How to autotune one or more sensors

Abstract

A method for automatically calibrating a sensor for a residual gas analyzer includes measuring a known sample with the sensor and obtaining data measurements at a first mass unit. A peak model function is fitted to the data to determine the presence of a peak. The peak model function is matched to the data using dynamic time warping to determine peak characteristics. It is determined whether the peak center location and peak width are within a predetermined tolerance, and if so, the previous steps are performed for the next mass unit. If the peak center location or peak width at the first mass unit is outside the predetermined tolerance, sensor parameters are adjusted and another iteration of the calibration is performed. A signal is emitted after a maximum number of iterations are performed and the peak center and width are outside the predetermined tolerance.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 397,471, entitled "METHOD OF AUTO TUNING ONE OR MORE SENSORS," filed August 12, 2022, the entire contents of which are incorporated herein by reference.

[0002] Technical Field FIELD OF THE DISCLOSURE This disclosure relates generally to the field of sensors and to automated methods for adjusting or calibrating one or more sensors, and more particularly to automated methods for adjusting one or more sensors in a residual gas analyzer. [Background technology]

[0003] Efficient manufacturing is the result of many variables, including the efficient use of resources. Resources used in manufacturing include material resources and human resources. In many manufacturing processes, especially in the manufacture of scientific equipment, human resources can be the scarcest resource. Therefore, many manufacturers seek to find ways to maximize the use of their human resources through the use of automation.

[0004] In the case of mass spectrometry or residual gas analyzers, part of the manufacturing process involves calibrating the sensor, and more specifically, the mass filter portion of the sensor. This may involve running the mass spectrometer using one or more known samples to measure the mass-to-charge ratios of the ions present. The sensor is then adjusted so that the resulting spectral peaks correspond to the expected mass-to-charge ratios of the known samples. Such calibration is performed manually by a technician and can be time-consuming and tedious. Because manual calibration requires a technician to perform a step-by-step process, human error can lead to one or more calibration steps being omitted or incorrectly performed. Because technicians must perform manual calibration, fewer technicians are available to solve other problems that require more detailed analysis and expertise. This can result in manufacturing delays due to the time required to manually calibrate the sensor and any remaining manufacturing issues that cannot be resolved in a timely manner. Furthermore, these remaining manufacturing issues can result in wasted materials and increased overall costs.

[0005] These are just some of the problems associated with the manufacturing process, and more specifically the manufacturing process for mass spectrometers. Summary of the Invention [Means for solving the problem]

[0006] An embodiment of a method for automatically calibrating a sensor for a residual gas analyzer includes the steps of (a) measuring a standard sample with the sensor; (b) obtaining data measurements from the sensor for the standard measured at a first mass unit; (c) fitting a peak model function to the measurement data; (d) determining the presence of peaks in the data measurements with the peak model function; and (e) performing dynamic time warping. (e) aligning the peak model function to the data using dynamic time warping, (f) determining one or more peak features in the data measurements by the dynamic time warping, (g) determining whether a peak center position and a peak width are within a predetermined peak tolerance range and a peak width tolerance range, (h) repeating steps (b) through (g) for a next mass unit if the peak center position and peak width of the first mass unit are within the predetermined tolerance range, (i) adjusting at least one sensor parameter when the peak center position or the peak width is outside the predetermined tolerance range, (j) repeating steps (b) through (i) until the predetermined tolerance is met or a maximum number of iterations has been performed, and (k) issuing a signal if the peak center position and the peak width of the first mass unit are not within the predetermined tolerance range after the maximum number of iterations has been performed, the signal indicating that technician intervention is required.

