Displacement measurement method and device based on four paths of interference signals, medium and equipment

Abstract

The invention discloses a displacement measurement method and device based on four-way interference signals, a medium and equipment, and relates to the technical field of semiconductor detection, and the method comprises the steps: carrying out the differential processing of four-way interference signal data with a preset phase relation, and obtaining two-way orthogonal signal data; respectively carrying out extreme value detection on the two paths of orthogonal signal data through a self-adaptive updating strategy to obtain extreme value detection results; performing first normalization processing, orthogonal transformation processing, second normalization processing and phase resolving processing on the two paths of orthogonal signal data in sequence based on an extreme value detection result to obtain phase data; calculating the phase difference between any two adjacent elements in the phase data to obtain phase difference data; and calculating a displacement increment corresponding to each phase difference in the phase difference data through an intelligent direction identification algorithm, and accumulating the displacement increments to obtain a displacement measurement result corresponding to the four paths of interference signal data. According to the invention, a displacement measurement result with higher precision and higher stability can be provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor detection, and in particular to a displacement measurement method and device based on four-path interference signals, a medium and equipment. BACKGROUND

[0002] Interferometric displacement measurement technology is a precision measurement technology based on the principle of light interference. Since the advent of the Michelson interferometer in the late 19th century, after more than a century of technical iteration and improvement, it has become a core technical means in the field of nanometer ultra-precision measurement, and is widely used in technical fields such as semiconductor detection, high-end equipment manufacturing, micro-nano processing, etc. which have strict requirements on displacement measurement precision and stability. Especially in the semiconductor detection scene, as the chip process technology advances to smaller nodes, the demand for displacement measurement resolution, anti-interference ability and long-term stability continues to increase, which also puts higher challenges on the performance of interferometric displacement measurement technology.

[0003] At present, the mainstream interferometric displacement measurement scheme in the industry generally uses a two-path orthogonal signal processing method to realize displacement calculation. The typical process is as follows: first, two orthogonal interference light signals with a phase difference of 90° are collected by a photoelectric detector and converted into electrical signals; then, the electrical signals are subjected to simple normalization signal conditioning operations to eliminate part of the DC bias and adjust the signal amplitude; then, the phase calculation is completed by the inverse tangent function, and then the phase difference between adjacent sampling points is calculated; finally, the displacement increment is calculated according to the corresponding relationship between the phase difference and the wavelength of light, and the total displacement is obtained by accumulating the displacement increment. This technical scheme has been widely used in measurement scenes with conventional precision requirements due to its clear principle and simple operation.

[0004] However, in the ultra-precision measurement scene such as semiconductor detection, the above-mentioned two-path orthogonal signal processing scheme has a core technical problem: limited anti-interference ability, which is difficult to cope with the interference of complex environments. Since the two-path signal is directly used for phase calculation, it is easily affected by common-mode interference such as power supply noise, temperature drift, mechanical vibration in the industrial field, which not only causes signal amplitude mismatch and phase calculation deviation, but also causes displacement direction judgment error in the working condition of rapid movement or vibration of the measured object, ultimately causing a significant decrease in measurement accuracy, which cannot meet the actual needs of nanometer ultra-precision measurement. SUMMARY

[0005] Therefore, the present application provides a displacement measurement method and device based on four-path interference signals, which can provide displacement measurement results with higher precision and stronger stability.

[0006] According to a first aspect of the present application, a displacement measurement method based on four-path interference signals is provided, comprising: Differential processing is performed on four interference signal data with a preset phase relationship to obtain two orthogonal signal data, which include a first sine signal data and a first cosine signal data. An adaptive update strategy is used to perform extreme value detection on the two orthogonal signal data respectively to obtain the extreme value detection results; Based on the extreme value detection results, the two orthogonal signal data are subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process in sequence to obtain phase data; Calculate the phase difference between any two adjacent elements in the phase data to obtain the phase difference data; The displacement increment corresponding to each phase difference in the phase difference data is calculated by an intelligent direction discrimination algorithm, and the displacement increments are accumulated to obtain the displacement measurement results corresponding to the four interference signal data.

[0007] Further, the differential processing of the four interferometric signal data with a preset phase relationship to obtain two orthogonal signal data includes: The four interference signal data are grouped into pairs according to a preset phase relationship to determine the first signal group and the second signal group; Perform a subtraction operation on the two signals in the first signal group to generate the first sinusoidal signal data; The two signals in the second signal group are subtracted to generate the first cosine path signal data.

[0008] Furthermore, the extreme value detection result includes the current maximum value and the current minimum value of each of the two orthogonal signal data; An adaptive update strategy is used to perform extreme value detection on the two orthogonal signal data respectively, and the extreme value detection results are obtained, including: Extract the first preset number of consecutive data points from the two orthogonal signal data, and then filter the current maximum and current minimum values ​​of the two orthogonal signal data respectively from the consecutive data points; Using a sliding window of preset length as a unit, read the subsequent new sampled data of the two orthogonal signal data in sequence; Each new sampled data is compared in real time with the current maximum and minimum values ​​of the corresponding orthogonal signal data. If the new sampled data is greater than the current maximum value of the corresponding quadrature signal data, then the new sampled data is updated to the current maximum value of the quadrature signal data; if the new sampled data is less than the current minimum value of the corresponding quadrature signal data, then the new sampled data is updated to the current minimum value of the quadrature signal data. Every second preset number of sampling points, the current maximum and minimum values ​​of the two orthogonal signal data are attenuated.

[0009] Further, based on the extreme value detection results, the two orthogonal signal data are sequentially subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process to obtain phase data, including: The current maximum and minimum values ​​of the first sine path signal data and the first cosine path signal data in the extreme value detection result are called, and four arithmetic operations are performed on the two orthogonal signal data respectively to obtain the second sine path signal data and the second cosine path signal data after the first normalization process. Addition and subtraction operations are performed on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, and the corresponding maximum values ​​are detected in the third sine wave signal data and the third cosine wave signal data respectively. Based on the maximum value, division operations are performed on the third sine path signal data and the third cosine path signal data respectively to obtain the fourth sine path signal data and the fourth cosine path signal data after the second normalization process. Phase calculation processing is performed based on the fourth sine wave signal data and the fourth cosine wave signal data to obtain a continuous phase data sequence, which is used as the phase data.

[0010] Further, the step of calling the current maximum and minimum values ​​of the first sine wave signal data and the first cosine wave signal data in the extreme value detection result, and performing four arithmetic operations on the two orthogonal signal data respectively to obtain the second sine wave signal data and the second cosine wave signal data after the first normalization process includes: The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called, and the four arithmetic operations are performed on each sample point element of the first sine wave signal data using the first normalization calculation formula to obtain the second sine wave signal data after the first normalization process. The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called. The four arithmetic operations are performed on each sample point element of the first cosine wave signal data using the second normalization calculation formula to obtain the second cosine wave signal data after the first normalization process.

[0011] Further, addition and subtraction operations are performed on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, including: Perform an addition operation on the elements of the same sampling point in the second sinusoidal signal data and the second cosine signal data to obtain the third sinusoidal signal data after orthogonal transformation; Subtract the elements at the same sampling point in the second sine signal data and the second cosine signal data to obtain the third cosine signal data after orthogonal transformation.

[0012] Further, based on the maximum value, division operations are performed on the third sine wave signal data and the third cosine wave signal data respectively to obtain the fourth sine wave signal data and the fourth cosine wave signal data after the second normalization process, including: Each element of the third sinusoidal signal data is divided by the maximum value in the third sinusoidal signal data to obtain the fourth sinusoidal signal data after the second normalization process. Each sample point element in the third cosine path signal data is divided by the maximum value in the third cosine path signal data to obtain the fourth cosine path signal data after the second normalization process.

