Method for automatically adjusting one or more sensors

Abstract

A method for automatically calibrating a sensor for a residual gas analyzer includes measuring a known sample by the sensor and obtaining a data measurement 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 aligned with the data using dynamic time warping to determine peak characteristics. It is determined whether the position of the peak center and the peak width are within predetermined tolerances, and if so, a previous step is performed on a next quality unit. If the position peak center or peak width at the first mass unit is outside of a predetermined tolerance, the sensor parameters are adjusted and another iteration of the calibration is performed. A signal is emitted after a maximum number of iterations has been performed and the peak center and width are outside of predetermined tolerances.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 397,471, filed on August 12, 2022, and entitled “METHOD OF AUTO TUNING ONE ORMORE SENSORS,” the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present disclosure relates generally to the field of sensors and automated methods of tuning or calibrating one or more sensors. The present disclosure relates specifically to automated methods of tuning one or more sensors of a residual gas analyzer. Background Art

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

[0004] In the case of a mass spectrometer or residual gas analyzer, part of the manufacturing process includes calibration of (one or more) sensors, and more specifically calibration of the mass filter portion of the sensor. This can include running a mass spectrometer with one or more known samples to measure the mass-to-charge ratio of the ions present. Then adjust the (one or more) sensors so that the resulting spectral peaks correspond to the expected mass-to-charge ratio of the known samples. Such calibration is performed manually by a technician and may be time-consuming and tedious. Since manual calibration requires a technician to go through a step-by-step process, human error may cause one or more calibration steps to be omitted or incorrectly performed. Since a technician is required to perform manual calibration, fewer technicians can then be used to eliminate other problems that require more in-depth analysis and expertise to resolve. Due to the time required for manual calibration of (one or more) sensors and linger manufacturing problems that cannot be resolved in a timely manner, this can cause manufacturing delays. In addition, these linger manufacturing problems may result in wasted materials and overall increased costs.

[0005] These are just some of the problems associated with the manufacturing process and more specifically the process of manufacturing mass spectrometers. Summary of the invention

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

[0007] In an embodiment of the method, the calibration method is stopped after the signal is issued. In an embodiment, the method also includes calibrating the first mass unit and each next mass unit from a predetermined mass unit slate, wherein the calibration of the predetermined mass unit slate results in calibration for the entire mass spectrum. In an embodiment, the maximum number of iterations is between three (3) iterations and six (6) iterations. In an embodiment, at least one sensor is a mass filter sensor. In an embodiment, the predetermined peak tolerance is 0.05 AMU of the actual value. In a further embodiment, adjusting at least one sensor parameter also includes adjusting at least one of the DC and RF voltage settings. 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 be executed to: (a) select a mass unit from a predefined mass unit plan; (b) determine a base adjustment of the mass unit using historical parameter data from one or more previous adjustments; (c) perform 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; (d) perform iterations of mass accuracy adjustment until the mass accuracy is within a predetermined tolerance, wherein a maximum number of iterations of mass accuracy adjustment is assigned; (e) move to the next mass unit from the predefined mass unit plan and repeat (b)-(d); (f) issue a warning signal. The warning signal is used when at least one of the following occurs: (i) the maximum number of iterations of peak width adjustment is reached before the peak width is adjusted within the predetermined tolerance, or (ii) the maximum number of iterations of mass accuracy adjustment is reached before the peak width is adjusted within the predetermined tolerance. The warning signal causes the sensor calibration to stop.

[0009] In an embodiment, calibration at a first mass unit and each next mass unit from a predetermined mass unit table results in calibration of the entire mass spectrum. In an embodiment, the maximum number of iterations of the peak width adjustment is between three (3) iterations and six (6) iterations. In an embodiment, the maximum number of iterations of the peak accuracy adjustment is between three (3) iterations and six (6) iterations. In an embodiment, at least one sensor is a mass filter sensor. In another embodiment, the predetermined tolerance for mass accuracy is 0.05 AMU of the actual value. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The invention briefly summarized above may be described in more detail by reference to embodiments, some of which are illustrated in the accompanying drawings. However, it should be noted that the accompanying drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting the scope thereof, as the invention may admit of other equivalent embodiments. Therefore, in order to further understand the nature and purpose of the invention, reference may be made to the following detailed description.

