Baseline correction and noise suppression method for magnetic mass spectrometry secondary ion signals

Abstract

The application discloses a baseline correction and noise suppression method for magnetic mass spectrum secondary ion signals and relates to the technical field of mass spectrum analysis signal processing. The method sequentially completes raw signal collection, standardization preprocessing, baseline feature identification, differential baseline correction, noise feature classification extraction and hierarchical noise suppression, and finally outputs a high-precision signal after verification. The preprocessing eliminates invalid data and normalizes the signal timing, the global scanning identifies different baseline change sections and performs adaptive correction, and the multiple types of noise are accurately distinguished and targetedly suppressed. The application solves the problems of low baseline correction precision, poor noise suppression adaptability and easy loss of effective signals of the traditional method, effectively improves the signal noise ratio and the reference stability, completely retains the ion signal features, and is suitable for high-precision magnetic mass spectrum analysis operations in multiple fields such as semiconductor detection, geological analysis and environmental monitoring.

Description

Technical Field

[0001] This invention relates to the field of mass spectrometry analysis signal processing technology, specifically to a method for baseline correction and noise suppression of secondary ion signals in magnetic mass spectrometry. Background Technology

[0002] Magnetic mass spectrometry (MMS) is a core technology for high-precision inorganic element and isotope detection, widely used in materials science, geological exploration, environmental monitoring, and semiconductor testing, among other high-end fields. The accuracy of secondary ion signal acquisition directly determines the accuracy of qualitative and quantitative analysis results. In actual testing operations, MMS equipment is susceptible to various factors, including fluctuations in its hardware, changes in power supply, unstable vacuum environment, and external electromagnetic interference. Consequently, the acquired primary secondary ion signals commonly exhibit baseline drift and various noise interference issues.

[0003] Existing conventional signal processing methods mostly employ single, fixed correction and filtering modes, which cannot accurately identify the dynamic changes in the signal baseline and are ill-suited to different types of baseline shifts, such as smooth drift and sudden fluctuations. Significant residual baseline biases remain even after correction. Furthermore, traditional methods cannot effectively distinguish between random, systematic, and environmentally correlated noise in the signal. Noise suppression processes easily lead to the loss of effective characteristic peaks in the ion signal, reducing signal integrity and discernibility. In addition, existing processing workflows lack standardized data preprocessing steps; invalid data and temporal discrepancies in the raw data further amplify processing errors, failing to meet the stringent requirements of high-precision magnetic mass spectrometry (MMS) analysis for signal purity and stability. This severely limits the application of MMS in ultra-high-precision analytical scenarios. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry, which achieves accurate calibration of the signal baseline and efficient removal of various types of noise, while fully preserving the effective characteristics of the ion signal and improving the accuracy and reliability of magnetic mass spectrometry analysis results.

[0005] To address the aforementioned technical problems, the present invention proposes the following technical solution: a baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry, which is executed sequentially according to the following steps: Step 1: Collect complete raw secondary ion signal data generated during the operation of the magnetic mass spectrometry analysis equipment; Step 2: Perform standardized preprocessing on the acquired raw secondary ion signal data to remove interference data and regulate the signal structure; Step 3: Based on the preprocessed signal data, perform a full-domain scan to identify the overall distribution pattern and local variation characteristics of the signal baseline; Step 4: Perform targeted baseline correction processing based on the identified baseline characteristics to eliminate signal deviation caused by baseline drift; Step 5: Extract various noise features contained in the baseline-corrected signal and classify the noise type; Step 6: Perform noise suppression processing by matching the noise type and the signal's own attributes, and finally output high-precision secondary ion signal data after processing.

[0006] Furthermore, the standardized preprocessing described in step two includes intelligent removal of anomalous and invalid data from the original signal, as well as uniformly regulating the signal acquisition sequence and data arrangement. This eliminates format differences and temporal disorder issues arising during data acquisition, providing standardized and uniform basic data for subsequent baseline identification and correction. The temporal regulation of the original signal data follows the detection sequence of the magnetic mass spectrometry equipment, uniformly adjusting the arrangement order of the signal data and the correspondence between acquisition intervals to eliminate interference caused by data misalignment and repeated acquisitions, ensuring that the preprocessed data completely matches the actual detection process.

[0007] Furthermore, in step three, the identification of the distribution pattern and change characteristics of the signal baseline is carried out by using a combination of full-domain continuous scanning and local feature analysis to distinguish between the stable sections, slowly drifting sections and sudden fluctuation sections of the signal baseline, accurately locate the position and degree of baseline shift, and complete feature marking.

