Overlay error compensation precision measurement system and method

Abstract

The application relates to the field of precision machining, and discloses a measurement system for overlay error compensation precision, which comprises a multi-modal sensor array directly installed near the working area of an overlay machine, and comprises a laser displacement sensor with a resolution of 0.01 microns, a thermocouple temperature sensor with a precision of + / -0.5 DEG C, and an accelerometer with a sensitivity of 0.001 g, which are used for monitoring displacement, temperature and vibration data in real time during the overlay process. The application can accurately capture the slight changes in the overlay process by monitoring the displacement, temperature and vibration data in real time through the multi-modal sensor array directly installed near the working area of the overlay machine, and adjusting the measurement parameters of the laser displacement sensor through the ambient light intensity sensor. The real-time and accurate monitoring and adjustment of the data can effectively compensate for the errors in the overlay process, and significantly improve the precision of the overlay product and the consistency of the production process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of precision machining, in particular to a measurement system and method for overlay error compensation accuracy. BACKGROUND

[0002] As a precision machining method, the quality control of overlay technology is crucial to ensure the accuracy and consistency of the final product. In the traditional overlay process, various errors often occur due to equipment aging, improper operation, environmental changes and other factors. Although these errors can be corrected to some extent through post-detection and manual adjustment, these methods are often time-consuming and labor-intensive, and cannot achieve real-time error compensation, thereby affecting production efficiency and product quality.

[0003] In the prior art, although some basic monitoring and control systems have been introduced to improve the overlay process, these systems are mostly limited to single type of sensor data, lacking comprehensive monitoring and analysis of various changing factors in the overlay process. In addition, these systems lack adaptive adjustment capability, and cannot dynamically optimize overlay parameters according to real-time feedback, making it difficult to meet the increasingly stringent overlay accuracy requirements. At the same time, the existing solutions are relatively rough in user interaction design, and do not provide sufficient information support for operators to deeply understand the overlay process, let alone effectively adjust according to system feedback.

[0004] Therefore, the present application proposes a measurement system and method for overlay error compensation accuracy to solve the problems of the prior art. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a measurement system and method for overlay error compensation accuracy, which solves the problems of insufficient real-time error monitoring, limited adaptive adjustment capability and poor user interaction experience in the traditional overlay process.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a measurement system for overlay error compensation accuracy, comprising:

[0007] A multi-modal sensor array is directly installed near the working area of the overlay machine, including a laser displacement sensor with a resolution of 0.01 microns, a thermocouple temperature sensor with an accuracy of ±0.5℃, and an accelerometer with a sensitivity of 0.001g, for real-time monitoring of displacement, temperature and vibration data during the overlay process;

[0008] A data fusion processing unit is built-in with a microcontroller and dedicated software, which integrates and optimizes the data from the multi-modal sensor array using weighted average and Kalman filtering algorithm;

[0009] An adaptive error compensation module integrates a PID controller to receive the output of the data fusion processing unit and dynamically adjust overlay parameters to compensate for errors.

[0010] An interactive user interface equipped with a high-definition touch screen displays overlay error analysis results and compensation suggestions and receives user input for overlay parameter adjustments.

[0011] An error prediction and compensation strategy generator utilizes an embedded deep neural network processor to execute LSTM-based machine learning algorithms, analyze historical and current data to predict future overlay errors, and generate corresponding compensation strategies.

[0012] Preferably, the multi-modal sensor array further includes an ambient light intensity sensor to monitor changes in the working environment light and adjust the measurement parameters of the laser displacement sensor to eliminate the impact of ambient light on displacement measurement accuracy.

[0013] Preferably, the data fusion processing unit dynamically adjusts the weight parameters of the weighted average and Kalman filtering algorithms according to changes in real-time data streams to adapt to the characteristics of different overlay environments and materials.

[0014] Preferably, the PID controller of the adaptive error compensation module uses a self-adjusting algorithm to adjust its control parameters based on error feedback from consecutive overlay processes to achieve more accurate dynamic error compensation.

[0015] Preferably, the LSTM model used by the error prediction and compensation strategy generator contains pre-trained data sets specific to material types and overlay processes to enhance its prediction accuracy and adaptability.