[0013] Further, based on the fourth sine wave signal data and the fourth cosine wave signal data, phase calculation processing is performed to obtain a continuous phase data sequence, which serves as the phase data, including: Using the sampling point elements in the fourth sine path signal data as the numerator and the same sampling point element in the fourth cosine path signal data as the denominator, perform an arctangent operation to obtain the phase value corresponding to each sampling point element. All phase values ​​are arranged in order of sampling time to form a continuous phase data sequence, which is used as the phase data.

[0014] Further, the phase difference between any two adjacent elements in the phase data is calculated to obtain phase difference data, including: Extract two adjacent phase elements from the phase data in the order of sampling time, and record them as the preceding phase value and the following phase value. Calculate the initial phase difference between the subsequent phase value and the preceding phase value; Determine whether the initial phase difference is within a preset phase interval; If so, the initial phase difference is determined as the phase difference between the two adjacent phase elements; If not, the initial phase difference is wrapped to obtain a phase difference within the preset phase range.

[0015] Further, the step of calculating the displacement increment corresponding to each phase difference in the phase difference data using an intelligent direction-identification algorithm, and accumulating the displacement increments to obtain the displacement measurement result corresponding to the four-channel interference signal data, includes: Based on a preset phase interval, the target phase interval of each phase difference in the phase difference data is determined. The preset phase interval includes (-π, -0.6π), [-0.6π, 0), [0, 0.6π), and [0.6π, +π). Different preset phase intervals are configured with different displacement calculation strategies. Based on the displacement calculation strategy configured for the target phase interval, calculate the displacement increment corresponding to each phase difference in the phase difference data; The total displacement data is obtained by summing all the displacement increments in the order of sampling time. Drift compensation correction is performed on the total displacement data, and the corrected displacement measurement results are output.

[0016] According to a second aspect of this application, a displacement measurement device based on four-channel interferometric signals is provided, comprising: The first processing module is used to perform differential processing on four interference signal data with a preset phase relationship to obtain two orthogonal signal data, the two orthogonal signal data including a first sine signal data and a first cosine signal data; The detection module is used to perform extreme value detection on the two orthogonal signal data respectively through an adaptive update strategy to obtain extreme value detection results; The second processing module is used to perform a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process on the two orthogonal signal data based on the extreme value detection result, so as to obtain phase data. The first calculation module is used to calculate the phase difference between any two adjacent elements in the phase data to obtain phase difference data. The second calculation module is used to calculate the displacement increment corresponding to each phase difference in the phase difference data through an intelligent direction discrimination algorithm, and to accumulate the displacement increments to obtain the displacement measurement results corresponding to the four interference signal data.

[0017] Furthermore, the first processing module is specifically used for: The four interference signal data are grouped into pairs according to a preset phase relationship to determine the first signal group and the second signal group; Perform a subtraction operation on the two signals in the first signal group to generate the first sinusoidal signal data; The two signals in the second signal group are subtracted to generate the first cosine path signal data.

[0018] Furthermore, the extreme value detection result includes the current maximum value and the current minimum value of each of the two orthogonal signal data; The detection module is specifically used for: Extract the first preset number of consecutive data points from the two orthogonal signal data, and then filter the current maximum and current minimum values ​​of the two orthogonal signal data respectively from the consecutive data points; Using a sliding window of preset length as a unit, read the subsequent new sampled data of the two orthogonal signal data in sequence; Each new sampled data is compared in real time with the current maximum and minimum values ​​of the corresponding orthogonal signal data. If the new sampled data is greater than the current maximum value of the corresponding quadrature signal data, then the new sampled data is updated to the current maximum value of the quadrature signal data; if the new sampled data is less than the current minimum value of the corresponding quadrature signal data, then the new sampled data is updated to the current minimum value of the quadrature signal data. Every second preset number of sampling points, the current maximum and minimum values ​​of the two orthogonal signal data are attenuated.

[0019] Furthermore, the second processing module is specifically used for: The current maximum and minimum values ​​of the first sine path signal data and the first cosine path signal data in the extreme value detection result are called, and four arithmetic operations are performed on the two orthogonal signal data respectively to obtain the second sine path signal data and the second cosine path signal data after the first normalization process. Addition and subtraction operations are performed on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, and the corresponding maximum values ​​are detected in the third sine wave signal data and the third cosine wave signal data respectively. Based on the maximum value, division operations are performed on the third sine path signal data and the third cosine path signal data respectively to obtain the fourth sine path signal data and the fourth cosine path signal data after the second normalization process. Phase calculation processing is performed based on the fourth sine wave signal data and the fourth cosine wave signal data to obtain a continuous phase data sequence, which is used as the phase data.

[0020] Furthermore, the second processing module is specifically used for: The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called, and the four arithmetic operations are performed on each sample point element of the first sine wave signal data using the first normalization calculation formula to obtain the second sine wave signal data after the first normalization process. The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called. The four arithmetic operations are performed on each sample point element of the first cosine wave signal data using the second normalization calculation formula to obtain the second cosine wave signal data after the first normalization process.

[0021] Furthermore, the second processing module is specifically used for: Perform an addition operation on the elements of the same sampling point in the second sinusoidal signal data and the second cosine signal data to obtain the third sinusoidal signal data after orthogonal transformation; Subtract the elements at the same sampling point in the second sine signal data and the second cosine signal data to obtain the third cosine signal data after orthogonal transformation.

[0022] Furthermore, the second processing module is specifically used for: Each element of the third sinusoidal signal data is divided by the maximum value in the third sinusoidal signal data to obtain the fourth sinusoidal signal data after the second normalization process. Each sample point element in the third cosine path signal data is divided by the maximum value in the third cosine path signal data to obtain the fourth cosine path signal data after the second normalization process.

[0023] Furthermore, the second processing module is specifically used for: Using the sampling point elements in the fourth sine path signal data as the numerator and the same sampling point element in the fourth cosine path signal data as the denominator, perform an arctangent operation to obtain the phase value corresponding to each sampling point element. All phase values ​​are arranged in order of sampling time to form a continuous phase data sequence, which is used as the phase data.

[0024] Furthermore, the first computing module is specifically used for: Extract two adjacent phase elements from the phase data in the order of sampling time, and record them as the preceding phase value and the following phase value. Calculate the initial phase difference between the subsequent phase value and the preceding phase value; Determine whether the initial phase difference is within a preset phase interval; If so, the initial phase difference is determined as the phase difference between the two adjacent phase elements; If not, the initial phase difference is wrapped to obtain a phase difference within the preset phase range.

[0025] Furthermore, the second computing module is specifically used for: Based on a preset phase interval, the target phase interval of each phase difference in the phase difference data is determined. The preset phase interval includes (-π, -0.6π), [-0.6π, 0), [0, 0.6π), and [0.6π, +π). Different preset phase intervals are configured with different displacement calculation strategies. Based on the displacement calculation strategy configured for the target phase interval, calculate the displacement increment corresponding to each phase difference in the phase difference data; The total displacement data is obtained by summing all the displacement increments in the order of sampling time. Drift compensation correction is performed on the total displacement data, and the corrected displacement measurement results are output.

[0026] According to a third aspect of this application, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described displacement measurement method based on four-way interference signals.

[0027] According to a fourth aspect of this application, an electronic device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described displacement measurement method based on four-way interference signals.

