoring (imaging and analysis simultaneously), blockchain verification (data credibility), quantum algorithm optimization (quantum CNN), and user customization (graphical template design), the system not only meets current chip manufacturing needs but also has strong potential for future processes (such as 1 nm nodes). Specific embodiment two:

[0082] As shown in Figures 1-8 , the following details the key algorithms mentioned in embodiment one, including their core mathematical formulas and explanations:

[0083] Generative Adversarial Network (GAN):

[0084] Loss Function:

[0085]

[0086] where x: real TEM image (256x256 pixels, containing chip structure boundary), z: random noise vector (generated by normal distribution), G(z): reconstructed TEM image output by generator, D(x): probability of real image judged by discriminator, D(G(z)): probability of generated image judged by discriminator.

[0087] In TEM-Nanoscope, GAN is used in image preprocessing module. The generator is composed of 5 layers of convolutional layers (3x3 convolution kernel, stride 1, ReLU activation), which receives 256x256 pixel TEM images and reconstructs the blurred boundaries (such as interlayer edges, trench profiles) due to noise or imaging defects. The discriminator is composed of 3 layers of fully connected layers (512-256-1 neurons, Sigmoid activation), which verifies the consistency of the reconstructed image and the original image. The loss function is optimized by confrontation, so that the generator approximates the real image distribution.

[0088] GAN automatically repairs the image boundary and provides clear input to the subsequent module.

[0089] Usage:

[0090] Input: 256x256 pixel original TEM image (such as.tif format, noisy chip cross section).

[0091] Processing: The generator extracts features and reconstructs boundaries through 5 layers of convolution, and the discriminator compares the reconstructed image with the real image, and iteratively optimizes the loss function.

[0092] Output: 256x256 pixel image after reconstruction, with improved boundary clarity.

[0093] Graph Neural Network (GNN):

[0094] Message Passing Update:

[0095]

[0096] where Feature vector of node v at layer l (TEM image feature point); Neighbor set of node v (adjacent feature points); α vu Attention weight (GAT calculation); W (l) , b (l) Weight matrix and bias at layer l; σ: activation function (ReLU).

[0097] GNN is used for image preprocessing module to segment TEM images into 100x100 pixel sub-regions, extract 5 key feature points (such as boundary inflection points, defect points) from each region, and construct a topological graph through 10 layers of message passing. GAT mechanism calculates the connection strength between feature points, and outputs a 50-relationship structure feature map (matrix form) reflecting the topological characteristics of chip structure.

[0098] GNN automatically extracts the topological relationship between features and labels the potential defect area, solving the problem of difficult manual analysis of multi-layer complex structure (such as trenches, holes) of chip TEM images and low efficiency of defect (such as interlayer misalignment, dense area fracture) recognition.

[0099] Q-learning:

[0100] Q value update:

[0101]

[0102] Where s: current state (process rule parameters, such as layer thickness 0.5-50nm); a: action (selecting measurement point coordinates); r: reward (based on point accuracy, e.g. inverse of deviation); a: learning rate (set to 0.1); g: discount factor (set to 0.9); s ′ : next state (updated point distribution).

[0103] In the measurement template generation module, Q-learning generates a 20-point dynamic template based on the process rule database (layer thickness, feature size range 1-100nm). The state is the current point distribution, the action is to select the point in the 10x10 grid, the reward measures the consistency of the point with the process requirements, and the Q table is optimized through 500 iterations.

[0104] Solves the problem of time-consuming manual definition of TEM image measurement points and inconsistency caused by differences in engineers' experience, making it difficult to adapt to different chip processes. The template generation time is shortened from several hours to seconds, the point consistency is improved from 80% to 99%, and accurate input is provided for path planning.

[0105] A * algorithm:

[0106] Cost function:

[0107] f(n) = g(n) + h(n)

[0108] Where f(n): total cost of node n, g(n): path cost from start to n (gray scale gradient accumulation, Sobel operator calculation), h(n): heuristic estimate from n to target (feature density, 15x15 window statistics).