[0007] In one embodiment of the method, the calibration method stops after a signal is emitted. In one embodiment, the method further includes calibrating the initial mass unit and each subsequent mass unit from a predetermined slate of mass units, whereby the entire mass spectrum is calibrated by calibrating the mass units from the predetermined slate. In one embodiment, the maximum number of iterations is between 3 and 6. In one embodiment, at least one of the sensors is a mass filter sensor. In one embodiment, the predetermined peak tolerance is an actual value of 0.05 AMU. In a further embodiment, adjusting at least one of the sensor parameters further includes adjusting at least one of a DC voltage setting and an RF voltage setting. In another embodiment, the peak model is Gaussian.

[0008] Another embodiment of a method for automatically calibrating a sensor for a residual gas analyzer includes configuring one or more data storage devices to store a plurality of computer-readable instructions configured to perform the following steps: (a) selecting a mass unit from a predetermined candidate list of mass units; (b) determining a base adjustment for the mass unit using historical parameter data obtained from one or more previous adjustments; (c) performing iterations of peak width adjustment until the peak width is within a predetermined tolerance, wherein a maximum number of iterations of the peak width adjustment is assigned; (d) performing iterations of mass accuracy adjustment until the mass accuracy is within a predetermined tolerance, wherein a maximum number of iterations of the mass accuracy adjustment is assigned; (e) moving to a next mass unit from the predetermined candidate list of mass units and repeating steps (b)-(d); and (f) issuing a warning signal. A warning signal is used when at least one of the following occurs: (i) a maximum number of iterations of the peak width adjustment is reached before the peak width is adjusted to within the predetermined tolerance, or (ii) a maximum number of iterations of the mass accuracy adjustment is reached before the peak width is adjusted to within the predetermined tolerance, and the warning signal stops sensor calibration.

[0009] In one embodiment, the entire mass spectrum is calibrated by calibrating at the first mass unit and each subsequent mass unit from a predetermined candidate list of mass units. In one embodiment, the maximum number of iterations of the peak width adjustment is 3 to 6. In one embodiment, the maximum number of iterations of the peak precision adjustment is 3 to 6. In one embodiment, at least one of the sensors is a mass filter sensor. In another embodiment, the predetermined tolerance for mass precision is an actual value of 0.05 AMU. [Brief explanation of the drawings]

[0010] A more particular description of the present invention, briefly summarized above, can be had by reference to embodiments, some of which are illustrated in the accompanying drawings. It should be noted, however, that the accompanying drawings illustrate only typical embodiments of the present invention and should not be considered as limiting the scope of the present invention, since the present invention may admit of other equally effective embodiments. Accordingly, for a better understanding of the nature and objects of the present invention, reference may be made to the following detailed description.

[0011] [Figure 1] 1 shows a perspective view of an embodiment of a sensor used in a residual gas analyzer. [Figure 2] FIG. 1 shows a perspective view of an embodiment of parallel rods used in a mass filter. [Figure 3] An example of a peak function fitted to data obtained from a spectrum between 1 and 3 m / z is shown. [Figure 4] An example of a peak function fitted to data obtained from a spectrum between 1 and 3 m / z is shown. [Figure 5A] 10 is a graphical representation of example parameters that can be changed to tune one or more sensors. [Figure 5B] 1-4 show examples of adjustment methods showing the progress of data processing during a single iteration. [Figure 5C] 10 illustrates another embodiment of a conditioning method that illustrates a transformation of the original data distribution. [Figure 6] 1 illustrates a schematic representation of an embodiment of a method for automatically adjusting a sensor. [Figure 7] 1 shows two different examples of adjustment methods showing the progression of the peak after each iteration. [Figure 8] 1 shows two different examples of adjustment methods showing the progression of the peak after each iteration. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following description relates to a method for automatically adjusting a mass spectrometer. It will be understood that the versions described herein are examples embodying the specific inventive concepts detailed herein. As such, other variations and modifications will be readily apparent to those skilled in the art. Additionally, certain terms may be used throughout this description to provide an appropriate frame of reference with respect to the accompanying drawings. These terms may include "upper," "lower," "forward," "rearward," "internal," "external," "front," "rear," "top," "bottom," "inside," "outside," "first," "second," etc., and are not intended to limit these concepts unless specifically so indicated. As used herein, the terms "about" or "approximately" may refer to a range of 80% to 125% of a claimed or disclosed value. The drawings, whose purpose is to depict the salient features of the mass spectrometer and the disclosed automatic adjustment method, are not specifically provided to scale.