[0011] Figure 1 A perspective view of an embodiment of a sensor for use in a residual gas analyzer is illustrated.

[0012] Figure 2 A perspective view of an embodiment of parallel rods for use in a mass filter is illustrated.

[0013] Figure 3 and Figure 4 An example of a peak function fitted to data acquired in the spectrum from 1-3 m / z is shown.

[0014] Figure 5A is a graphical representation of examples of parameters that may be changed to adjust one or more sensors.

[0015] Figure 5B 1 - 4 illustrate examples of adjustment methods showing the progress of data processing during a single iteration.

[0016] Figure 5C Another embodiment of the conditioning method showing a transformation of the original data distribution is illustrated.

[0017] Figure 6 An embodiment of a method for automatically adjusting a sensor is schematically illustrated.

[0018] Figure 7 and Figure 8 Two different examples of adjustment methods showing the progression of the peak after each iteration are illustrated. DETAILED DESCRIPTION

[0019] The following discussion relates to a method for automatically adjusting a mass spectrometer. It will be understood that the versions described herein are examples that embody certain inventive concepts as described in detail herein. To this end, other variations and modifications will be readily apparent to those skilled in the art. In addition, certain terms may be used throughout this discussion in order to provide a suitable reference frame with respect to the accompanying drawings. These terms may include "above", "below", "forward", "backward", "inside", "outside", "front", "back", "top", "bottom", "inside", "outside", "first", "second", etc., and are not intended to limit these concepts unless specifically indicated. The terms "approximately" or "approximately" as used herein may refer to a range of 80%-125% of a claimed or disclosed value. With respect to the accompanying drawings, their purpose is to depict the salient features of the mass spectrometer and the disclosed automatic adjustment method, and are not specifically provided in scale.

[0020] refer to Figure 1 , an example of a sensor 100 for a mass spectrometer is shown. The sensor 100 includes a flange 110, which couples the sensor 100 to a mass spectrometer (not shown), so that most of the sensor 100 is located within the vacuum chamber 50 of the mass spectrometer as schematically shown by the dashed line. A plurality of feedthroughs are provided in the flange 110, and electrical connections are made from one side of the flange 110 to the other side, such as between components in the vacuum chamber 50 and components outside the vacuum chamber 50. The sensor also includes an ion source 120, a mass filter 130, and a detector 140.

[0021] The ion source 120 is configured to be located inside the vacuum chamber 50. The ion source includes an electron source, such as a filament, that generates electrons. The electrons collide with molecules of the gas sample and generate ions. The ions are then directed into a mass filter 130, which in this case is a quadrupole mass filter. Figure 2 , mass filter 130 comprises four (4) parallel rods 132, wherein fixed DC (U) and alternating RF (V0) potential are applied between them. Potential enables some ions to pass between rods 132 and enter detector 140, while other ions are deflected due to potential and do not enter detector 140. Potential between rods 132 is controlled by controller 160, and can be changed to change DC and RF potential. In theory, changing DC and RF potential while keeping their ratio constant (i.e. linear) will scan the entire mass spectrum, because each DC and RF combination along the slope will only allow one mass unit to pass between rods 132 and arrive at detector 140. However, in reality, keeping the ratio between potential constant leads to inaccurate representation of mass spectrum. On the contrary, it is seen that changing DC and RF potential according to piecewise linear function produces sufficiently accurate mass spectrum.

[0022] The detector 140 detects the ions that pass through the mass filter 130 and determines the signal intensity of the ions based on the mass-to-charge ratio. The molecules in the sample are identified by correlating the mass of the entire sample molecule with the identified mass or by a characteristic fragmentation pattern. For example, the entire mass spectrum of the sample gas may include multiple spectral peaks at different mass-to-charge ratios for determining the composition of the sample gas. Therefore, it is important to correctly calibrate (one or more) sensors so that if a certain molecule is present, the characteristic peaks appear where they are expected to appear. For example, if chlorine is present in the sample, peaks will be expected to be seen at 35 and 37, where the peaks have a ratio of 3: 1, reflecting the abundance of the two chlorine isotopes.