[0109] In the metrology path planning module, the A* algorithm receives a 512x512 pixel grayscale matrix, optimizes a 15-point priority path from a 20-point template based on grayscale gradients (reflecting boundary changes) and feature density (reflecting regional complexity), focusing on high-risk areas,

[0110] Solves the problem of wasting time with random measurement sequence, not prioritizing dense patterns or high-defect areas, and reducing batch analysis efficiency. The planning time is reduced from minutes to 1 second, the coverage rate of high-risk areas is increased from 50% to 90%, and the batch processing efficiency is improved.

[0111] Multi-modal convolutional neural network:

[0112] Convolution operation:

[0113]

[0114] y[i,j]: feature map output.

[0115] x: input TEM image (256x256) or EDS / EELS data.

[0116] w: 5x5 convolution kernel, stride 2.

[0117] b: bias.

[0118] Pooling operation:

[0119]

[0120] 2x2 max pooling.

[0121] In the data fusion and analysis module, CNN (8 layers of convolution + 4 layers of pooling) fuses TEM image boundary features and EDS / EELS spectral data (100-point concentration list), extracts spatial and chemical information, and outputs key dimensions and composition table.

[0122] Solves the problem of traditional analysis only measuring dimensions, unable to correlate size changes with material composition.

[0123] Quantum computing acceleration:

[0124] Quantum state encoding:

[0125]

[0126] where |ψ>: 20-qubit encoding of TEM image features, a i : normalized amplitude of pixel value.

[0127] cnor operation:

[0128]

[0129] c: control bits, t: target bits.

[0130] In the quantum computing acceleration module, 20 qubits and 10 CNOT gates are used to process 256x256 TEM images at the level of millions in parallel, and after feature extraction, a 1024x1024 matrix is output.

[0131] Federated learning:

[0132] Global parameter update:

[0133]

[0134] where w global : global model parameters, w k : the local parameters of the kth client (5-layer CNN training), K: the number of clients (set to 10).

[0135] In the cloud optimization module, federated learning updates the local model through 10 clients (each client trains a 5-layer CNN with a 3x3 kernel and ReLU activation), and the server generates global parameters by weighted averaging, optimizing GAN, GNN, etc.

[0136] Solves the problem that a single data set trained model cannot adapt to different Fab environments and data privacy restrictions on uploading. Specific embodiment three:

[0138] As Figures 1-8 shown below is a detailed hardware composition and hardware description of each module in embodiment one:

[0139] The image acquisition module is the core of data input, which generates 512x512 or 256x256 pixel TEM images (.tif or.dm3 format) to capture the cross-sectional structure of the chip such as trenches and holes by using FEI Tecnai G2 F20 transmission electron microscope (with 2048x2048 pixel CCD camera, resolution 0.24 nm, frame rate 30 fps), and synchronously collects chemical composition data by Oxford Instruments X-Max 80 TEDS detector (energy resolution 130 eV, 100 points / s acquisition) and Gatan Enfinium EREELS system (energy resolution 0.8 eV) to provide element distribution of Al, Si, Ni, etc. To enhance environmental adaptability, a sensor array is equipped, including DHT22 temperature and humidity sensor (accuracy ±0.5℃, ±2% RH), TSL2561 light sensor (0.1-40000 Lux), MPX5700AP pressure sensor (15-700 kPa), MQ-135 gas sensor (detecting CO2, etc.), anemometer (±0.1 m / s), ACS712 current / voltage sensor (±5A / 30V) to monitor the imaging conditions in real time and generate an environmental parameter table in CSV format. Raspberry Pi 4 Model B (4-core Cortex-A72, 8GB RAM, 64GB microSD storage) is used as an embedded controller to integrate sensor data through GPIO interface, run association rule mining and clustering analysis algorithms, and establish an imaging quality and environmental prediction model. Semtech SX1276 LoRa module (transmission distance 10 km, 50 kbps) and Huawei NB-IoT module (250 kbps, 10 mW power consumption) are used to wirelessly transmit environmental data to the cloud, and TEM images and spectral data are sent to the image preprocessing module through the USB 3.0 interface to solve the problem of traditional single acquisition and provide comprehensive input.