[0028] Using the above technical solution, this application provides a displacement measurement method, device, medium, and equipment based on four-channel interferometric signals. The method involves differential processing of four interferometric signal data with a preset phase relationship to obtain two orthogonal signal data, including a first sine signal data and a first cosine signal data. An adaptive update strategy is used to perform extreme value detection on the two orthogonal signal data to obtain extreme value detection results. Based on the extreme value detection results, the two orthogonal signal data are sequentially subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process to obtain phase data. The phase difference between any two adjacent elements in the phase data is calculated to obtain phase difference data. An intelligent direction-identification algorithm is used to calculate the displacement increment corresponding to each phase difference in the phase difference data, and the displacement increments are accumulated to obtain the displacement measurement result corresponding to the four-channel interferometric signal data. Among them, the differential processing of the four interference signals can effectively cancel common-mode interference such as power supply noise, temperature drift, and mechanical vibration, greatly improving the anti-interference capability of the measurement system and ensuring the purity of the signal in complex industrial environments. The adaptive extreme value update strategy can provide real-time and accurate extreme value references for subsequent normalization processing. Combined with the first normalization to eliminate DC bias and unify the dynamic range, and the orthogonal transformation and the second normalization to further optimize the orthogonal characteristics of the signal and ensure amplitude consistency, it can significantly improve the accuracy and stability of phase calculation. In addition, the intelligent direction identification algorithm based on phase difference interval division can effectively avoid errors in displacement direction judgment under rapid movement or vibration conditions of the measured object. Combined with the accurate calculation of phase difference and the accumulation of displacement increment, it can finally achieve nanometer-level ultra-precision displacement measurement, which fully meets the accuracy requirements of ultra-precision measurement scenarios such as semiconductor testing.

[0029] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic flowchart of a displacement measurement method based on four-channel interferometric signals provided in an embodiment of this application is shown. Figure 2 This illustration shows a schematic diagram of the principle of generating two orthogonal signal data according to an embodiment of this application; Figure 3 A flowchart illustrating a displacement measurement method based on four-channel interferometric signals according to another embodiment of this application is shown. Figure 4 This illustration shows a process diagram of differential processing of four-channel interferometric signal data according to an embodiment of this application; Figure 5 This illustration shows a process diagram of performing a first normalization process on two orthogonal signal data according to an embodiment of this application; Figure 6 This illustration shows a process diagram of orthogonal transformation of two orthogonal signal data according to an embodiment of this application; Figure 7 This illustration shows a process diagram of performing a second normalization process on two orthogonal signal data according to an embodiment of this application; Figure 8 This illustration shows a process diagram of phase calculation for two orthogonal signal data according to an embodiment of this application; Figure 9 This application provides a flowchart illustrating the process of calculating displacement measurement results according to an embodiment of the present application. Figure 10 A schematic diagram of a displacement measurement device based on four-channel interference signals provided in an embodiment of this application is shown. Detailed Implementation

[0031] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0032] Currently, mainstream interferometric displacement measurement solutions in the industry generally employ a dual-channel orthogonal signal processing method to calculate displacement. The typical process is as follows: First, two orthogonal interference light signals with a 90° phase difference are acquired using a photodetector and converted into electrical signals. Then, the electrical signals undergo simple normalization and signal conditioning to eliminate some DC bias and adjust the signal amplitude. Next, the phase is calculated using an arctangent function, and the phase difference between adjacent sampling points is obtained. Finally, the displacement increment is calculated based on the relationship between the phase difference and the light wavelength, and the total displacement is obtained by accumulating the displacement increments. This technical solution is widely used in measurement scenarios with conventional accuracy requirements due to its clear principle and ease of operation.

[0033] However, in ultra-precision measurement scenarios such as semiconductor testing, the aforementioned dual-channel orthogonal signal processing scheme suffers from a core technical problem: limited anti-interference capability, making it difficult to cope with interference in complex environments. Since the dual signals are directly used for phase calculation, they are susceptible to common-mode interference from power supply noise, temperature drift, and mechanical vibration in industrial environments. This not only leads to signal amplitude mismatch and increased phase calculation deviation, but also causes errors in displacement direction judgment under conditions of rapid movement or vibration of the measured object, ultimately resulting in a significant decrease in measurement accuracy and failing to meet the practical needs of nanometer-level ultra-precision measurement.

[0034] To address the aforementioned problems, embodiments of the present invention provide a displacement measurement method based on four-channel interferometric signals, such as... Figure 1 As shown, the method includes: Step 110: Perform differential processing on the four interference signal data with a preset phase relationship to obtain two orthogonal signal data, which include the first sine signal data and the first cosine signal data.

[0035] Among them, the four-channel interference signal data refers to the data set formed after the four analog interference light signals generated by a special optical path (composed of optical components such as He-Ne laser, corner bevel prism, λ / 4 waveplate, polarizing beam splitter (PBS), non-polarizing beam splitter (NPBS), and photodetector (PD)) are converted into digital signals by an AD conversion card. The four signals have a preset fixed phase relationship and are the original data basis for subsequent displacement measurement signal processing. Differential processing is a signal preprocessing operation for the four-channel interference signal data. Specifically, it is a method of processing that groups the four signals into pairs according to the preset phase relationship and then performs a subtraction operation to cancel out the common interference components in the signals. The two-channel orthogonal signal data refers to the two sets of digital signal data with a 90° phase difference obtained after differential processing of the four-channel interference signals. The first sine signal data is the data of the two orthogonal signal data that exhibits the characteristics of a sine signal. The first cosine signal data is the data of the two orthogonal signal data that exhibits the characteristics of a cosine signal and is orthogonal to the first sine signal data.

[0036] In this embodiment, a matching optical system and signal conversion module are required. First, a He-Ne laser emits laser light, which is then split and phase-modulated by optical elements such as a corner prism, various waveplates, and polarizing beam splitters to form four interference light signals with a preset phase relationship. These signals are then converted into analog electrical signals by a photodetector. Subsequently, an AD conversion card converts the four analog electrical signals into digital signals, i.e., four interference signal data, and transmits them to the FPGA processing board. In the FPGA processing board, the system divides the four signals into two signal groups according to the preset phase relationship. The two signals in the first signal group are subtracted to generate the first sine signal data, and the two signals in the second signal group are subtracted to generate the first cosine signal data. Finally, two orthogonal signal data that satisfy the orthogonal phase relationship are obtained.

[0037] like Figure 2As shown, the data generation process for two orthogonal signals can be summarized as follows: The monochromatic coherent laser emitted by the He-Ne laser is first polarized by a λ / 2 waveplate 1. After entering the polarization beam splitter PBS1, it is split into two beams with perpendicular polarization directions. One beam serves as a reference beam, is reflected by a corner prism 1, and returns to PBS1 after undergoing polarization transformation via a λ / 4 waveplate 1. The other beam serves as the measurement beam, is modulated by a subsequent λ / 4 waveplate, and then directed towards a corner prism 2 that moves with the object being measured. Its optical path changes with the displacement of the corner prism 2 and carries displacement phase information. The two beams are combined at PBS1 and then enter the PBS1. The non-polarized beam splitter (NPBS) is divided into two unpolarized beams. One beam enters PBS2 and is split again according to the polarization direction, and is received by PD2 and PD4 respectively. The other beam enters PBS3 and is split in the same way, and is received by PD1 and PD3. Finally, through polarization modulation of the optical path, introduction of optical path difference, and multiple splitting and combining of the beams, the four photodetectors PD1-PD4 receive four interference light signals with a fixed preset phase relationship. After photoelectric conversion, these signals will become the core raw data for subsequent differential processing, phase calculation, and displacement measurement, laying a solid optical signal foundation for realizing nanometer-level ultra-precision displacement detection.

[0038] This technical step, through differential processing of four interference signals, can effectively cancel common-mode interference such as power supply noise, temperature drift, and mechanical vibration that are common in the four signals. At the same time, it eliminates DC bias and low-frequency drift in the original signal, greatly improving the purity of the signal. In addition, the first sine and first cosine orthogonal signal data formed after differential processing have a stable phase orthogonality relationship, which can avoid the amplitude mismatch and phase deviation problems that are easy to occur in traditional dual-channel direct acquisition signals. It can not only provide high-quality signal input for subsequent high-precision processing links such as extreme value detection and double normalization, but also improve the anti-interference capability of the entire displacement measurement system from the signal source, laying a solid signal foundation for realizing nanometer-level high-precision displacement measurement.