[0013] As shown in Figure 1, an example of a sensor 100 for a mass spectrometer is shown. The sensor 100 includes a flange 110 that couples the sensor 100 to a mass spectrometer (not shown) such that a majority of the sensor 100 is disposed within a vacuum chamber 50 of the mass spectrometer, which is shown schematically as a dotted line. A number of feedthroughs are disposed within the flange 110 to allow electrical connections from one side of the flange 110 to the other, such as between components within the vacuum chamber 50 and components external to the vacuum chamber 50. The sensor also includes an ion source 120, a mass filter 130, and a detector 140.

[0014] The ion source 120 is configured to be disposed within the vacuum chamber 50. The ion source includes an electron source, such as a filament, that generates electrons. The electrons collide with molecules in the gas sample, creating ions. The ions are then directed toward the mass filter 130, in this case a quadrupole mass filter. As shown in FIG. 2, the mass filter 130 includes four parallel rods 132 to which fixed DC (U) and alternating RF (Vo) potentials are applied. The potentials allow certain ions to pass between the rods 132 and into the detector 140, while other ions are deflected by the potentials and do not enter the detector 140. The potentials between the rods 132 are controlled by a controller 160 and can be altered to vary the DC and RF potentials. Theoretically, if the DC and RF potentials are varied while keeping their ratio constant (i.e., linear), each DC and RF combination along the gradient will allow only one mass unit to pass between the rods 132 and reach the detector 140, thereby scanning the entire mass spectrum. However, in practice, maintaining a constant ratio between the potentials leads to an inaccurate representation of the mass spectrum. Rather, varying the DC and RF potentials according to a piecewise linear function has been found to produce sufficiently accurate mass spectra.

[0015] The detector 140 detects ions passing through the mass filter 130 and determines the signal intensity of the ions as a function of mass-to-charge ratio. Molecules in a sample are identified by correlating the mass of the entire sample molecule with an identified mass or by a characteristic fragmentation pattern. For example, the entire mass spectrum of a sample gas may contain multiple spectral peaks at various mass-to-charge ratios that are used to determine the composition of the sample gas. Therefore, it is important to properly calibrate the sensor so that characteristic peaks appear where they would be expected if a particular molecule were present. For example, if chlorine were present in the sample, peaks would be expected to appear at 35 and 37, with a 3:1 ratio reflecting the abundance of the two chlorine isotopes.

[0016] While sensor calibration has traditionally been performed manually, a method for automating sensor calibration is disclosed herein, thereby increasing the time technicians have to monitor and address other, more substantial issues that may arise during the manufacturing process. This automation not only increases the throughput of the calibration effort when multiple sensors are calibrated simultaneously (in parallel with one another), but also increases consistency because potential human error is eliminated. The calibration method described herein may be performed by a separate control system 200 that electronically communicates with the sensors solely for calibration purposes. Once connected, the control system 100 can automatically perform the method with little or no human intervention. In another embodiment, the mass spectrometer or residual gas analyzer controller 160 may be configured to perform calibrations such that an initial calibration is performed by the manufacturer, after which subsequent calibrations can be performed by the end user or customer. Thus, once the end user initiates the calibration process, it is automatically performed with little or no human intervention. The disclosed method is configured to be performed by the manufacturer and / or the end user / customer.