[0140] The image preprocessing module enhances the quality of TEM images. A NVIDIA RTX3090 GPU (24GB GDDR6X memory, 10496 CUDA cores, 35.6 TFLOPS) is used to execute the generative adversarial network (GAN) and graph neural network (GNN) algorithms, and a host server (Intel Xeon Gold 6226R, 16 cores and 32 threads, 2.9 GHz, 128 GB DDR4 RAM, 2 TB NVMe SSD) is used to support the algorithms. The generator (5 layers of convolutional layers, 3x3 kernel, stride 1, ReLU activation) and the discriminator (3 layers of fully connected layers, 512-256-1 neurons, Sigmoid activation) of the GAN process 256x256 pixel TEM images, reconstruct boundary features, and the GNN divides the images into 100x100 pixel sub-regions, extracts 5 feature points, and constructs a topological graph with 50 connection relationships. The high parallel computing capability of the GPU supports fast iterative optimization (such as the GAN loss function), and the Xeon CPU and SSD ensure data loading and storage efficiency. The reconstructed images and feature maps are transmitted to the metrology template generation module and the data fusion and analysis module through the PCIe 4.0 channel, solving the problems of image noise and low efficiency of topological analysis.

[0141] The metrology template generation module automatically generates metrology templates. The hardware includes a NVIDIA Jetson AGX Xavier (8-core ARMv8.2 CPU, 512-core Volta GPU, 32 GB LPDDR4x RAM, 32 GB eMMC storage) that runs a Q-learning reinforcement learning algorithm. The Jetson AGX receives reconstructed images and a process rule database (layer thickness 0.5-50 nm, feature size 1-100 nm), updates a 10x10 Q table through 500 iterations, and generates a 20-point template (JSON format). The 512-core GPU accelerates Q value calculation, the ARM CPU handles logic control, and the eMMC stores the template library. The template is transmitted to the metrology path planning module through gigabit Ethernet and fed back to the cloud optimization module, solving the problem of time-consuming manual point definition and improving automation efficiency.

[0142] The measurement path planning module optimizes the measurement sequence. The hardware uses AMD Ryzen 95950X (16 cores, 32 threads, 3.4 GHz, 64 MB cache) combined with NVIDIA GTX 1660 Super (6 GB GDDR6, 1408 CUDA cores, 8.9 TFLOPS) to run the A* algorithm. The 512x512 grayscale matrix is received, the gradient is calculated by the Sobel operator on the Ryzen CPU, the GTX 1660 accelerates the 15x15 window feature density statistics, and a 15-point path sequence (linked list format) is generated. The CPU efficiently processes path iteration, and the GPU optimizes the cost function in parallel. The path data is transmitted to the data fusion and analysis module via PCIe, solving the problem of low efficiency of random measurement and prioritizing coverage of high-risk areas.

[0143] The data fusion and analysis module integrates multi-source data. The hardware is a dual NVIDIA A100 GPU (40 GB HBM3 memory, 6912 CUDA cores, 19.5 TFLOPS) and a host server (AMD EPYC 7543P, 32 cores, 64 threads, 2.8 GHz, 256 GB DDR4 RAM, 4 TB NVMe SSD). The dual A100 runs a multi-modal CNN (8 layers of convolution, 5x5 kernel, stride 2; 4 layers of pooling, 2x2 max pooling) to fuse 256x256 boundary matrices and 100-point spectral data. The EPYC CPU manages data flow, and the SSD stores the output CSV table (size + composition). The results are transmitted to the AR interaction module and the cloud optimization module via high-speed NVLink, solving the limitations of single analysis and supporting process optimization.

[0144] The cloud optimization module continuously improves the model. The hardware includes a cloud server cluster (4 Dell PowerEdge R7525, each equipped with 2 AMD EPYC 7763, 64 cores, 128 threads, 2.45 GHz, 512 GB RAM, 10 TB HDD) and edge nodes (10 NVIDIA Jetson Nano, 4-core ARM A57, 128 Maxwell cores, 4 GB RAM). The edge nodes train a local 5-layer CNN to generate parameter vectors, and the cloud server receives 10 client parameters via gigabit Ethernet, generates a global model (Binary format) by weighted averaging, and stores the model and blockchain hash on the HDD. The model is pushed back to the edge node, solving the problems of privacy and model adaptability.