[0008] Furthermore, the baseline correction process described in step four performs differentiated offset correction on signals in different segments based on the marked baseline offset characteristics. For stable segments, the baseline reference is maintained, while for drifting and fluctuating segments, the signal reference value is gradually calibrated, comprehensively eliminating the impact of overall baseline offset and local fluctuations on ion signal detection results. During the baseline offset correction process, the dynamic changes of the signal baseline are monitored in real time, and the correction amplitude is adaptively adjusted according to the real-time fluctuation state of the baseline during signal detection, ensuring the accuracy and adaptability of the baseline correction and accommodating baseline changes under different detection conditions.

[0009] Furthermore, in step five, noise feature extraction and classification distinguish between random noise that appears irregularly in the signal, systematic inherent noise generated by equipment operation, and associated noise caused by external environmental interference. The amplitude characteristics and distribution patterns of each type of noise are extracted and the classification and calibration are completed.

[0010] Furthermore, the noise suppression process described in step six combines the calibrated noise type with the distribution characteristics of the effective signal components, and adopts a hierarchical and layered suppression method. While preserving the effective characteristic peaks and key data of the ion signal, it specifically weakens the interference intensity of different types of noise, avoiding the loss of effective signal information during the suppression process.

[0011] Furthermore, before outputting the final high-precision secondary ion signal data, a comprehensive validity and integrity check is performed on the signal data that has undergone baseline correction and noise suppression. After confirming that the effective features of the signal are complete and without missing data and without residual interference data, the final data output is completed.

[0012] The advantages of this invention compared to the prior art are: First, this method constructs a standardized signal processing system for the entire process. Through intelligent removal of invalid data and preprocessing operations that regulate the timing structure, it completely eliminates format disorder and invalid interference in the original data, providing high-quality basic data for baseline correction and noise suppression, thereby improving the overall accuracy and stability of signal processing from the source.

[0013] Secondly, this invention adopts a baseline feature recognition method that combines full-domain scanning with local analysis, which can accurately distinguish various types of baseline change segments and perform differentiated adaptive correction, adapting to the dynamic drift and sudden fluctuations of the baseline in real time, and completely solving the problem of poor adaptability and large residual correction deviation of traditional fixed correction methods.

[0014] Third, this invention achieves accurate classification and hierarchical suppression of various types of noise, and can specifically distinguish between random noise, systematic noise and environmental noise. It optimizes the suppression strategy by combining the distribution characteristics of the effective components of the signal, so as to maximize the reduction of various interference noises while fully preserving the characteristic peaks and key effective information of the ion signal. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent changes and modifications made based on the technical essence of the present invention should be covered within the scope of protection of the present invention.

[0016] Example 1 This embodiment is applied to the secondary ion signal processing of magnetic mass spectrometry in the scenario of high-purity element detection of semiconductor materials. The specific implementation steps are as follows: First, start the magnetic mass spectrometry analysis equipment to complete the ion bombardment detection of the semiconductor sample, and collect the complete data of the original secondary ion signal generated throughout the operation of the equipment, covering the ion intensity time series data of the entire sample detection period.

[0017] Subsequently, standardized preprocessing operations are performed to intelligently identify and remove blank invalid data caused by momentary power failure of the equipment and abnormal mutation data caused by sudden external interference in the original signal. Following the element detection sequence of the magnetic mass spectrometer, the arrangement order of the signal data is uniformly and regularly arranged, the correspondence of the data acquisition interval is calibrated, and the structural interference caused by data misalignment and repeated acquisition is eliminated, resulting in basic signal data with uniform format and regular timing.

[0018] The preprocessed signal data is continuously scanned across the entire domain. Simultaneously, the features of local segments are refined and analyzed to accurately divide the signal baseline into stable segments, slowly drifting segments, and sudden fluctuation segments. The starting position and baseline offset of each offset segment are marked to complete the accurate identification of all baseline features.

[0019] Differential correction is performed based on the characteristics of the marked baseline. The baseline reference value is kept constant in the stable section. The baseline offset is gradually calibrated in the slowly drifting section using a progressive reference correction method. The local baseline abrupt change is eliminated in the sudden fluctuation section. The dynamic changes of the baseline are monitored in real time during the correction process, and the correction amplitude is adaptively adjusted to completely eliminate the signal deviation caused by the baseline drift.