[0017] The disclosed automatic calibration method takes into account actual sensor measurements of a standard or known sample to modify one or more parameters of the sensor to produce one or more desired spectral peaks. This is an iterative process in which the accuracy of each iteration is determined before determining whether to perform another iteration, terminate the process, or issue a notification for further investigation. For example, if the accuracy is not within desired tolerances, another iteration may be automatically performed. Alternatively, if the accuracy is within desired tolerances, the calibration process terminates. After a predetermined maximum number of iterations are performed without meeting predetermined accuracy requirements, the process outputs an "incomplete" message or other notification indicating that technician intervention may be required.

[0018] The method begins by introducing a known sample or standard sample into the sensor 100. The standard sample may be of any composition. Because the known sample has been analyzed by the sensor 100 in a controlled environment, such as the vacuum chamber 50 of a mass spectrometer or residual gas analyzer, a spectral peak is expected at a particular m / z value. For example, if the standard sample contains helium, a spectral peak is expected at or about m / z 2. In such a case, only a particular mass-to-charge range needs to be scanned for data points. As shown in Figures 3-4, data collected by the detector is shown from m / z 0 to 3. The data detected by the detector is shown as a non-smooth curve. A function, such as a peak model function, is then fitted to the data and displayed as a smooth curve. A peak function is fitted to the data to smooth the data. In one embodiment, the peak function may be Gaussian, as shown in Figure 4. A Gaussian distribution refers not only to a normal distribution but also to the variance and derivative of a Gaussian distribution. In other embodiments, the peak function may not be Gaussian. Although nonlinear least squares curve fitting is described as being performed, this is an example and other modeling techniques are equally feasible.

[0019] Next, the smooth function is aligned to the non-smooth data (original or actual data) using dynamic time warping, an algorithm for measuring the similarity of two temporal sequences. The data, and subsequently the function, may be multidimensional. The dynamic time warping algorithm finds an optimal alignment path that allows for derived peak placement and signal quality characteristics. This alignment between the peak function and the original data allows for an assessment of the signal quality and signal noise of the actual data or features of the actual data. In other words, the peak function identifies whether peaks are present in the actual data, and data mining the results of the dynamic time warping process identifies peak features within the data. The fit, or magnitude, of the alignment between the peak function and the actual data can be measured and used to shift between the peak function and the data itself. In this way, the data on which the peak function is based can be used for analysis. The greater the magnitude of the alignment between the peak function and the actual data, the less likely the peak function is a realistic model for the acquired data.

[0020] The location of the peak center is measured and compared to a user-defined optimal location to determine whether the location of the peak center is within a predetermined tolerance or deviation. For example, for helium, the user-defined optimal location of the peak center is 2 m / z. If the measured peak center location is outside the predetermined tolerance, an optimization algorithm is used to adjust the sensor parameters to find a setting that has the closest match to the peak function and is within the predetermined tolerance. The optimization algorithm typically uses Newton's method to adjust the parameters, but other root-finding and optimization algorithms (e.g., stochastic gradient descent, particle swarm optimization) can be used to continuously generate better matches. In the case of a Gaussian model, the optimization algorithm simply generates the mean of the distribution. If a multimodal model is used instead, the optimization routine can generate different locations. In the case of the mass filter sensor 130 of the mass spectrometer, the optimization algorithm automatically adjusts at least one of the RF voltage and the DC voltage. In one embodiment, the DC voltage and the RF voltage are adjusted simultaneously, additively and / or multiplicatively. When shifting the position of the peak center, the new RF and DC voltages are extrapolated according to a piecewise linear function that encapsulates how the potential varies with mass-to-charge ratio. Alternatively, when optimizing the peak width (the distance between the right and left endpoints at a given percentage of the maximum peak height), the DC voltage is adjusted by a specific shift value, and the RF voltage is adjusted by one-third of the specific shift value. The position of the peak center is controlled approximately by adjusting the RF voltage, so that within a given range of RF voltage, the adjustment shifts the peak center but does not affect the peak width. After the adjustment is complete, another iteration of the calibration process is automatically initiated. This sequence is repeated until a predetermined tolerance is met or a predetermined maximum number of iterations has been performed. If the adjustment produces results that are farther away from the predetermined tolerance (predetermined accuracy), the optimization algorithm readjusts the RF voltage in the opposite direction to the previous adjustment, or in the same direction but with a smaller magnitude.In one embodiment, the predetermined tolerance may be that the peak center location must be within 0.05 AMU of a user-defined optimum location, and the maximum number of iterations may be three.