[0016] Preferably, a measurement system for overlay error compensation accuracy includes the following steps:

[0017] Real-time synchronous acquisition of displacement, temperature, vibration, and ambient light intensity data during the overlay process;

[0018] Running weighted average and Kalman filtering algorithms to integrate and optimize collected data;

[0019] Based on the optimized data, the adaptive error compensation module dynamically adjusts overlay parameters through the PID controller;

[0020] Using an LSTM model to predict future overlay errors and generating compensation strategies based on the prediction results;

[0021] The interactive user interface displays overlay error analysis results and compensation suggestions and receives user fine-tuning inputs.

[0022] Preferably, the measurement method of the overlay error compensation precision measurement system further comprises automatically adjusting the weight parameters of the data fusion algorithm according to the volatility of the real-time collected data to ensure the accuracy of data optimization under different overlay conditions.

[0023] Preferably, the adaptive error compensation module adopts a reinforcement learning strategy to automatically learn the optimal PID parameter adjustment strategy to adapt to new error patterns that occur in long-term overlay processes.

[0024] Preferably, the error prediction and compensation strategy generator periodically receives model updates from the cloud platform to incorporate new overlay data and error patterns, maintaining the timeliness and accuracy of the prediction model.

[0025] Preferably, the measurement method of the overlay error compensation precision measurement system further comprises providing customized overlay parameter suggestions using an interactive user interface, allowing users to select different preset parameter configurations according to specific overlay tasks to optimize the overlay process.

[0026] The present application provides an overlay error compensation precision measurement system and method. It has the following beneficial effects:

[0027] 1. The present application can accurately capture small changes in the overlay process by directly installing a multi-modal sensor array near the working area of the overlay machine to monitor displacement, temperature and vibration data in real time, and adjusting the measurement parameters of the laser displacement sensor through the ambient light intensity sensor. Real-time and accurate monitoring and adjustment of these data can effectively compensate for errors in the overlay process, significantly improving the precision of the overlay product and the consistency of the production process.

[0028] 2. The present application allows the system to dynamically adjust the overlay parameters according to the changes in the real-time data stream and the error feedback of the continuous overlay process through the design of the data fusion processing unit and the adaptive error compensation module. This adaptive capability enables the system to automatically learn and adjust to the optimal operating parameters to adapt to different overlay environments and material characteristics, thereby maintaining excellent overlay results under different conditions.

[0029] 3. The overlay error compensation precision measurement system provided by the present application uses the LSTM machine learning algorithm of the error prediction and compensation strategy generator, combined with pre-trained data sets specific to material types and overlay processes, to accurately predict future overlay errors. By periodically receiving model updates from the cloud platform, the system can incorporate new overlay data and error patterns, continuously optimize the compensation strategy, and ensure high efficiency and accuracy in long-term operation.

[0030] 4、The present application not only displays overlay error analysis results and compensation suggestions through an interactive user interface, but also allows users to manually fine-tune overlay parameters according to specific overlay tasks. In addition, the interface also provides customized overlay parameter suggestions and visualization of real-time and historical overlay process data, enhancing user operation experience and system transparency, enabling users to better understand the overlay process and make more accurate adjustments.

[0031] 5、The present application automatically learns the optimal PID parameter adjustment strategy by adopting reinforcement learning strategy, and periodically updates the prediction model through cloud platform support. The system not only adapts to new error patterns that appear in long-term overlay processes, but also continuously adapts to new materials and new process requirements that may appear in the future. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 Flowchart of the measurement method for overlay error compensation accuracy. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] Embodiment:

[0035] Please refer to the drawings of the present application Figure 1 :

[0036] Embodiment 1: Standard environment overlay accuracy improvement system

[0037] Title: Intelligent overlay error compensation system for standard working conditions

[0038] Steps:

[0039] In a standardized working environment, a high-precision multi-modal sensor array is deployed close to the overlay machine to capture real-time key displacement, temperature and vibration data.

[0040] The data fusion processing unit is equipped with advanced microprocessors and customized software, which efficiently integrates sensor data through fine weighted average and Kalman filtering technology, ensuring accurate information transmission.

[0041] The adaptive error compensation module flexibly receives the output signal of the data processing unit through integrated PID control technology, instantly adjusts the overlay process parameters, and optimizes error compensation.

[0042] An interactive user interface with a clear touch screen display presents real-time error analysis and compensation suggestions, along with intuitive options for operational adjustments.

[0043] An error prediction and compensation strategy generator using deep learning algorithms, based on the classic LSTM network structure, accurately predicts future overlay errors by analyzing accumulated historical and real-time data.