[0020] Noise features were extracted from the corrected signal to distinguish between systematic noise generated by the operation of the equipment hardware and associated noise from the laboratory electromagnetic environment, and the amplitude range and distribution pattern of each type of noise were calibrated. Based on the noise classification results, a hierarchical suppression process was adopted, prioritizing the reduction of high-intensity systematic noise, then filtering environmental associated noise, and finally smoothing low-amplitude random noise, while preserving the characteristic peak structure of the semiconductor element ion signal throughout the process to avoid loss of effective information.

[0021] After noise suppression is completed, the integrity and validity of the signal data are verified. Once the elemental characteristic signals are confirmed to be complete, without missing or residual interference data, high-precision secondary ion signal data adapted for semiconductor high-purity element detection is output for subsequent elemental qualitative and quantitative analysis.

[0022] Example 2 This embodiment is applied to the secondary ion signal processing of magnetic mass spectrometry in the scenario of isotope detection of geological rock samples. The specific implementation steps are as follows: the magnetic mass spectrometry equipment is operated to complete the secondary ion excitation detection of geological rock samples, and the raw isotope ion signal data output by the equipment is collected at all times, covering the intensity change data of multiple isotope ions.

[0023] Standardized preprocessing operations are carried out to automatically screen and remove invalid data and abnormal jump data caused by fluctuations in the sample vacuum chamber in the original data. The signal data arrangement is reconstructed strictly according to the working sequence of magnetic mass spectrometry isotope detection. Data acquisition standards and format specifications are unified, data timing misalignment problems are corrected, and the preprocessed data is fully matched with the actual detection process, thus building a standardized signal processing foundation.

[0024] The baseline features are identified by combining full-domain traversal scanning with local feature extraction. This distinguishes between long-term stable baseline segments, slow-drift segments, and sudden fluctuation segments caused by detection switching. All baseline offset areas are accurately located, and the baseline change characteristics of each area are quantified and marked, providing a precise basis for correction processing.

[0025] Adaptive dynamic baseline correction is performed. For stable sections, the baseline benchmark is locked to ensure signal stability. For slow drift sections, a continuous smooth correction method is used to eliminate the gradual deviation of the baseline. For fluctuating sections during detection switching, rapid orientation correction is performed. The correction parameters are adjusted in real time to synchronize with the dynamic changes of the baseline. This adapts to the long-term baseline drift characteristics in geological sample detection and completely eliminates the impact of baseline deviation on isotope detection.

[0026] The corrected signal undergoes full-dimensional noise feature extraction to distinguish between random noise caused by environmental vibration, systematic noise generated by the equipment's vacuum system, and associated noise caused by external radiation interference, clarifying the distribution characteristics and interference intensity of each type of noise. A hierarchical noise suppression strategy is employed, optimizing suppression parameters based on the characteristic distribution of isotope ion signals to precisely weaken various noise interferences, fully preserve the characteristic peaks and abundance information of isotope ions, and eliminate signal distortion.

[0027] The processed signals undergo comprehensive quality verification to check the integrity of isotopic signal characteristics and the validity of data. After confirming that there are no interference residues, high-precision isotopic secondary ion signal data are output to support the accurate analysis of isotopic ratios in geological samples.

[0028] Example 3 This embodiment is applied to the secondary ion signal processing of magnetic mass spectrometry in the scenario of detecting trace pollutants in environmental water bodies. The specific implementation steps are as follows: run the magnetic mass spectrometry detection equipment to detect trace pollutant ions in environmental water samples, and collect the original secondary ion signal data throughout the entire detection cycle, covering the weak signal data of low-concentration pollutant ions.

[0029] Standardized preprocessing is implemented to intelligently remove blank data and low-amplitude abnormal interference data caused by fluctuations in the sampling system from the original signal. The signal data structure is organized according to the time sequence of water sample detection, and the data layout format and the correspondence between the acquisition time sequence are unified to eliminate data redundancy and misalignment. This enhances the basic data quality of weak pollutant signals and prevents weak signals from being masked by invalid data.

[0030] High-precision full-domain baseline scanning is performed on the preprocessed weak signals. Combined with local subtle feature analysis, it accurately identifies low-amplitude stable baselines, slowly drifting baselines, and slightly fluctuating baselines. It also finely marks the position and amount of tiny baseline offsets, adapting to the characteristics of subtle baseline changes in trace pollutant detection.

