Ultra-precision polishing method for calcium fluoride optical element

Abstract

The invention relates to the technical field of optical machining, and provides an ultra-precision polishing method for a calcium fluoride optical element, which comprises the following steps: S10, initializing polishing process parameters of the calcium fluoride optical element; s20, polishing equipment is started to polish the calcium fluoride optical element, the multi-dimensional sensor network is activated synchronously, and multi-dimensional sensing data of a polishing area are collected in real time; s30, a sliding window median and a median absolute deviation standardization value are calculated by adopting the sliding window, and abnormal points of the multi-dimensional sensing data are checked; s40, in the polishing process, polishing process parameters are dynamically corrected based on abnormal point distribution of the multi-dimensional sensing data; and S50, after the surface roughness reaches the target value and the surface shape precision reaches the standard, polishing is stopped, and the polished target calcium fluoride is obtained. Through multi-sensor fusion and intelligent feedback control, the core problems that in traditional calcium fluoride polishing, real-time performance is poor, multi-physical-quantity coupling is difficult to control, and abnormal response lags behind are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical processing, and particularly relates to an ultra-precision polishing method for calcium fluoride optical elements. Background Art

[0002] Calcium fluoride is an important optical material, which is widely used in ultraviolet and deep ultraviolet optical systems, such as lithography machine lenses, laser optical elements, etc. Existing calcium fluoride polishing methods include mechanical polishing, chemical mechanical polishing, magnetorheological polishing and ion beam polishing.

[0003] Due to the characteristics of calcium fluoride such as low hardness, high brittleness and large thermal expansion coefficient, traditional polishing methods face many challenges in the processing process. For example, traditional polishing relies on off-line detection (such as white light interferometer, atomic force microscope), and the process parameters cannot be adjusted in real time, resulting in poor processing consistency. Moreover, in the polishing process, factors such as pressure, temperature, vibration, and chemical environment affect each other, and it is difficult for traditional methods to comprehensively monitor, easily leading to local over-polishing or under-polishing. Summary of the Invention

[0004] In view of the above-mentioned defects of the prior art, the present invention proposes an ultra-precision polishing method for calcium fluoride optical elements. The technical solution designed by the present invention includes the following steps:

[0005] S10: Polishing process parameters of the initial calcium fluoride optical element;

[0006] S20: Start the polishing equipment to polish the calcium fluoride optical element, and simultaneously activate the multi-dimensional sensor network to collect multi-dimensional sensing data of the polishing area in real time;

[0007] S30: Use a sliding window to calculate the median of the sliding window and the normalized value of the median absolute deviation to check for outliers in the multi-dimensional sensing data;

[0008] S40: During the polishing process, dynamically correct the polishing process parameters based on the distribution of outliers in the multi-dimensional sensing data;

[0009] S50: When the surface roughness reaches the target value and the surface shape accuracy meets the standard, terminate the polishing to obtain the polished target calcium fluoride.

[0010] Preferably, the multi-dimensional sensor network in S20 is composed of the fusion of multiple types of sensors, and key physical quantities during the polishing process are monitored in real time. The sensors include but are not limited to mechanical sensors, optical sensors, thermal sensors, motion control sensors and chemical sensors.

[0011] Preferably, S30 includes:

[0012] S3001: Divide the multi-dimensional sensing data into consecutive subsequences A and B in chronological order for cross-validating the polishing stability;

[0013] S3002: Use a sliding window to calculate the sliding window median and the median absolute deviation normalization value for subsequences A and B respectively, and check for outliers in subsequences A and B based on the sliding window median and the median absolute deviation normalization value;

[0014] S3003: If the difference in the outlier ratios of subsequences A and B is greater than a preset difference threshold, extend the global window for re-detection until the ratio difference between subsequences A and B is less than the preset difference threshold, and output the outlier distribution.

[0015] Preferably, the sliding window median in S3002 has the following formula:

[0016]

[0017] wherein, is the sliding window median value with the data point in the subsequence as the window center, is the number of data points covered by the sliding window before and after the data point in the sliding window.

[0018] Preferably, the median absolute deviation normalization value in S3002 has the following formula:

[0019]

[0020] wherein, is the median absolute deviation normalization value.

[0021] Preferably, the checking for outliers in subsequences A and B based on the sliding window median and the median absolute deviation normalization value includes:

[0022] A preset outlier threshold , if , then determine that the data point in the subsequence is an outlier.