[0044] Summary of the embodiment: This embodiment is suitable for deployment in standardized production environments, and through the integration of high-precision monitoring and intelligent data processing technology, it realizes real-time error compensation in the overlay process, significantly improving product quality and production efficiency.

[0045] Example 2: Overlay error compensation under high temperature conditions

[0046] Title: Intelligent control system for overlay precision in high temperature environment

[0047] Steps:

[0048] Under the working conditions of high temperature challenges, a specially configured sensor array implements strict monitoring to ensure accurate data collection and transmission.

[0049] The data fusion processing unit dynamically adapts to the high temperature environment using advanced algorithms to ensure the accuracy and timeliness of data analysis.

[0050] The adaptive error compensation module adopts innovative control strategies to accurately adjust overlay parameters to resist the impact of high temperature.

[0051] The user interface is optimized for high temperature working environment, providing easy-to-understand operation guidelines and suggestions to enhance user interaction experience.

[0052] The error prediction and compensation strategy generator is optimized for high temperature environment to ensure the efficiency and adaptability of prediction.

[0053] Summary of the embodiment: Designed for high temperature environment, this system ensures the stability and accuracy of the overlay process through environmental adaptability technology and high temperature optimization model, effectively improving the production capacity under extreme conditions.

[0054] Example 3: Overlay precision optimization scheme for complex patterns

[0055] Title: High-precision control system for complex pattern overlay process

[0056] Steps:

[0057] For complex pattern overlay tasks, the system automatically increases the density and frequency of data collection to capture detailed changes.

[0058] The data processing unit analyzes the specific needs of complex pattern overlay through advanced algorithms, optimizing the data integration process.

[0059] The adaptive error compensation module dynamically adjusts overlay parameters based on the characteristics of complex patterns, ensuring accurate reproduction of patterns.

[0060] The interactive interface provides users with professional analysis and operation guidance for complex pattern overlay, enhancing the accuracy and convenience of operations.

[0061] The error prediction module conducts deep learning on the characteristics of complex patterns, accurately predicting and developing compensation strategies.

[0062] Summary of the embodiment: The system designed for complex pattern overlay achieves high precision and high-quality output of pattern overlay through intelligent data processing and deep learning prediction.

[0063] Embodiment 4: Overlay error compensation system for long-term continuous operation

[0064] Title: Intelligent error management system for continuous operation overlay process

[0065] Steps:

[0066] For long-term continuous overlay operation, the system adds an environmental monitoring module to adjust the sensitivity and response range of the sensor array in real time to cope with changes in environmental and material conditions during continuous operation.

[0067] The data fusion processing unit uses advanced time series analysis techniques to continuously adjust and optimize data processing strategies, ensuring the accuracy and consistency of data over long periods.

[0068] The adaptive error compensation module uses complex feedback control logic combined with long-term operation data analysis results to implement fine dynamic adjustments, ensuring the stability and precision of continuous operation.

[0069] The interactive user interface provides real-time status monitoring, early warning and dynamic adjustment suggestions for operators during long-term operation, enhancing the real-time and predictive nature of operations.

[0070] The error prediction and compensation strategy generator uses big data and long-term sequence analysis to optimize the LSTM model, improving the prediction accuracy of long-term continuous operation error trends and achieving efficient forward-looking error management.

[0071] Summary of the embodiment: The system designed for long-term continuous overlay operation achieves timely and accurate error compensation through continuous environmental monitoring, intelligent data processing and prediction, ensuring the stability of the production process and the high-quality output of products, greatly improving the economic efficiency and production safety of continuous operation.

[0072] Embodiment 5: Cloud computing supported overlay error intelligent compensation system

[0073] Title: Cloud computing enhanced overlay precision control and optimization system

[0074] Steps:

[0075] The system seamlessly connects the overlay machine with the cloud computing platform, uploads the multi-modal data collected during the overlay process to the cloud in real time, and uses the powerful processing capability of cloud computing for deep data analysis.

[0076] According to the big data analysis results, the cloud platform automatically issues the optimized data fusion algorithm and compensation strategy to the local processing unit, realizing the real-time update and optimization of data processing logic.

[0077] Using cloud resources to update the control strategy of the adaptive error compensation module in real time ensures the optimal adjustment of the overlay parameters and adapts to the changing production conditions.