[0039] Step 120: Perform extreme value detection on the two orthogonal signal data using an adaptive update strategy to obtain the extreme value detection results.

[0040] The adaptive update strategy is a dynamic calibration mechanism for the extrema of orthogonal signals. Specifically, it first extracts the extrema of the initial data segment of the signal as a benchmark, then updates the extrema by comparing the new sampled data in real time through a sliding window, and periodically attenuates the current extrema to avoid the solidification of extrema. The extrema detection is an operation to extract the amplitude characteristics of the two orthogonal signal data. Specifically, it identifies the maximum and minimum values ​​in the signal data to provide key reference data for subsequent normalization processing. The extrema detection result refers to the current maximum and minimum values ​​of the two orthogonal signal data that are dynamically changing in real time after extrema detection and adaptive update.

[0041] In this embodiment of the present disclosure, after acquiring the two orthogonal signal data after differential processing, the first 200-300 consecutive data points of the two signals can be extracted, and the initial current maximum and minimum values ​​of each signal can be selected as the initial extreme values. Then, in a sliding window of a preset length, the subsequent new sampled data of the two signals are read sequentially, and each new sampled data is compared with the current maximum and minimum values ​​of the corresponding signal in real time. If the new sampled data is greater than the current maximum value, the maximum value is updated; if it is less than the current minimum value, the minimum value is updated. At the same time, the system will perform attenuation processing on the current maximum and minimum values ​​of the two signals at intervals of a certain number of sampling points, and finally form an extreme value detection result that can be dynamically updated in real time, providing an accurate amplitude reference for subsequent normalization processing.

[0042] This technical step, through branched extreme value detection and an adaptive update strategy, can provide a real-time and accurate amplitude reference for subsequent normalization processing, adapt to real-time signal fluctuations through dynamic updates of the sliding window, and effectively avoid extreme value solidification by periodically performing extreme value attenuation processing. It can also adapt to signal drift during long-term measurement, ensuring the accuracy of signal amplitude adjustment and DC bias elimination from the data source, thereby improving the long-term stability and phase calculation accuracy of the entire displacement measurement system.

[0043] Step 130: Based on the extreme value detection results, perform the first normalization processing, orthogonal transformation processing, second normalization processing, and phase calculation processing on the two orthogonal signal data in sequence to obtain phase data.

[0044] The first normalization process involves performing arithmetic operations on two orthogonal signal data based on the extreme values ​​detected in the extreme value detection results. Its core purpose is to eliminate DC bias and unify the dynamic range of the two signals. The second normalization process involves performing addition and subtraction operations on the normalized signal to generate new orthogonal signal pairs, further optimizing the orthogonal characteristics and reducing orthogonal errors. The third normalization process involves performing division operations on the transformed orthogonal signal based on its maximum value, further calibrating the signal amplitude and ensuring amplitude consistency. The fourth phase calculation process involves performing an arctangent operation on the normalized signal to convert the signal amplitude information into phase information. The phase data refers to the continuous phase value sequence formed by arranging the samples in chronological order after phase calculation, which is the core data for subsequent calculations of phase difference and displacement.

[0045] In this embodiment of the present disclosure, after acquiring two orthogonal signal data and the corresponding extreme value detection results, the current maximum and minimum values ​​of the first sine signal data and the first cosine signal data in the extreme value detection results are first called. The two orthogonal signal data are then subjected to four arithmetic operations to complete the first normalization process, eliminating the DC bias of the signals and unifying their dynamic range, resulting in the normalized intermediate orthogonal signals. Subsequently, addition and subtraction operations are performed on the intermediate orthogonal signals to complete the orthogonal transformation process, generating new orthogonal signal pairs. At the same time, the maximum values ​​of the new signal pairs are detected simultaneously. Then, based on the detected maximum values ​​of the transformed signals, division operations are performed on the new orthogonal signal pairs to complete the second normalization process, further calibrating the signal amplitude to ensure amplitude consistency. Finally, the elements of the corresponding sampling points in the two signals after the second normalization are used as the numerator and denominator to perform arctangent operations, obtaining the phase value corresponding to each sampling point. All phase values ​​are arranged in the order of sampling time to form a continuous phase data sequence, which is the final phase data.

[0046] This technical process combines dual normalization (first normalization, orthogonal transformation, and second normalization) with signal optimization. The first normalization eliminates the DC bias of the original orthogonal signals and unifies the dynamic range. The orthogonal transformation further optimizes the orthogonal characteristics of the signals, reducing the impact of orthogonal errors on subsequent processing. The second normalization calibrates the amplitude consistency of the transformed signals, providing high-quality signal input for phase calculation. Accurate phase calculation transforms the optimized signal amplitude information into continuous phase data. The overall technical characteristics enable a step-by-step improvement in signal quality, significantly reducing phase calculation errors caused by factors such as signal amplitude mismatch and orthogonal deviation. This lays a stable and accurate data foundation for subsequent phase difference calculation and high-precision displacement measurement, significantly improving the calculation accuracy and stability of the entire displacement measurement system.

[0047] Step 140: Calculate the phase difference between any two adjacent elements in the phase data to obtain the phase difference data.

[0048] Among them, adjacent elements specifically refer to two phase values ​​that are immediately adjacent in the sampling time in the phase data. The time interval between the two is consistent with the signal sampling period, and they are the basic data pairs for calculating the phase difference. The phase difference is the difference between two adjacent phase values ​​in the phase data, and its magnitude is directly related to the displacement change of the measured object. The phase difference data is a set of data formed after uniformly processing the phase differences calculated from all adjacent phase values. This data must be limited to a preset phase interval and can be directly used to solve for the subsequent displacement increment.

[0049] In this embodiment of the present disclosure, after acquiring continuous phase data, each pair of adjacent phase elements in the phase data can be extracted sequentially according to the sampling time order, and recorded as the preceding phase value and the following phase value respectively. Then, the initial phase difference between the following phase value and the preceding phase value is calculated. To solve the problem of abnormal difference caused by phase jump, it is necessary to determine whether the initial phase difference is within the preset phase interval (-π, π). If it exceeds the interval, it is wrapped to limit the value to the interval. Finally, all processed phase differences are integrated according to the sampling order to form phase difference data that can be used for subsequent displacement calculation.

[0050] This technical step establishes a crucial link for converting phase information into displacement information by sequentially extracting adjacent phase values ​​and calculating the phase difference. The interval wrapping processing of the initial phase difference can effectively solve the problem of abnormal difference caused by phase jump, ensuring the rationality and continuity of the phase difference data. This provides high-quality input data for subsequent intelligent orientation algorithms, ensuring the accuracy of displacement increment calculation from the data level, and thus improving the continuity and measurement accuracy of the entire displacement measurement system.

[0051] Step 150: Calculate the displacement increment corresponding to each phase difference in the phase difference data using the intelligent direction identification algorithm, and accumulate the displacement increments to obtain the displacement measurement results corresponding to the four interference signal data.

[0052] Among them, the intelligent direction identification algorithm is a displacement direction and increment judgment algorithm based on the phase difference interval division. Its core is to divide the phase difference limit interval into four specific sub-intervals, and configure different displacement calculation logic for different sub-intervals, which can effectively solve the problem of direction judgment error caused by phase jump. The displacement increment is the small displacement change of the measured object corresponding to a single phase difference. It is obtained by combining the phase difference value with the optical wavelength parameter through specific calculations and is the basic unit of the total displacement. The displacement measurement result is the final total displacement data obtained by accumulating all displacement increments according to the sampling time sequence and correcting for drift compensation, which can intuitively reflect the overall displacement of the measured object.