[0145] Augmented Reality (AR) interaction module realizes human-computer collaboration, hardware is Microsoft HoloLens 2 (Qualcomm Snapdragon 850, 4GB RAM, 64GB storage, field of view 52°) and workstation (Intel Core i9-12900K, 16 cores 24 threads, 3.2GHz, 32GB RAM, NVIDIA RTX 3060, 12GB GDDR6). HoloLens 2 receives 512x512 images, renders measurement points and contours through OpenGL, users adjust to generate XML coordinates, workstation RTX 3060 accelerates rendering, i9 CPU processes real-time feedback, data is transmitted to data fusion and analysis module through Wi-Fi, solving the problem of lack of interactivity in static analysis.

[0146] Quantum computing acceleration module processes large-scale data, hardware uses IBM Quantum System One (20 qubits, quantum volume 32, gate operation time 100ns) and preprocessing server (Intel Xeon Silver 4210R, 10 cores 20 threads, 2.4GHz, 64GB RAM, 1TB SSD). Receive million-level 256x256 images, Xeon CPU pre-encode data, quantum system extracts features through 20 qubits and 10 CNOT gates, output 1024x1024 matrix, transmitted to data fusion and analysis module through Ethernet, solving high-throughput analysis bottleneck, efficiency improved 50 times. Specific embodiment four:

[0148] As shown in Figures 1-8 , wherein Figure 3 is a functional comparison table of TEM-Nanoscope and traditional software, which proves its speed, automation and reliability, and simply shows the advantages of TEM-Nanoscope in multi-source acquisition, AI automation, path optimization and scalability, Figure 4 to show the data flow and format between modules, and prove the efficiency of system cooperation, Figure 5 to prove the benefits of quantitative expansion function, and prove the foresight of the system. Through comparison, data flow and benefit analysis, the superiority of TEM-Nanoscope is verified from multiple angles, Figure 6 Q-learning generates 20 measurement points through 10x10 Q table iteration 500 times, reward is based on feature map intensity, and automatic template generation is embodied, Figure 7 A* algorithm optimizes 15-point path combining gradient and density, simplifies heuristic function to distance and density weighted, and embodies fast path planning, Figure 8 to simulate user adjustment for random offset of path points, show interactive results, and embody human-computer collaboration.

[0149] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it is intended to be limited only by the words of the patent claims. A reference to an aspect of the present application employing a particular aspect, feature or structure of the described embodiments is not to be interpreted as an indication that all or even any aspects of the present application have such feature or structure in some way.

[0150] While the embodiments of the application have been shown and described herein, it is to be understood that the application is not limited to these embodiments. Rather, many modifications, changes, substitutions, and alterations can be made to the embodiments of the application without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.

Claims

1. A method for fast batch analysis of TEM images and critical dimension measurement by nanoscope software, comprising building a TEM image intelligent analysis and measurement system, characterized in that: The TEM image intelligent analysis and measurement system comprises an image acquisition module, an image preprocessing module, a measurement template generation module, a measurement path planning module, a data fusion and analysis module, and a cloud optimization module. The specific settings of the modules in the TEM image intelligent analysis and measurement system are as follows: The image acquisition module is configured to receive TEM images and corresponding energy dispersive spectroscopy and electron energy loss spectroscopy data in the field of chip manufacturing. The image preprocessing module comprises a generative adversarial network unit and a graph neural network unit, wherein the generative adversarial network unit comprises a generator and a discriminator, the generator is composed of 5 convolutional layers for reconstructing boundary features in the TEM image, and the discriminator is composed of 3 fully connected layers for verifying the consistency of the reconstructed boundary and the original image; the graph neural network unit comprises 10 node layers for extracting topological connection relationships between key features in the TEM image. The measurement template generation module comprises a reinforcement learning unit driven by a Q-learning algorithm, and the input parameters include layer thickness values and feature size ranges in a process rule database, and the output is a dynamic measurement template comprising at least 20 measurement points. The measurement path planning module comprises an A algorithm unit, which generates a path sequence comprising 15 priority measurement points by taking image pixel gray gradient and feature density as weight parameters. The data fusion and analysis module comprises a multi-modal convolutional neural network composed of 8 convolutional layers and 4 pooling layers, which performs feature-level fusion of boundary data of the TEM image and element distribution data of EDS / EELS, and outputs key size values and material composition distribution tables. The cloud optimization module comprises a federated learning unit composed of a local client and a cloud server, the local client is configured to train a neural network model comprising 5 convolutional layers, and the cloud server is configured to receive model parameter updates of 10 clients and generate a global model by weighted averaging.