[0078] The user interface synchronizes the analysis results and compensation suggestions of the overlay process in real time through cloud services, and provides customized overlay optimization solutions supported by cloud big data.

[0079] The error prediction module regularly receives the latest machine learning models and algorithm updates through the cloud platform, maintaining the leading and adaptability of prediction accuracy.

[0080] Embodiment summary: This embodiment realizes the intelligent upgrade of the overlay error compensation system through the introduction of cloud computing, not only improves the efficiency and accuracy of data processing and error compensation, but also provides personalized overlay solutions based on big data analysis for users, significantly improves the intelligence and automation level of the overlay process, and opens a new chapter in overlay technology.

[0081] Summary:

[0082] Through the above five carefully designed embodiments, the invention fully demonstrates its strong adaptability and innovation in different application scenarios. Whether it is a standard environment, special conditions (such as high temperature environment), complex tasks (such as complex pattern overlay), long continuous operation, or advanced data analysis and error compensation using cloud computing platform, the invention can provide accurate, efficient and user-friendly solutions. Through the integration of high-precision multi-modal sensing technology, intelligent data processing and fusion, advanced error compensation mechanism, cloud computing and deep learning algorithm, the invention not only significantly improves the overlay precision and production efficiency, but also greatly optimizes the operation experience, promotes the development of overlay technology towards intelligence, automation and individualization, and meets the demand for high quality and high efficiency production in modern precision machining field.

[0083] Comparative experiment:

[0084] Contrast Experiment 1: Real-time monitoring accuracy comparison

[0085] Objective: To compare the differences in real-time monitoring of displacement, temperature and vibration data accuracy between the invention and existing technology.

[0086] Existing technology solution: Traditional overlay machines only install a single type of sensor (usually displacement sensor), without temperature and vibration data monitoring. The resolution of displacement sensor is generally 0.1 microns.

[0087] Invention technology solution: Multimodal sensor array, including 0.01 micron resolution laser displacement sensor, ±0.5℃ accuracy thermocouple temperature sensor and 0.001g sensitivity accelerometer.

[0088] Experiment design:

[0089] Select the same material and pattern for overlay operation, keeping other conditions (such as ambient temperature, machine status) the same.

[0090] Record displacement, temperature (if applicable) and vibration data of both technology solutions within the same time.

[0091] Compare the accuracy and monitoring range of the data.

[0092] Experiment data table:

[0093]

[0094] Comparison summary: The multimodal sensor array provided by the invention can capture small changes in the overlay process more accurately, especially in temperature and vibration monitoring, which cannot be achieved by traditional technology. In addition, high-resolution displacement monitoring provides reliable data support for accurate compensation of overlay errors.

[0095] Contrast Experiment 2: Adaptive error compensation effect comparison

[0096] Objective: To compare the performance of adaptive error compensation between the invention and existing technology.

[0097] Existing technology solution: Use a fixed parameter PID controller for error compensation, which cannot automatically adjust overlay parameters according to real-time data.

[0098] Invention technology solution: PID controller integrated with self-adjusting algorithm, which can dynamically adjust overlay parameters according to real-time data provided by multimodal sensor array.

[0099] Experiment design:

[0100] Run both technology solutions under the same overlay task (including complex patterns).

[0101] Record and compare the error rate and consistency of the products after overlay completion.

[0102] Analyze the reaction speed and effect of the two technical solutions on overlay parameter adjustment.

[0103] Experimental data table:

[0104]

[0105] Comparison and summary: By adaptively adjusting overlay parameters, the invention significantly reduces error rate and improves product consistency. Compared with existing technology, the invention has faster reaction speed, more accurate adjustment, and higher production efficiency and product quality.

[0106] Comparison experiment 3: stability and adaptability of long-term operation

[0107] Objective: To compare the stability and ability to adapt to new error patterns of the invention and existing technology in long-term operation.

[0108] Existing technical solution: Fixed error compensation strategy is used in long-term operation, which cannot adapt to new error patterns.

[0109] Technical solution of the invention: Adopt reinforcement learning strategy and model update supported by cloud platform to continuously optimize error compensation strategy.

[0110] Experimental design:

[0111] Run the overlay machines processed by the two technical solutions for 100 hours continuously.

[0112] Record and compare the error trend and adaptability of the compensation strategy of the two solutions.

[0113] Evaluate the product quality consistency after long-term operation.