[0053] In this embodiment of the present disclosure, after acquiring the phase difference data after interval wrapping processing, the target interval to which each phase difference belongs can be determined one by one according to the four preset phase sub-intervals ((-π, -0.6π), [-0.6π, 0), [0, 0.6π), [0.6π, +π)). Then, according to the displacement calculation strategy corresponding to each target interval, the phase difference value is subjected to targeted calculation in combination with the wavelength parameters of the He-Ne laser to obtain the displacement increment corresponding to each phase difference. Subsequently, the system will accumulate all displacement increments according to the sampling time sequence to form preliminary total displacement data. Finally, drift compensation correction is performed on the total displacement data to eliminate the cumulative error caused by long-term measurement, thereby obtaining the final displacement measurement result corresponding to the four-channel interference signal data.

[0054] This technical step, through the partitioning judgment and differentiated calculation strategy of the intelligent orientation algorithm, can effectively avoid the error in displacement direction judgment under phase jump conditions and ensure the accuracy of displacement increment calculation. The time-series accumulation of displacement increment can realize the complete integration of small displacement changes, while drift compensation correction can further offset the accumulated deviation in long-term measurement. The overall technical features form a precise conversion link from phase difference to final displacement, which can not only improve the orientation accuracy and data continuity of displacement measurement, but also enhance the stability of long-term measurement, and ultimately achieve high-precision and high-reliability displacement measurement results that meet the requirements of ultra-precision measurement.

[0055] In summary, the displacement measurement method based on four-channel interferometric signals provided by this invention involves differential processing of four-channel interferometric signal data with a preset phase relationship to obtain two orthogonal signal data, including a first sine signal data and a first cosine signal data; extreme value detection is performed on the two orthogonal signal data respectively using an adaptive update strategy to obtain extreme value detection results; based on the extreme value detection results, the two orthogonal signal data are sequentially subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process to obtain phase data; the phase difference between any two adjacent elements in the phase data is calculated to obtain phase difference data; and the displacement increment corresponding to each phase difference in the phase difference data is calculated using an intelligent direction discrimination algorithm, and the displacement increments are accumulated to obtain the displacement measurement result corresponding to the four-channel interferometric signal data. Among them, the differential processing of the four interference signals can effectively cancel common-mode interference such as power supply noise, temperature drift, and mechanical vibration, greatly improving the anti-interference capability of the measurement system and ensuring the purity of the signal in complex industrial environments. The adaptive extreme value update strategy can provide real-time and accurate extreme value references for subsequent normalization processing. Combined with the first normalization to eliminate DC bias and unify the dynamic range, and the orthogonal transformation and the second normalization to further optimize the orthogonal characteristics of the signal and ensure amplitude consistency, it can significantly improve the accuracy and stability of phase calculation. In addition, the intelligent direction identification algorithm based on phase difference interval division can effectively avoid errors in displacement direction judgment under rapid movement or vibration conditions of the measured object. Combined with the accurate calculation of phase difference and the accumulation of displacement increment, it can finally achieve nanometer-level ultra-precision displacement measurement, which fully meets the accuracy requirements of ultra-precision measurement scenarios such as semiconductor testing.

[0056] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, and to fully illustrate the implementation of this embodiment, this embodiment also provides another displacement measurement method based on four-way interferometric signals, such as... Figure 3 As shown, the method includes: Step 310: Perform differential processing on the four interference signal data with a preset phase relationship to obtain two orthogonal signal data, which include the first sine signal data and the first cosine signal data.

[0057] For embodiments of this disclosure, such as Figure 4 As shown, the steps in the embodiment may include: Step 310-1: Group the four interference signal data into pairs according to the preset phase relationship to determine the first signal group and the second signal group.

[0058] The length measurement digital signal calculation module reads four interference signals and records them as interference signal data. , , , These four signals are obtained through a special optical path design and have a specific phase relationship, namely... , , , Since there is a 90° phase difference between each signal, the four interferometric signal data can be paired and integrated, and the two signals whose phase correlation satisfies the conditions for generating a sinusoidal signal can be combined. , They are grouped into the first signal group, while the other two signals whose phase correlation meets the requirements for generating cosine signals are also grouped together. , It was divided into the second signal group.

[0059] Step 310-2: Perform a subtraction operation on the two signals in the first signal group to generate the first sine wave signal data.

[0060] For embodiments of this disclosure, the formula can be used. For the two signals in the first signal group , Perform the subtraction operation to obtain the first sinusoidal signal data. .

[0061] Step 310-3: Perform a subtraction operation on the two signals of the second signal group to generate the first cosine path signal data.

[0062] For embodiments of this disclosure, the formula can be used. For the two signals in the second signal group , Perform the subtraction operation to obtain the first cosine path signal data. .

[0063] Step 320: Perform extreme value detection on the two orthogonal signal data using an adaptive update strategy to obtain the extreme value detection results.

[0064] The extreme value detection results include the current maximum and minimum values ​​of the two orthogonal signal data.

[0065] In the embodiments of this disclosure, the first sinusoidal signal data can be detected. The current maximum value in Compared with the current minimum value and the first cosine path signal data The current maximum value in Compared with the current minimum value Furthermore, an adaptive update strategy is employed: First, the extreme values ​​of the first 200-300 data points are identified as initial values. Then, these extreme values ​​are compared in real-time with subsequent orthogonal signals, and the extreme values ​​are continuously updated using a sliding window approach. The update algorithm is as follows: if the new sampled value is greater than the current maximum value, the maximum value is updated; if the new sampled value is less than the current minimum value, the minimum value is updated. The extreme values ​​are periodically (e.g., every 1000 sample points) attenuated to prevent them from becoming fixed.

[0066] Accordingly, the implementation steps may include: extracting the first preset number of consecutive data points from the two orthogonal signal data, and filtering the current maximum and minimum values ​​of the two orthogonal signal data respectively from the consecutive data points; sequentially reading the subsequent new sampled data of the two orthogonal signal data in a sliding window of preset length; comparing each new sampled data with the current maximum and minimum values ​​of the corresponding orthogonal signal data in real time; if the new sampled data is greater than the current maximum value of the corresponding orthogonal signal data, updating the new sampled data to the current maximum value of the orthogonal signal data; if the new sampled data is less than the current minimum value of the corresponding orthogonal signal data, updating the new sampled data to the current minimum value of the orthogonal signal data; attenuating the current maximum and minimum values ​​of the two orthogonal signal data at intervals of a second preset number of sampling points, and finally forming an extreme value detection result containing the real-time dynamic maximum and minimum values ​​of the two signals.

[0067] The first preset quantity is the size of the initial data points used to determine the initial benchmark of the extreme value, corresponding to the sample size in the initial stage of the signal, which can ensure the statistical validity of the initial extreme value; the sliding window is the basic unit for reading subsequent new sampled data. Through this unit, the signal data can be monitored segment by segment continuously, providing data input for real-time updates of the extreme value; the new sampled data refers to the subsequent signal data continuously collected after the initial data point in the two orthogonal signal data, which has not participated in the initial extreme value calculation; the second preset quantity is the number of interval sampling points for performing extreme value attenuation processing, which is a key parameter for balancing the stability and flexibility of the extreme value; the attenuation processing is a fine-tuning operation performed on the current extreme value, which can avoid the problem that the extreme value is fixed for a long time and cannot adapt to signal drift, thus ensuring the dynamic adaptability of the extreme value.

[0068] Step 330: Call the current maximum and minimum values ​​of the first sine wave signal data and the first cosine wave signal data in the extreme value detection results, and perform four arithmetic operations on the two orthogonal signal data respectively to obtain the second sine wave signal data and the second cosine wave signal data after the first normalization process.