[0021] The peak width (i.e., the distance measured between the left and right endpoints of the peak on the mass spectrum curve at a user-defined percentage of the peak location in the vertical (Y) direction) is compared to a user-defined optimal peak width to determine whether the peak width is within a predetermined tolerance or deviation. If the measured width is outside the predetermined tolerance, an optimization algorithm is used to adjust the sensor parameters. For the mass filter sensor 130 of the mass spectrometer, the optimization algorithm adjusts the DC voltage, and then another iteration of the calibration process is automatically initiated. In other words, the peak width is approximately controlled by adjusting the DC voltage, so that within a specific range of DC voltage, the adjustment shifts the peak width without affecting the peak center. This is repeated until the predetermined tolerance is met or a predetermined maximum number of iterations has been performed. In one embodiment, the peak's optimal width may be 1.55 to 2.50 AMU at an optimal location of 2 m / z with a peak tolerance of 0.05 AMU, with a specific width tolerance of 0.9 AMU ± 0.05 AMU, and the maximum number of iterations may be three. Thus, the maximum number of iterations for the entire calibration of a single adjustment mass for a given sensor may be six (three each for peak center and peak width). If the predetermined tolerance is not met after the maximum number of iterations is reached, the automatic adjustment procedure is stopped and a signal is generated indicating that the sensor must be manually checked by a technician. Any predetermined tolerance may be used and may vary depending on the manufacturer and type of sensor being calibrated. If the sensor is adjusted when or before the maximum number of iterations is reached, the number of iterations is set to zero (0) and adjustment automatically begins with the next mass unit in the queue.

[0022] As shown in Figure 5A, it is possible to see how changes in DC and RF voltage affect the peaks displayed at specific m / z values. In this figure, peaks are shown to be expected at 28 m / z, 69 m / z, and 219 m / z. These values can be measured by a single sensor if the DC and RF voltages are adjusted appropriately. In other embodiments, the auto-calibration method is performed sequentially in parallel for each adjusted mass on multiple sensors. The goal of the auto-calibration method is to find the optimal DC and RF voltages that result in peaks at or near the expected m / z, within a predetermined tolerance range. The dotted line represents the DC and RF values selected in the first iteration. The DC voltages and corresponding RF voltages along this line are not close to the peaks displayed at 28, 69, and 219 m / z. The solid line represents the second iteration of the calibration process, in which the DC and / or RF voltages are varied to move the lines closer to each of the peaks. This adjustment brings the data peaks closer to the acceptable peak centers and widths. The solid lines show examples of DC and RF voltage values required to see one, two, or all three peaks.

[0023] It should be understood that the goal of the calibration method is to automatically calibrate the sensor in as few iterations or steps as possible. The longer the calibration takes (i.e., the more iterations performed), the longer the process takes and the less advantageous it becomes. In one embodiment, the optimal maximum number of iterations is 3-6. After calibration is completed at the final mass unit in a given candidate list of mass units, the automatic calibration process ends.