[0023] Preferably, S40 includes:

[0024] Divide the outlier sources according to the sensor type, calculate the correlation between the sensor data belonging to the outlier sources and the polishing process parameters, assign weights to each sensor belonging to the outlier sources, calculate the adjustment amount of the polishing process parameters, and correct the polishing process parameters during the polishing process.

[0025] Preferably, S40 further includes:

[0026] Before calculating the correlation between the sensor data belonging to the source of abnormal points and the polishing process parameters, dimensionless processing is performed on the sensor data belonging to the source of abnormal points and the polishing process parameters.

[0027] S10: Polishing process parameters of the initial calcium fluoride optical element;

[0028] S20: Start the polishing equipment to polish the calcium fluoride optical element, and synchronously activate the multi-dimensional sensor network to collect multi-dimensional sensing data of the polishing area in real time;

[0029] S30: Use a sliding window to calculate the median of the sliding window and the standardized value of the median absolute deviation to check for abnormal points in the multi-dimensional sensing data;

[0030] S40: During the polishing process, dynamically correct the polishing process parameters based on the distribution of abnormal points in the multi-dimensional sensing data;

[0031] S50: When the surface roughness reaches the target value and the surface shape accuracy meets the standard, terminate the polishing to obtain the polished target calcium fluoride.

[0032] Preferably, the multi-dimensional sensor network in S20 includes:

[0033] Composed of the fusion of multiple types of sensors, which monitors the key physical quantities during the polishing process in real time. The sensors include but are not limited to mechanical sensors, optical sensors, thermal sensors, motion control sensors, and chemical sensors.

[0034] Beneficial effects:

[0035] 1. Through the collaborative monitoring of heterogeneous sensors, this application breaks through the limitations of a single data dimension, accurately captures the multi-dimensional characteristics of the polishing state, and improves the sensitivity of anomaly detection;

[0036] 2. This application judges the process stability by checking the abnormal points and their proportional differences, and the extended window mechanism avoids misadjustment, significantly reduces noise interference, and ensures that the parameter correction is based on reliable data. Brief description of the drawings

[0037] Figure 1 is a schematic flow chart of a preferred embodiment of the present invention.

[0038] Figure 2 is a schematic flow chart of the abnormal point inspection of a preferred embodiment of the present invention. Detailed implementation manners

[0039] The following details the embodiments of the present invention. The following embodiments are implemented on the premise of the technical solution of the present invention, and give detailed implementation manners and specific operation processes. However, the protection scope of the present invention is not limited to the following embodiments.

[0040] The present invention designs an ultra-precision polishing method for calcium fluoride optical elements, as Figure 1 shown, the technical solution includes the following steps, specifically including:

[0041] S10: Polishing process parameters of the initial calcium fluoride optical element;

[0042] S20: Start the polishing equipment to polish the calcium fluoride optical element, and synchronously activate the multi-dimensional sensor network to collect multi-dimensional sensing data of the polishing area in real time;

[0043] S30: Use a sliding window to calculate the sliding window median and the median absolute deviation normalization value, and check for outliers in the multi-dimensional sensing data;

[0044] S40: During the polishing process, dynamically correct the polishing process parameters based on the distribution of outliers in the multi-dimensional sensing data;

[0045] S50: When the surface roughness reaches the target value and the surface shape accuracy meets the standard, terminate the polishing to obtain the polished target calcium fluoride.

[0046] Preferably, the multi-dimensional sensor network in S20 includes a fusion of multiple types of sensors to monitor key physical quantities during the polishing process. The sensors include, but are not limited to, mechanical sensors, optical sensors, thermal sensors, motion control sensors, and chemical sensors.

[0047] Specifically, the mechanical sensors include a dynamic pressure sensor and a six-axis force sensor, which are respectively used to detect the pressure distribution in the polishing area and the attitude and force balance of the polishing head; the optical sensors include an in-line white light interferometer and a laser displacement sensor, which are used to measure the surface roughness, surface shape accuracy, and workpiece thickness change in real time; the thermal sensors include an infrared thermal imager and a thermocouple, which are respectively used to detect the temperature field distribution in the polishing area and the situation of being embedded in the bottom layer of the polishing pad; the motion control sensors include an encoder and a vibration sensor, which are respectively used to detect the rotational speed feedback of the polishing head and the workpiece table and the abnormal vibration spectrum; the chemical sensors are used to detect pH electrodes and conductivity meters.