[0114] Experimental data table:

[0115]

[0116] Comparison and summary: The invention shows excellent long-term operation stability and rapid adaptability to new error patterns. In contrast, the existing technology cannot effectively deal with new error patterns in long-term operation, resulting in a decline in product quality.

[0117] Summary

[0118] Through the above-mentioned comparative experiments, the application has significant advantages compared with the prior art in real-time monitoring accuracy, self-adaptive error compensation efficiency, and long-term running stability and adaptability. The experimental data clearly proves the beneficial effects of the application in improving overlay accuracy, production efficiency and product consistency, especially the advancement in adapting to complex production environment and long-term stable operation, providing an efficient, intelligent and reliable technical solution for the field of precision overlay.

[0119] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Modifications can be made by those skilled in the art, particularly in light of the foregoing teachings, without departing from the spirit of the intended application. The scope of the present application solely defined by the claims appended hereto.

Claims

1. A system for measuring overlay error compensation accuracy, comprising: Comprising: A multi-modal sensor array, directly installed near the working area of the lithography machine, including laser displacement sensors with a resolution of 0.01 microns, thermocouple temperature sensors with an accuracy of ±0.5°C, and accelerometers with a sensitivity of 0.001g, for real-time monitoring of displacement, temperature, and vibration data during the lithography process, the multi-modal sensor array further includes ambient light intensity sensors for monitoring changes in the working environment light, adjusting the measurement parameters of the laser displacement sensors to eliminate the influence of ambient light on displacement measurement accuracy; A data fusion processing unit, with a built-in microcontroller and specialized software, using weighted averaging and Kalman filtering algorithms to integrate and optimize data from the multi-modal sensor array, the data fusion processing unit dynamically adjusts the weight parameters of the weighted averaging and Kalman filtering algorithms according to the changes in real-time data flow to adapt to the characteristics of different lithography environments and materials; An adaptive error compensation module, integrating a PID controller, receiving the output of the data fusion processing unit, dynamically adjusting lithography parameters to compensate for errors; An interactive user interface, equipped with a high-definition touch screen, displaying lithography error analysis results and compensation suggestions, receiving user input for adjusting lithography parameters; An error prediction and compensation strategy generator, using an embedded deep neural network processor, executing LSTM-based machine learning algorithms to analyze historical and current data to predict future lithography errors and generate corresponding compensation strategies.

2. The system for measuring overlay error compensation accuracy according to claim 1, wherein, The PID controller of the adaptive error compensation module uses a self-adjusting algorithm to adjust its control parameters based on error feedback from consecutive lithography processes to achieve more accurate dynamic error compensation.

3. The system for measuring overlay error compensation accuracy of claim 1, wherein, The LSTM model used by the error prediction and compensation strategy generator contains a pre-trained dataset specific to material types and lithography processes to enhance its prediction accuracy and adaptability.

4. The measurement method of the overlay error offset precision measurement system according to any one of claims 1-3, characterized in that, Comprising the following steps: Real-time synchronous acquisition of displacement, temperature, vibration, and ambient light intensity data during the lithography process; Running weighted averaging and Kalman filtering algorithms to integrate and optimize collected data; According to the optimized data, the adaptive error compensation module dynamically adjusts the lithography parameters through the PID controller; Using the LSTM model to predict future lithography errors and generating compensation strategies based on prediction results; The interactive user interface displays lithography error analysis results and compensation suggestions and receives user fine-tuning input.

5. The method of measuring overlay error compensation accuracy according to claim 4, wherein, Further comprising automatically adjusting the weight parameters of the data fusion algorithm according to the volatility of real-time collected data to ensure the accuracy of data optimization under different lithography conditions.

6. The measurement method of overlay error offset precision of a measurement system according to claim 4, wherein, The adaptive error compensation module uses reinforcement learning strategies to automatically learn the optimal PID parameter adjustment strategy to adapt to new error patterns that appear in long-term lithography processes.

7. The measurement method of overlay error offset precision of a measurement system according to claim 4, wherein, The error prediction and compensation strategy generator regularly receives model updates from the cloud platform to incorporate new lithography data and error patterns, maintaining the timeliness and accuracy of the prediction model.

8. The measurement method of overlay error offset precision of the measurement system according to claim 4, wherein, Further comprising providing customized lithography parameter suggestions using the interactive user interface, allowing users to select different preset parameter configurations according to specific lithography tasks to optimize the lithography process.

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