[0024] Figures 5B-1 through 5B-4 (and also see Appendix I) show an embodiment of the described signal processing method, in which original measurement data is obtained from a known sample in Figure 5B-1. The data is then fitted to a function, such as a cubic spline. In Figure 5B-2, the data is interpolated so that it is upsampled to improve resolution (i.e., provide more data points) through model fitting. In Figure 5B-3, the data is normalized in the horizontal (X) direction so that the data peak is centered at zero (0) and the data has a normal distribution on both sides. This normalization is achieved by subtracting each data point X value from the mean (in this case, mean = 2) and dividing by the standard deviation. This produces data that is normally distributed along the X direction. In Figure 5B-4, a model function is fitted to the normalized data. As shown, the data is fitted using a Gaussian function, but in other embodiments, different functions may be used for fitting. The peak function in Figure 5B 4 is then matched or mapped to an interpolation function that is used to generate the data in Figure 5B 2 (a proxy for the data in Figure 5B 1) in a particular subsection using dynamic time warping. For example, mapping can be performed in a 10% region of the peak height on either side of the peak, and also at the peak center for each group of data (the data associated with each given mass value being adjusted). Figures 5B 1-4 are ... using the disclosed method to generate the data from the original data set.

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[0025] An embodiment of method 300 is shown generally in FIG. 6. As shown, method 300 begins in step 302 by selecting a predetermined mass unit in the tuning mass queue to be tuned. Each sensor is tuned to a predetermined candidate list of masses in the tuning mass queue. In this manner, the sensors are tuned across the entire mass spectrum without actually tuning at each individual mass in the spectrum. Next, step 304 is performed for each mass unit in the tuning mass queue. In step 304, the system accesses the parameters set in the previous tuning mass and combines this information with historical parameter data from previously tuned sensors to fit a conditional probability distribution function to determine the statistically most significant combination of RF and DC voltage settings. This value is then set as the base tuning for that particular mass.

[0026] Next, peak width adjustment 306 and mass accuracy adjustment 308 are performed one or more times using the methods described above. If the peak width and mass accuracy are within tolerance after or before reaching a predetermined adjustment iteration threshold, the system moves to the next mass unit in the queue (310). If the sensor cannot be adjusted at one of the specific mass units, the automatic adjustment procedure stops and an "incomplete" message or warning signal is issued indicating that a technician must intervene (312). At this point, automatic calibration stops until technician intervention is complete and calibration resumes. In one embodiment, the predetermined candidate list of mass units and maximum number of iterations are preloaded into controller 160 or control system 200 by the manufacturer. In another embodiment, the predetermined candidate list of mass units and maximum number of iterations are selected by the end user.

[0027] As shown in Figure 7, an example with five calibration iterations is shown. In this example, a peak is expected at m / z 4, so a spectral scan is performed from m / z 3 to 5. The first iteration (lowest peak), shown as the peak with the least intensity, has a peak center at approximately 4.18 m / z. Because this value is outside the predetermined tolerance for peak center, additional iterations of correction are performed until the peak center and peak width are within the predetermined tolerance. In this example, five iterations are required, and the fifth iteration produces a peak center at 4 m / z or within the predetermined tolerance. Similarly, the peak width in the first iteration is approximately 0.63 m / z, which is outside the determined tolerance. By the fifth iteration, the peak width is 0.85 m / z, which is within the predetermined tolerance. Therefore, in this example, both the peak center and width are adjusted to be within the predetermined tolerance before the maximum number of iterations is reached.

[0028] FIG. 8 shows another example of a calibration method. In this example, a peak is expected at 28 m / z. As can be seen, the first iteration (highest peak) is shown to have a peak at approximately 27.75 m / z. Two more iterations are performed, with the third iteration having a peak at 28 m / z or within the predetermined tolerance. Similarly, the peak width after the first iteration is approximately 1.58 m / z, which is outside the predetermined tolerance. After the third iteration, the peak width is approximately 0.86 m / z, which is within the predetermined tolerance. Thus, in this example, both the peak center and peak width are adjusted to be within the predetermined tolerance after the third iteration.

[0029] Although the method has been described in relation to the calibration of a sensor, mass spectrometer or residual gas analyzer, it will be apparent that it can be adapted to allow automatic calibration of a variety of sensors for a variety of types of equipment.