[0069] For embodiments of this disclosure, such as Figure 5 As shown, the steps in the embodiment may include: Step 330-1: Call the current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data, and perform arithmetic operations on each sample point element of the first sine wave signal data using the first normalization calculation formula to obtain the second sine wave signal data after the first normalization process.

[0070] The formula characteristics of the first normalization calculation formula are described as follows:

[0071] In the formula, This is the second sinusoidal signal data after the first normalization process.

[0072] Step 330-2: Call the current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data, and use the second normalization calculation formula to perform arithmetic operations on each sampling point element of the first cosine wave signal data to obtain the second cosine wave signal data after the first normalization process.

[0073] The formula characteristics of the second normalization calculation formula are described as follows:

[0074] In the formula, This is the second cosine path signal data after the first normalization process.

[0075] Step 340: Perform addition and subtraction operations on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, and detect the corresponding maximum values ​​in the third sine wave signal data and the third cosine wave signal data respectively.

[0076] In the embodiments of this disclosure, when performing addition and subtraction operations on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, as follows: Figure 6 As shown, the steps in the embodiment may include: Step 340-1: Perform an addition operation on the elements of the same sampling point in the second sine wave signal data and the second cosine wave signal data to obtain the third sine wave signal data after orthogonal transformation.

[0077] For the disclosed embodiments, the formula can be followed. The second sine wave signal data Second cosine path signal data Adding elements at the same sampling point yields the third sinusoidal signal data after orthogonal transformation. .

[0078] Step 340-2: Perform a subtraction operation on the elements of the same sampling point in the second sine signal data and the second cosine signal data to obtain the third cosine signal data after orthogonal transformation.

[0079] For the disclosed embodiments, the formula can be followed. The second sine wave signal data Second cosine path signal data Subtraction is performed on elements at the same sampling point to obtain the third cosine signal data after orthogonal transformation. .

[0080] Furthermore, the third sinusoidal signal data can be... and the third cosine path signal data The maximum value is detected in each of the following. and .

[0081] Step 350: Based on the maximum value, perform division operations on the third sine wave signal data and the third cosine wave signal data respectively to obtain the fourth sine wave signal data and the fourth cosine wave signal data after the second normalization process.

[0082] For embodiments of this disclosure, such as Figure 7 As shown, the steps in the embodiment may include: Step 350-1: Perform a division operation between each sample point element in the third sinusoidal signal data and the maximum value in the third sinusoidal signal data to obtain the fourth sinusoidal signal data after the second normalization process.

[0083] For the disclosed embodiments, the formula can be followed. The third sine wave signal data Each sample point element is compared with the maximum value in the third sinusoidal signal data. Performing a division operation yields the fourth sinusoidal signal data after the second normalization process. .

[0084] Step 350-2: Perform a division operation between each sampled element in the third cosine path signal data and the maximum value in the third cosine path signal data to obtain the fourth cosine path signal data after the second normalization process.

[0085] For embodiments of this disclosure, the formula can be used. The third cosine path signal data Each sampled element in the data is compared with the maximum value in the third cosine path signal data. Performing a division operation yields the fourth cosine path signal data after the second normalization process. .

[0086] Step 360: Perform phase calculation processing based on the fourth sine wave signal data and the fourth cosine wave signal data to obtain a continuous phase data sequence, which is used as phase data.

[0087] For embodiments of this disclosure, such as Figure 8 As shown, the steps in the embodiment may include: Step 360-1: Take the sampled element in the fourth sine signal data as the numerator and the same sampled element in the fourth cosine signal data as the denominator, and perform an arctangent operation to obtain the phase value corresponding to each sampled element.

[0088] For embodiments of this disclosure, the formula can be used. The fourth sine wave signal data The sampling point elements in the data are used as the numerator to represent the fourth cosine path signal data. The same sample point element is used as the denominator to perform an arctangent operation, and the phase value corresponding to each sample point element is obtained.

[0089] Step 360-2: Arrange all phase values ​​in order of sampling time to form a continuous phase data sequence, which is used as phase data.

[0090] Step 370: Calculate the phase difference between any two adjacent elements in the phase data to obtain the phase difference data.

[0091] For embodiments of this disclosure, phase data can be sequentially obtained. Read two adjacent elements from the data, and calculate the phase difference data by subtracting every two adjacent elements in the phase data θ according to the formula. : .

[0092] In specific application scenarios, to handle phase jump problems, the phase difference needs to be wrapped to ensure that the phase difference is within the range of (-π, π). Accordingly, as a preferred approach, the implementation steps may further include: sequentially extracting two adjacent phase elements from the phase data according to the sampling time sequence, denoted as the preceding phase value and the following phase value; calculating the initial phase difference between the following phase value and the preceding phase value; determining whether the initial phase difference is within a preset phase interval; if so, determining the initial phase difference as the phase difference between two adjacent phase elements; if not, wrapping the initial phase difference to obtain a phase difference within the preset phase interval. The preset phase interval can be (-π, π).

[0093] The initial phase difference wrapping process relies on the periodicity of phase 2π to calibrate initial phase differences exceeding the preset interval (-π, π) to within that interval. Specifically, the process involves first calculating the initial phase difference between two adjacent phase values ​​in the phase data. Then judge The range of values, if If it is greater than π, then execute it. The operation, if If less than -π, then execute. The operation, if If the phase difference is within the (-π, π) interval, no additional processing is required. The final effective phase difference is obtained within the preset phase interval, thereby eliminating the abnormal difference caused by phase jump, ensuring the continuity and physical rationality of the phase difference data, and providing reliable data for subsequent intelligent orientation identification and displacement increment calculation.

[0094] Step 380: Calculate the displacement increment corresponding to each phase difference in the phase difference data using the intelligent direction identification algorithm, and accumulate the displacement increments to obtain the displacement measurement results corresponding to the four interference signal data.

[0095] For embodiments of this disclosure, such as Figure 9 As shown, the steps in the embodiment may include: Step 380-1: Based on the preset phase interval, determine the target phase interval where each phase difference in the phase difference data is located. The preset phase interval includes (-π, -0.6π), [-0.6π, 0), [0, 0.6π), and [0.6π, +π). Different preset phase intervals are configured with different displacement calculation strategies.

[0096] For the embodiments of this disclosure, when the target phase interval containing the phase difference is (-π, -0.6π), the displacement calculation strategy between the two sampling points is as follows: .

[0097] When the target phase interval containing the phase difference is [-0.6π, 0), the displacement calculation strategy between the two sampling points is as follows: .

[0098] When the target phase interval containing the phase difference is [0, 0.6π), the displacement calculation strategy between the two sampling points is as follows: .

[0099] When the target phase interval containing the phase difference is [0.6π, +π), the displacement calculation strategy between the two sampling points is as follows: .

[0100] Where 633 is the wavelength (nm) of the He-Ne laser.

[0101] Step 380-2: Based on the displacement calculation strategy configured for the target phase interval, calculate the displacement increment corresponding to each phase difference in the phase difference data.

[0102] Step 380-3: Accumulate all displacement increments in the order of sampling time to obtain the total displacement data.

[0103] For embodiments of this disclosure, all displacement increments are processed in chronological order of sampling time. The total displacement data X is obtained by performing cumulative calculations. The corresponding calculation formula is: In the formula, n is the number of displacement increments.

[0104] Step 380-4: Perform drift compensation correction on the total displacement data and output the corrected displacement measurement results.

[0105] Among them, drift compensation correction is a special calibration mechanism for the cumulative drift error in the total displacement data. Its core is to identify and offset the displacement deviation caused by environmental fluctuations, device aging and other factors in long-term measurement through a preset calibration strategy, so as to ensure the accuracy of displacement data.