[0048] Preferably, as Figure 2 shown, S30 includes:

[0049] S3001: Divide the multi-dimensional sensing data into continuous subsequences A and B in chronological order for cross-verifying the polishing stability;

[0050] S3002: Respectively use a sliding window to calculate the sliding window median and the median absolute deviation normalization value for subsequences A and B, and check for outliers in subsequences A and B based on the sliding window median and the median absolute deviation normalization value;

[0051] S3003: If the difference in the proportion of outliers between subsequence A and subsequence B is greater than the preset difference threshold, extend the global window for re-detection until the proportion difference between subsequence A and subsequence B is less than the preset difference threshold, and output the outlier distribution.

[0052] Specifically, the multi-dimensional sensor network collects multi-dimensional sensing data of the polishing area at fixed time The time stamp of a single sensing data in the polishing area is , is the number of times of the preset fixed time, is the initial time of this sensing data; divide this sensing data into two parts, the first half is subsequence A and the second half is subsequence B according to the time stamp; before calculating using the sliding window, the preset global window duration is Then the time stamps of subsequence A and subsequence B are respectively expressed as and , where is the initial time of subsequence A, and the preset sliding window duration is If it is necessary to extend the global window for re-detection, the total duration of the new window is Then the time stamps of subsequence A and subsequence B are respectively expressed as and , where is the preset extended global window time.

[0053] In addition, since the outliers in a single subsequence may be only temporary fluctuations rather than systematic problems, if the difference in the proportion of outliers between the two subsequences is large, it indicates that the process is unstable. Direct correction at this time may lead to misadjustment because it may be only a temporary interference. By comparing the stabilities of the two subsequences, it can be ensured that the correction is based on more reliable data and noise interference is avoided. When detecting the proportion difference between them, if the difference is small, it indicates good stability throughout the time period, which is convenient for subsequent parameter correction based on outliers. If the difference is large, there may be an unstable situation, and it is necessary to extend the sampling window for re-detection to ensure that the final subsequences A and B used for correction represent a stable state and avoid misjudgment.

[0054] Preferably, the median of the sliding window in S3002 has the following formula:

[0055]

[0056] In the formula, is the median value of the sliding window with the data point in the subsequence as the window center, is the number of data points covered in the sliding window before and after the data point respectively.

[0057] Preferably, the median absolute deviation normalization value in S3002 has the following formula:

[0058]

[0059] In the formula, is the median absolute deviation normalization value.

[0060] Preferably, checking for outliers in subsequence A and subsequence B based on the sliding window median and the median absolute deviation normalization value includes:

[0061] A preset outlier threshold , if , then it is determined that the data point in the subsequence is an outlier.

[0062] Specifically, the resistance of the median and MAD to non-Gaussian distributions and outliers solves the problem of the failure of the traditional mean-variance method in polishing data. In addition, the sliding window mechanism dynamically tracks changes in the process state, avoiding the lag of the fixed threshold method, and is particularly suitable for ultra-precision machining of brittle materials such as calcium fluoride.

[0063] Preferably, S40 includes:

[0064] Dividing the source of outliers according to the sensor type, calculating the correlation between the sensor data belonging to the source of outliers and the polishing process parameters, assigning weights to each sensor belonging to the source of outliers, calculating the adjustment amount of the polishing process parameters, and correcting the polishing process parameters during the polishing process.

[0065] Preferably, S40 further includes:

[0066] Before calculating the correlation between the sensor data belonging to the source of outliers and the polishing process parameters, perform dimensionless processing on the sensor data belonging to the source of outliers and the polishing process parameters.

[0067] Specifically, the formula for calculating the correlation between the sensor data belonging to the source of outliers and the polishing process parameters is as follows:

[0068]

[0069] In the formula, is the correlation between the i-th type of sensor and the j-th polishing process parameter, is the sensing data sequence of the i-th type of sensor at time t, is the normal reference value of sensor i, is the set value sequence of the j-th polishing process parameter, is the reference set value of the j-th polishing process parameter, t is the sampling time, and T is the total number of sampling points.