[0030] Although the present invention has been particularly shown and described with reference to certain exemplary embodiments, those skilled in the art will understand that various changes in detail may be made therein without departing from the spirit and scope of the invention as may be supported by the written description and drawings. Furthermore, when an exemplary embodiment is described with reference to a particular number of elements, it will be understood that the exemplary embodiment may be practiced utilizing fewer or more elements than the particular number.

Claims

1. 1. A method for automatically calibrating a sensor for a residual gas analyzer, comprising: (a) measuring a standard sample with the sensor; (b) obtaining data measurements from the sensor relating to the standard measured at a first mass unit; (c) fitting a peak model function to the measured data; (d) determining the presence of a peak in the data measurements according to the peak model function; (e) fitting the peak model function to the data using dynamic time warping; (f) determining one or more peak features within the data measurements by the dynamic time warping; (g) determining whether the peak center location and peak width are within predetermined peak tolerance ranges and peak width tolerance ranges; (h) repeating steps (b) through (g) for a next mass unit if the peak center location and peak width of the first mass unit are within the predetermined tolerance range; (i) adjusting at least one sensor parameter when the peak center position or the peak width is outside the predetermined tolerance; (j) repeating steps (b) through (i) until the predetermined tolerance is met or a maximum number of iterations has been performed; step (k) of issuing a signal if the peak center location and peak width of the first mass unit are not within the predetermined tolerance range after the maximum number of iterations have been performed, the signal indicating that technician intervention is required; A method comprising:

2. The method of claim 1 further comprising the step of stopping the calibration process after the signal is emitted.

3. 2. The method of claim 1, further comprising the step of calibrating each subsequent mass unit from the initial mass unit and the predetermined candidate list of mass units, wherein calibration with the predetermined candidate list of mass units calibrates the entire mass spectrum.

4. The method of claim 1 , wherein the maximum number of iterations is between 3 and 6.

5. The method of claim 1 , wherein at least one of the sensors is a mass filter sensor.

6. The method of claim 1 , wherein the predetermined peak tolerance is an actual value of 0.05 AMU.

7. The method of claim 5 , wherein adjusting at least one of the sensor parameters further comprises adjusting at least one of a DC voltage setting and an RF voltage setting.

8. The method of claim 1 , wherein the peak model is Gaussian.

9. 1. A method for automatically calibrating a sensor for a residual gas analyzer, comprising: configuring one or more data storage devices to store a plurality of computer readable instructions, the plurality of computer readable instructions comprising: (a) selecting a mass unit from a predetermined candidate list of mass units; (b) determining a base adjustment for said mass unit using historical parameter data obtained from one or more previous adjustments; (c) performing iterations of peak width adjustment until the peak width is within a predetermined tolerance, wherein a maximum number of iterations of peak width adjustment is assigned; Step (d) performing mass accuracy adjustment iterations until the mass accuracy is within a predetermined tolerance, wherein a maximum number of mass accuracy adjustment iterations is assigned; (e) moving to the next mass unit from the predetermined candidate list of mass units and repeating steps (b) through (d); (f) issuing a warning signal if at least one of (i) a maximum number of iterations of the peak width adjustment is reached before the peak width is adjusted to be within the predetermined tolerance, or (ii) a maximum number of iterations of the mass accuracy adjustment is reached before the peak width is adjusted to be within the predetermined tolerance occurs, wherein the warning signal causes calibration of the sensor to be stopped; and The method is configured to perform the following.

10. 10. The method of claim 9, wherein the entire mass spectrum is calibrated by calibrating the first mass unit and each subsequent mass unit from the predetermined candidate list of mass units.

11. The method of claim 9, wherein the maximum number of iterations of the peak width adjustment is 3 to 6.

12. The method of claim 9, wherein the maximum number of iterations of the peak precision adjustment is 3 to 6.

13. The method of claim 9 , wherein at least one of the sensors is a mass filter sensor.

14. The method of claim 1 , wherein the predetermined tolerance for mass accuracy is 0.05 AMU of actual value.

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