[0023] Until now, calibration of (one or more) sensors has been done manually, however disclosed herein is a creative method for automating the calibration of (one or more) sensors, thereby increasing the time that technicians must monitor and deal with other more substantial problems that may arise during the manufacturing process. When calibrating multiple sensors at the same time (in parallel with each other), this automation not only increases the throughput of the calibration work, but also increases consistency because potential human errors are eliminated. The calibration method described herein can be performed by a separate control system 200, which is in electrical communication with (one or more) sensors and is only used for calibration. Once connected, the control system 1 is able to automatically perform the method with little human intervention. In another embodiment, the controller 160 of the mass spectrometer or residual gas analyzer can be configured to perform calibration so that after the initial calibration is performed by the manufacturer, subsequent calibration can be performed by the end user or customer. Therefore, once the end user starts the calibration process, it will run automatically with little or no human intervention. The disclosed method is configured to be performed by the manufacturer and / or the end user / customer.

[0024] The disclosed automatic calibration method takes into account actual sensor measurements of a standard or known sample, and then changes one or more parameters of (one or more) sensors to produce one or more desired spectral peaks. This is an iterative process in which the accuracy of each iteration is determined before a decision is made whether to perform another iteration, end the process, or issue a notification for further investigation. For example, if the accuracy is not within the expected tolerance, another iteration can be automatically performed. Alternatively, if the accuracy is within the expected tolerance, the calibration process ends. After a predetermined maximum number of iterations have been performed without meeting the predetermined accuracy requirements, the process outputs an "Not Completed" message or other notification indicating that technician intervention may be required.

[0025] The method begins by introducing a known or standard sample into the sensor(s) 100. The standard sample can be of any composition. Since the known sample is analyzed by the sensor(s) 100 in a controlled environment (such as the vacuum chamber 50 of a mass spectrometer or residual gas analyzer), spectral peaks at certain m / z values ​​will be expected. For example, if the standard sample includes helium, a spectral peak at or near 2 m / z will be expected. In this case, only a certain mass-to-charge ratio range needs to be scanned for data points. Reference Figure 3-4 , the data collected by the detector is shown from 0-3 m / z. The data detected by the detector is shown as a non-smooth curve. Then, a function such as a peak model function is fitted to the data and shown as a smooth curve. In the work of smoothing the data, the peak function is fitted to the data. In an embodiment, as Figure 4As shown in , the peak function can be Gaussian. Gaussian distribution refers not only to normal distribution, but also to variants and derivatives of Gaussian distribution. In other embodiments, the peak function may not be Gaussian. Performing curve fitting with nonlinear least squares method is discussed and is an example, and other modeling methods are also feasible.

[0026] The smoothed function is then aligned with the non-smoothed data (raw or actual data) using dynamic time warping, which is an algorithm for measuring the similarity between two time series. The data and subsequent functions can be multidimensional. The dynamic time warping algorithm finds the best alignment path that allows the peak arrangement and signal quality characteristics to be derived. This alignment of the peak function and the original data enables the evaluation of the characteristics of the actual data or the signal quality and signal noise of the actual data. In other words, the peak function identifies whether there are peaks in the actual data, and the results of the data mining dynamic time warping process identify the peak features within the data. The fit or alignment amplitude of the peak function to the actual data can be measured and used to transfer 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 alignment amplitude of the peak function to the actual data, the less likely the peak function is to be a true model for the acquired data.