[0106] In the embodiments of this disclosure, after obtaining the displacement increment corresponding to each phase difference through the intelligent orientation algorithm and accumulating all displacement increments according to the sampling time sequence to form the total displacement data, the system will activate the preset drift compensation correction mechanism. First, by periodically collecting reference displacement data or establishing a drift error model, the system identifies the cumulative drift deviation in the total displacement data caused by long-term measurement. Then, based on the characteristics and magnitude of the deviation, the system performs targeted correction calculations on the total displacement data to offset the displacement error caused by drift. Finally, the system outputs a calibrated displacement measurement result that can accurately reflect the actual displacement of the measured object.

[0107] This technical step effectively eliminates the cumulative drift error caused by environmental interference, device performance fluctuations, and other factors during long-term measurement by performing drift compensation correction on the total displacement data. It compensates for the accuracy shortcomings of the displacement increment accumulation stage. Its correction mechanism can achieve accurate calibration of displacement data, which can not only ensure the long-term stability of displacement measurement results, but also further improve the accuracy and reliability of the overall measurement data. It provides key accuracy assurance for long-term continuous displacement monitoring in ultra-precision measurement scenarios and enhances the comprehensive performance of the entire displacement measurement system.

[0108] In summary, the technical solution in this application, by integrating a series of technical features such as differential processing of four-channel interferometric signals, adaptive extreme value update, dual normalization, quadratic orthogonal transformation, phase difference packaging, intelligent partitioning and orientation identification, and drift compensation, can achieve a significant improvement in displacement measurement performance. Specifically, the grouped differential processing of the four signals can effectively cancel common-mode noise and DC bias, enhancing the system's anti-interference capability from the signal source; the adaptive extreme value update strategy can dynamically calibrate signal extreme values ​​in real time, providing accurate reference for subsequent normalization. Combined with the first arithmetic operation normalization to eliminate the DC component of the signal and unify the dynamic range, and the second orthogonal transformation and the second division normalization, the orthogonal characteristics and amplitude consistency of the signal are further optimized, significantly reducing the error of phase calculation; the interval wrapping processing of phase difference can solve the phase jump problem, while the intelligent direction identification strategy based on the four-quadrant phase difference interval can accurately avoid direction judgment errors under rapid movement or vibration conditions; the drift compensation correction after displacement accumulation further ensures the stability of long-term measurement. Ultimately, it can realize nanometer-level ultra-precision displacement measurement with high anti-interference, high calculation accuracy, high direction identification accuracy and long-term stability, which can fully meet the application needs of high-end precision measurement scenarios such as semiconductor testing.

[0109] Furthermore, as Figure 1 and Figure 3 The specific implementation of the method shown in this embodiment provides a displacement measurement device based on four-channel interference signals, such as... Figure 10 As shown, the device includes: a first processing module 41, a detection module 42, a second processing module 43, a first calculation module 44, and a second calculation module 45; The first processing module 41 can be used to perform differential processing on four interference signal data with a preset phase relationship to obtain two orthogonal signal data, the two orthogonal signal data including a first sine signal data and a first cosine signal data; The detection module 42 can be used to perform extreme value detection on two orthogonal signal data respectively through an adaptive update strategy to obtain extreme value detection results; The second processing module 43 can be used to perform a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process on two orthogonal signal data based on the extreme value detection results to obtain phase data. The first calculation module 44 can be used to calculate the phase difference between any two adjacent elements in the phase data to obtain the phase difference data. The second calculation module 45 can be used to calculate the displacement increment corresponding to each phase difference in the phase difference data through an intelligent direction discrimination algorithm, and accumulate the displacement increments to obtain the displacement measurement results corresponding to the four interference signal data.

[0110] In some embodiments of this application, the first processing module 41 can be specifically used to: group the four interference signal data into pairs according to a preset phase relationship to determine the first signal group and the second signal group; perform a subtraction operation on the two signals of the first signal group to generate the first sine signal data; and perform a subtraction operation on the two signals of the second signal group to generate the first cosine signal data.

[0111] In some embodiments of this application, the extreme value detection result includes the current maximum and current minimum values ​​of the two orthogonal signal data respectively; the detection module 42 is specifically used to extract the first preset number of continuous data points from the two orthogonal signal data, and filter the current maximum and current minimum values ​​of the two orthogonal signal data respectively from the continuous data points; read the subsequent new sampled data of the two orthogonal signal data in sequence in a sliding window of preset length; compare each new sampled data with the current maximum and current minimum values ​​of the corresponding orthogonal signal data in real time; if the new sampled data is greater than the current maximum value of the corresponding orthogonal signal data, update the new sampled data to the current maximum value of the orthogonal signal data; if the new sampled data is less than the current minimum value of the corresponding orthogonal signal data, update the new sampled data to the current minimum value of the orthogonal signal data; and attenuate the current maximum and current minimum values ​​of the two orthogonal signal data every second preset number of sampling points.

[0112] In some embodiments of this application, the second processing module 43 can be specifically used to: call the current maximum and minimum values ​​of the first sine wave signal data and the first cosine wave signal data in the extreme value detection result, perform arithmetic operations on the two orthogonal signal data respectively, and obtain the second sine wave signal data and the second cosine wave signal data after the first normalization processing; perform addition and subtraction operations on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, and detect the corresponding maximum values ​​in the third sine wave signal data and the third cosine wave signal data respectively; perform division operations on the third sine wave signal data and the third cosine wave signal data based on the maximum values, and obtain the fourth sine wave signal data and the fourth cosine wave signal data after the second normalization processing; perform phase calculation processing based on the fourth sine wave signal data and the fourth cosine wave signal data to obtain a continuous phase data sequence as phase data.

[0113] In some embodiments of this application, the second processing module 43 can be specifically used to: call the current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data, and perform arithmetic operations on each sampling point element of the first sine wave signal data using the first normalization calculation formula to obtain the second sine wave signal data after the first normalization processing; call the current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data, and perform arithmetic operations on each sampling point element of the first cosine wave signal data using the second normalization calculation formula to obtain the second cosine wave signal data after the first normalization processing.

[0114] In some embodiments of this application, the second processing module 43 can be specifically used to: perform addition operations on the same sampling point elements in the second sine signal data and the second cosine signal data to obtain the third sine signal data after orthogonal transformation processing; and perform subtraction operations on the same sampling point elements in the second sine signal data and the second cosine signal data to obtain the third cosine signal data after orthogonal transformation processing.

[0115] In some embodiments of this application, the second processing module 43 can be specifically used to: perform a division operation between each sampling point element in the third sine wave signal data and the maximum value in the third sine wave signal data to obtain the fourth sine wave signal data after the second normalization process; and perform a division operation between each sampling point element in the third cosine wave signal data and the maximum value in the third cosine wave signal data to obtain the fourth cosine wave signal data after the second normalization process.

[0116] In some embodiments of this application, the second processing module 43 can be specifically used to: take the sampling point element in the fourth sine signal data as the numerator and the same sampling point element in the fourth cosine signal data as the denominator to perform an arctangent operation to obtain the phase value corresponding to each sampling point element; arrange all phase values ​​in the order of sampling time to form a continuous phase data sequence as phase data.

[0117] In some embodiments of this application, the first calculation module 44 can be specifically used to: extract two adjacent phase elements from the phase data in the order of sampling time, and record them as the preceding phase value and the following phase value; calculate the initial phase difference between the following phase value and the preceding phase value; determine whether the initial phase difference is within a preset phase interval; if so, determine the initial phase difference as the phase difference between two adjacent phase elements; if not, perform a wrapping process on the initial phase difference to obtain a phase difference within the preset phase interval.