2. The method according to claim 1, wherein the method is characterized by: The generative adversarial network unit in the image preprocessing module is configured as follows: the generator receives a 256x256 pixel TEM image input, applies a 3x3 convolution kernel, a convolution operation with a stride of 1, and a ReLU activation function in turn through 5 convolutional layers, and outputs a reconstructed boundary image; the discriminator receives the reconstructed image and the original image, applies 512, 256, and 1 neurons and a Sigmoid activation function in turn through 3 fully connected layers, and outputs a binary classification result for verifying the matching degree of the reconstructed boundary and the original image.

3. The method according to claim 1, wherein the method is characterized by: The graph neural network unit in the image preprocessing module is configured as follows: the TEM image is divided into 100x100 pixel sub-regions, 5 key feature points are extracted from each sub-region, the graph neural network unit constructs a topological graph through 10 node layers, each layer of nodes applies GAT to calculate adjacency feature weights, and outputs a structure feature map comprising 50 connection relationships, the feature map is stored in a matrix form, and each row represents the coordinates and connection strength of a feature point.

4. The method for fast batch analysis of TEM images and critical dimension measurement using nanoscope software according to claim 1, wherein: The reinforcement learning unit in the metrology template generation module is configured to input parameters in the process rule database, including layer thickness range 0.5-50 nm and feature size range 1-100 nm, the Q-learning algorithm is based on a 10×10 state-action table, the Q value table is updated through 500 iterations, a dynamic template containing 20 measurement points is generated, and the template is stored in JSON format, each point including coordinate values and measurement direction.

5. The method for fast batch analysis of TEM images and critical dimension measurement using nanoscope software according to claim 1, wherein: The A* algorithm unit in the metrology path planning module is configured to receive a 512×512 pixel grayscale matrix of the TEM image, calculate the grayscale gradient value and feature density value of each pixel point, the grayscale gradient is extracted by the Sobel operator, the feature density is calculated by pixel statistics of a 15×15 window, a path sequence containing 15 priority measurement points is generated, and the path sequence is stored in a linked list form, each node including coordinates and weight values.

6. The method for fast batch analysis of TEM images and critical dimension measurement using nanoscope software according to claim 1, wherein: The multi-modal convolutional neural network in the data fusion and analysis module is configured to receive boundary data of the TEM image and element distribution data of EDS / EELS, the boundary data is a 256×256 pixel matrix, and the element distribution data is a concentration value list of 100 sampling points; the multi-modal convolutional neural network applies a 5×5 convolution kernel and a convolution operation with a stride of 2 through 8 convolution layers, applies a 2×2 maximum pooling operation through 4 pooling layers, and outputs a distribution table containing key size values and Al, Si, and Ni element concentrations, the distribution table is stored in CSV format.

7. The method of claim 1, wherein the method is capable of rapidly batch analyzing TEM images and measuring critical dimensions using nanoscope software. The TEM image intelligent analysis and measurement system further includes an augmented reality interaction module configured to receive 512×512 pixel data of the TEM image through a display and AR glasses, superimpose measurement points and boundary contours in the form of red markers and green lines, the markers and lines are generated by OpenGL rendering, support users to adjust the marker position through a stylus, and the adjusted coordinate data is stored in XML format and transmitted to the data fusion and analysis module.

8. The method for fast batch analysis of TEM images and critical dimension measurement using nanoscope software according to claim 1, wherein: The federated learning unit in the cloud optimization module is configured to receive a 128×128 pixel subset of the TEM image by a local client, train a neural network containing 5 convolution layers, each layer applying a 3×3 convolution kernel and a ReLU activation function, and generate a model parameter vector; the cloud server receives model parameter vectors of 10 clients, calculates a global parameter vector through a weighted average algorithm, and stores the global parameter vector in a binary file form and pushes it to the client.

9. The method according to claim 8, wherein the method is characterized by: The TEM image intelligent analysis and measurement system further includes a quantum computing acceleration module configured to receive a data set containing 1 million TEM images, each image being 256×256 pixels, the quantum computing acceleration module realizes feature extraction through a quantum circuit, the quantum circuit contains 20 quantum bits and 10 CNOT gates, outputs a feature matrix stored in 1024×1024 dimensions, and transmits it to the data fusion and analysis module for subsequent processing.

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