[0070] Assign weights to each sensor belonging to the source of abnormal points, and the formula is as follows:

[0071]

[0072]

[0073] In the formula, is the weight distribution coefficient of the i-th type of sensor, N is the total number of sensor types, is the cross-correlation energy between sensors i and k, is the time lag, is the maximum time lag.

[0074] Calculate the adjustment amount of the polishing process parameter, and the formula is as follows:

[0075]

[0076]

[0077] In the formula, is the final parameter adjustment amount for the j-th polishing process parameter, is the dynamic adjustment coefficient, is the influence factor of sensor i on parameter j.

[0078] Specifically, when assigning weights to each sensor belonging to the source of abnormal points, it is necessary to limit that i and k are not equal; in addition, through multi-sensor data fusion and correlation weight assignment, intelligent dynamic adjustment of polishing parameters is realized. Compared with traditional static parameter control or single-dimensional feedback methods, multiple parameters such as pressure, temperature, rotation speed, and pH value are synchronously optimized to solve the strong coupling nonlinear problem in calcium fluoride polishing, establish a closed-loop relationship between process parameters - sensing data - material removal model, and realize true multi-variable decoupling control.

[0079] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. An ultra-precision polishing method for a calcium fluoride optical element, characterized in that: The following steps are involved: S10: polishing process parameters of initial calcium fluoride optical element; S20: start the polishing equipment to polish the calcium fluoride optical element, and simultaneously activate the multi-dimensional sensor network to collect multi-dimensional sensor data of the polishing area in real time; S30: using a sliding window to calculate the sliding window median and the standardized value of the median absolute deviation to check the abnormal points of the multi-dimensional sensor data; S40: During the polishing process, dynamically correct the polishing process parameters based on the abnormal point distribution of the multi-dimensional sensing data; S50: When the surface roughness reaches the target value and the surface accuracy reaches the standard, the polishing is terminated to obtain the target calcium fluoride after polishing.

2. The ultra-precision polishing method of a calcium fluoride optical element according to claim 1, characterized in that: The multi-dimensional sensor network in S20 includes a fusion of multiple types of sensors to monitor key physical quantities of the polishing process in real time. The sensors include but are not limited to mechanical sensors, optical sensors, thermal sensors, motion control sensors and chemical sensors.

3. The ultra-precision polishing method of a calcium fluoride optical element according to claim 1, characterized in that: The S30 includes: S3001: Dividing the multidimensional sensing data into continuous subsequence A and subsequence B in chronological order for cross-validation of polishing stability; S3002: Calculate the median and median absolute deviation normalized values ​​of the sliding window for subsequence A and subsequence B respectively using a sliding window, and check the abnormal points of subsequence A and subsequence B based on the median and median absolute deviation normalized values ​​of the sliding window; S3003: If the difference in the proportion of outliers between subsequence A and subsequence B is greater than a preset difference threshold, extend the global window for re-detection until the difference in the proportion of subsequence A and subsequence B is less than the preset difference threshold, and output the distribution of outliers.

4. The ultra-precision polishing method of a calcium fluoride optical element according to claim 3, characterized in that: The sliding window median in S3002 is formulated as follows: ; In the formula, The data points in the subsequence is the median value of the sliding window at the center of the window, Covering data points in sliding window The number of preceding and following data points.

5. The ultra-precision polishing method of a calcium fluoride optical element according to claim 4, characterized in that: The median absolute deviation normalized value in S3002 is as follows: ; In the formula, is the median absolute deviation normalized value.

6. The ultra-precision polishing method of a calcium fluoride optical element according to claim 5, characterized in that: The checking of abnormal points of subsequence A and subsequence B based on the sliding window median and median absolute deviation normalization value includes: Preset abnormal threshold ,like , then determine the data points in the subsequence For abnormal points.

7. The ultra-precision polishing method of a calcium fluoride optical element according to claim 1, characterized in that: The S40 includes: The sources of abnormal points are divided according to the sensor type, the correlation between the sensor data belonging to the abnormal point source and the polishing process parameters is calculated, a weight is assigned to each sensor belonging to the abnormal point source, the adjustment amount of the polishing process parameters is calculated and the polishing process parameters of the polishing process are corrected.

8. The ultra-precision polishing method of a calcium fluoride optical element according to claim 7, characterized in that: The S40 further includes: Before calculating the correlation between the sensor data belonging to the abnormal point source and the polishing process parameters, the sensor data belonging to the abnormal point source and the polishing process parameters are dimensionally processed.

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