[0118] In some embodiments of this application, the second calculation module 45 can be specifically used for: determining the target phase interval where each phase difference in the phase difference data is located based on a preset phase interval, wherein the preset phase interval includes (-π, -0.6π), [-0.6π, 0), [0, 0.6π), [0.6π, +π), and different preset phase intervals are configured with different displacement calculation strategies; calculating the displacement increment corresponding to each phase difference in the phase difference data based on the displacement calculation strategy configured for the target phase interval; accumulating all displacement increments in the order of sampling time to obtain the total displacement data; performing drift compensation correction on the total displacement data, and outputting the corrected displacement measurement result.

[0119] It should be noted that other corresponding descriptions of the functional units involved in the displacement measurement device based on four-channel interferometric signals provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 3 The corresponding descriptions in [the document] will not be repeated here.

[0120] Based on the above, Figure 1 and Figure 3 Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method. Figure 1 and Figure 3 The displacement measurement method based on four-channel interferometric signals is shown.

[0121] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause an electronic device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0122] Based on the above, Figure 1 and Figure 3 The method shown, and Figure 10 To achieve the above objectives, the present application also provides an electronic device, specifically a personal computer, tablet computer, server, or other network device, as shown in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figure 1 and Figure 3 The displacement measurement method based on four-channel interferometric signals is shown.

[0123] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0124] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0125] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0126] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware.

[0127] This invention effectively cancels common-mode interference such as power supply noise, temperature drift, and mechanical vibration through differential processing of four interference signals, significantly improving the anti-interference capability of the measurement system and ensuring signal purity in complex industrial environments. The adaptive extreme value update strategy provides real-time and accurate extreme value references for subsequent normalization processing. Combined with the first normalization to eliminate DC bias and unify the dynamic range, and the orthogonal transformation and second normalization to further optimize the orthogonal characteristics of the signal and ensure amplitude consistency, the accuracy and stability of phase calculation can be significantly improved. In addition, the intelligent direction identification algorithm based on phase difference interval division can effectively avoid errors in displacement direction judgment under rapid movement or vibration conditions of the measured object. Combined with accurate calculation of phase difference and accumulation of displacement increment, nanometer-level ultra-precision displacement measurement is finally achieved, fully meeting the accuracy requirements of ultra-precision measurement scenarios such as semiconductor testing.

[0128] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0129] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A displacement measurement method based on four-channel interferometric signals, characterized in that, include: Differential processing is performed on four interference signal data with a preset phase relationship to obtain two orthogonal signal data, which include a first sine signal data and a first cosine signal data. An adaptive update strategy is used to perform extreme value detection on the two orthogonal signal data respectively to obtain the extreme value detection results; Based on the extreme value detection results, the two orthogonal signal data are subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process in sequence to obtain phase data; Calculate the phase difference between any two adjacent elements in the phase data to obtain the phase difference data; The displacement increment corresponding to each phase difference in the phase difference data is calculated by an intelligent direction discrimination algorithm, and the displacement increments are accumulated to obtain the displacement measurement results corresponding to the four interference signal data.

2. The method according to claim 1, characterized in that, The differential processing of four interferometric signal data with a preset phase relationship to obtain two orthogonal signal data includes: The four interference signal data are grouped into pairs according to a preset phase relationship to determine the first signal group and the second signal group; Perform a subtraction operation on the two signals in the first signal group to generate the first sinusoidal signal data; The two signals in the second signal group are subtracted to generate the first cosine path signal data.

3. The method according to claim 1, characterized in that, The extreme value detection result includes the current maximum value and the current minimum value of each of the two orthogonal signal data; An adaptive update strategy is used to perform extreme value detection on the two orthogonal signal data respectively, and the extreme value detection results are obtained, including: Extract the first preset number of consecutive data points from the two orthogonal signal data, and then filter the current maximum and current minimum values ​​of the two orthogonal signal data respectively from the consecutive data points; Using a sliding window of preset length as a unit, read the subsequent new sampled data of the two orthogonal signal data in sequence; Each new sampled data is compared in real time with the current maximum and minimum values ​​of the corresponding orthogonal signal data. If the new sampled data is greater than the current maximum value of the corresponding quadrature signal data, then the new sampled data is updated to the current maximum value of the quadrature signal data; if the new sampled data is less than the current minimum value of the corresponding quadrature signal data, then the new sampled data is updated to the current minimum value of the quadrature signal data. Every second preset number of sampling points, the current maximum and minimum values ​​of the two orthogonal signal data are attenuated.

4. The method according to claim 1, characterized in that, Based on the extreme value detection results, the two orthogonal signal data are sequentially subjected to a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process to obtain phase data, including: The current maximum and minimum values ​​of the first sine path signal data and the first cosine path signal data in the extreme value detection result are called, and four arithmetic operations are performed on the two orthogonal signal data respectively to obtain the second sine path signal data and the second cosine path signal data after the first normalization process. Addition and subtraction operations are performed on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation processing, and the corresponding maximum values ​​are detected in the third sine wave signal data and the third cosine wave signal data respectively. Based on the maximum value, division operations are performed on the third sine path signal data and the third cosine path signal data respectively to obtain the fourth sine path signal data and the fourth cosine path signal data after the second normalization process. Phase calculation processing is performed based on the fourth sine wave signal data and the fourth cosine wave signal data to obtain a continuous phase data sequence, which is used as the phase data.

5. The method according to claim 4, characterized in that, The process involves calling the current maximum and minimum values ​​of the first sine wave signal data and the first cosine wave signal data from the extreme value detection results, performing arithmetic operations on the two orthogonal signal data respectively, and obtaining the second sine wave signal data and the second cosine wave signal data after the first normalization process, including: The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called, and the four arithmetic operations are performed on each sample point element of the first sine wave signal data using the first normalization calculation formula to obtain the second sine wave signal data after the first normalization process. The current maximum and minimum values ​​of the first sine wave signal data and the current maximum and minimum values ​​of the first cosine wave signal data are called. The four arithmetic operations are performed on each sample point element of the first cosine wave signal data using the second normalization calculation formula to obtain the second cosine wave signal data after the first normalization process.

6. The method according to claim 4, characterized in that, Addition and subtraction operations are performed on the second sine wave signal data and the second cosine wave signal data to generate the third sine wave signal data and the third cosine wave signal data after orthogonal transformation, including: Perform an addition operation on the elements of the same sampling point in the second sinusoidal signal data and the second cosine signal data to obtain the third sinusoidal signal data after orthogonal transformation; Subtract the elements at the same sampling point in the second sine signal data and the second cosine signal data to obtain the third cosine signal data after orthogonal transformation.

7. The method according to claim 4, characterized in that, Based on the maximum value, division operations are performed on the third sine wave signal data and the third cosine wave signal data respectively to obtain the fourth sine wave signal data and the fourth cosine wave signal data after the second normalization process, including: Each element of the third sinusoidal signal data is divided by the maximum value in the third sinusoidal signal data to obtain the fourth sinusoidal signal data after the second normalization process. Each sample point element in the third cosine path signal data is divided by the maximum value in the third cosine path signal data to obtain the fourth cosine path signal data after the second normalization process.

8. A displacement measuring device based on four-channel interferometric signals, characterized in that, include: The first processing module is used to perform differential processing on four interference signal data with a preset phase relationship to obtain two orthogonal signal data, the two orthogonal signal data including a first sine signal data and a first cosine signal data; The detection module is used to perform extreme value detection on the two orthogonal signal data respectively through an adaptive update strategy to obtain extreme value detection results; The second processing module is used to perform a first normalization process, an orthogonal transformation process, a second normalization process, and a phase calculation process on the two orthogonal signal data based on the extreme value detection result, so as to obtain phase data. The first calculation module is used to calculate the phase difference between any two adjacent elements in the phase data to obtain phase difference data. The second calculation module is used to calculate the displacement increment corresponding to each phase difference in the phase difference data through an intelligent direction discrimination algorithm, and to accumulate the displacement increments to obtain the displacement measurement results corresponding to the four interference signal data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

Images