[0031] We conduct refined baseline correction processing. For low-amplitude stable baselines, we maintain the reference stability. For slowly drifting baselines, we use a micro-amplitude progressive correction method to eliminate small offsets. For baselines with slight fluctuations, we perform precise orientation calibration. During the correction process, we dynamically adapt to subtle changes in the baseline, minimize the impact of correction operations on weak pollutant signals, and accurately eliminate baseline drift interference.

[0032] Noise features are extracted from weak signals to distinguish between systematic micro-noise caused by low-amplitude random noise from environmental airflow and systemic micro-noise generated by the operation of equipment circuits, as well as associated noise from external electromagnetic radiation. The characteristic parameters of various types of weak noise are precisely calibrated. A targeted, hierarchical noise suppression method is adopted, focusing on preserving the weak characteristic peaks of trace pollutant ions to specifically weaken various types of weak noise interference, improve the signal-to-noise ratio of weak signals, and prevent effective weak signals from being masked by noise.

[0033] After processing, the signal is validated with high precision to confirm that the trace pollutant ion signal is complete, identifiable, and free from noise residue and baseline deviation. High-precision secondary ion signal data is then output to meet the needs of accurate detection of ultra-trace pollutants in environmental water bodies.

[0034] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for baseline correction and noise suppression of secondary ion signals in magnetic mass spectrometry, characterized in that, Follow these steps in sequence: Step 1: Collect complete data of raw secondary ion signals generated during the operation of the magnetic mass spectrometry analysis equipment; Step 2 involves standardizing the acquired raw secondary ion signal data to remove interference data and regularize the signal structure. Step 3: Based on the preprocessed signal data, perform a full-domain scan to identify the overall distribution pattern and local variation characteristics of the signal baseline; Step four involves performing targeted baseline correction based on the identified baseline features to eliminate signal deviation caused by baseline drift. Step 5: Extract various noise features from the baseline-corrected signal and classify the noise type. Step six involves using a matching processing method based on the noise type and the signal's own properties to complete noise suppression processing, ultimately outputting high-precision secondary ion signal data after processing.

2. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: The standardization preprocessing described in step two includes intelligent removal of abnormal and invalid data from the original signal, as well as uniform and regularized signal acquisition timing and data arrangement to eliminate format differences and timing disorder problems generated during data acquisition, providing standardized and uniform basic data for subsequent baseline identification and correction processing.

3. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: In step three, the identification of the distribution pattern and change characteristics of the signal baseline is carried out by combining full-domain continuous scanning with local feature analysis to distinguish the stable section, slowly drifting section and sudden fluctuation section of the signal baseline, accurately locate the position and degree of baseline shift and complete feature marking.

4. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: The baseline correction process described in step four performs differentiated offset correction on signals in different segments based on the marked baseline offset characteristics. It maintains baseline stability in stable segments and gradually calibrates signal reference values ​​in drifting and fluctuating segments, thereby completely eliminating the impact of overall baseline offset and local fluctuations on ion signal detection results.

5. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: In step five, noise feature extraction and classification are performed to distinguish between random noise that appears irregularly in the signal, systematic inherent noise generated by equipment operation, and associated noise caused by external environmental interference. The amplitude characteristics and distribution patterns of each type of noise are extracted and the classification and calibration are completed.

6. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: The noise suppression process described in step six combines the calibrated noise type with the distribution characteristics of the effective signal components. It adopts a hierarchical and layered suppression method to reduce the interference intensity of different types of noise while preserving the effective characteristic peaks and key data of the ion signal, thereby avoiding the loss of effective signal information during the suppression process.

7. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 2, characterized in that: The timing regularization of the raw signal data follows the detection timing of the magnetic mass spectrometer, uniformly adjusting the arrangement order of the signal data and the corresponding acquisition interval to eliminate interference caused by data misalignment and repeated acquisition, ensuring that the preprocessed data is completely matched with the actual detection process.

8. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 4, characterized in that: During the baseline offset correction process, the dynamic changes of the signal baseline are monitored in real time. The correction amplitude is adaptively adjusted according to the real-time fluctuation of the baseline during signal detection to ensure the accuracy and adaptability of the baseline correction and to adapt to the baseline changes under different detection conditions.

9. The baseline correction and noise suppression method for secondary ion signals in magnetic mass spectrometry according to claim 1, characterized in that: Before outputting the final high-precision secondary ion signal data, a comprehensive validity and integrity check is performed on the signal data after baseline correction and noise suppression. The final data output is completed only after confirming that the effective features of the signal are complete and without missing data and without residual interference data.