[0027] By comparing the position of the peak center with the user-defined optimal locale, the position of the peak center is measured to determine whether it is within a predetermined accepted tolerance or deviation. For example, in the case of helium, the user-defined optimal location of the peak center will be 2m / z. If the measured peak center location is outside the predetermined accepted tolerance, an optimization algorithm is used to adjust the parameters of (one or more) sensors in an attempt to find a setting with the closest alignment to the peak function and within a predetermined tolerance. The optimization algorithm can usually use Newton's method to adjust the parameters, but other root-finding and optimization (e.g., stochastic gradient descent, particle swarm optimization) algorithms can be used to produce continuously better adjustments. In the case of a Gaussian model, the optimization algorithm will only produce the average value of the distribution. Alternatively, if a multimodal model is used, this optimization routine may produce different positions. In the case of the mass filter sensor 130 of the mass spectrometer, the optimization algorithm will result in automatic adjustment of at least one of the RF and DC voltages. In an embodiment, the DC and RF voltages are adjusted simultaneously by addition and / or multiplication. When the position of the peak center is moved, the new RF and DC voltages are extrapolated according to a piecewise linear function that summarizes how the potential varies with mass-to-charge ratio. Alternatively, when optimizing the peak width (distance between the left and right endpoints, a predefined % of the maximum peak height), the DC voltage is adjusted by a specified shift value and the RF voltage is adjusted by 1 / 3 of the specified shift value. The position of the peak center is approximately controlled by adjusting the RF voltage so that within a certain range of RF voltages, the adjustment will result in a shift of the peak center but will not affect the peak width. After the adjustment is completed, another iteration of the calibration process is automatically initiated. This sequence is repeated until a predetermined tolerance is met, or until a predetermined maximum number of iterations has been performed. If the adjustment results in a result that is further away from the predetermined tolerance (predetermined accuracy), the optimization algorithm either readjusts the RF voltage in the opposite direction of the previous adjustment or readjusts the RF voltage to a smaller amplitude in the same direction as the previous adjustment. In an embodiment, the predetermined tolerance may be that the peak center location must be within 0.05 AMU of the user-defined optimal location, and the maximum number of iterations may be three (3).

[0028] In addition, the width of the peak (i.e., the distance measured between the left and right peak endpoints in the vertical (Y) direction of the mass spectrum curve at a user-defined percentage of the peak location amplitude) is compared to the user-defined optimal peak width to determine whether the width of the peak is within a predetermined tolerance or deviation. If the measured width is outside the predetermined accepted tolerance, the optimization algorithm is used to adjust the parameters of the (one or more) sensors. Again, in the case of the mass filter sensor 130 of the mass spectrometer, the optimization algorithm will adjust 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 certain range of DC voltage, the adjustment will result in a shift in the peak width without affecting the peak center. This process is repeated until the predetermined tolerance is met, or until a predetermined maximum number of iterations has been performed. In an embodiment, with a specified width tolerance of 0.9 AMU ± 0.05 AMU, the optimal width of the peak can be 1.55-2.50 AMU from the optimal location at 2 m / z, with a peak tolerance of 0.05 AMU, and the maximum number of iterations can be three (3). Therefore, the maximum number of iterations for the entire calibration of a single adjusted mass for each given sensor can be six (6) times (three (3) iterations each for peak center and peak width). If the predetermined tolerances are not met after the maximum number of iterations is reached, the automatic adjustment procedure stops and a signal is generated indicating that the sensor must be manually checked by a technician. Any predetermined tolerance may be used and will likely vary between manufacturers and the type of sensor(s) being calibrated. If the sensor is adjusted when or before the maximum number of iterations is reached, the iteration count is set to zero (0) and adjustment is automatically started at the next mass unit in the queue.

[0029] refer to Figure 5A , it can be seen how changing the DC and RF voltages can affect the peak displayed at a certain m / z. In this figure, the expected peaks are indicated at 28m / z, 69m / z and 219m / z. If the DC and RF voltages are properly adjusted, these values ​​can be measured by a single sensor. In other embodiments, the automatic calibration method is performed sequentially on each adjustment mass on multiple parallel sensors. The goal of the automatic calibration method is to find the optimal DC and RF voltages, where the peak is shown as being 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 and corresponding RF voltages along this line are not close to the peaks indicated at 28, 69 and 219. The solid line represents the second iteration of the calibration process, where (one or more) DC and / or RF voltages are changed so that the line has moved closer to each peak in the peak. This adjustment will cause the data peaks to move closer to the accepted peak center and the accepted peak width. The solid line shows an example of the DC and RF voltage values ​​required to see one, two or all three peaks.

[0030] It can be appreciated that the goal of the calibration method is to automatically calibrate the sensor(s) in as few iterations or steps as possible. The longer the calibration takes (i.e., the more iterations are run), the more time is consumed and the process becomes less advantageous. In an embodiment, the optimal maximum number of iterations is from three (3) to six (6). After the calibration at the final mass unit in the predetermined mass unit plan is completed, the automatic calibration process ends.

[0031] Figures 5B1-4 (and also with reference to Appendix I) represent embodiments of the described signal processing method, where raw measurement data is obtained from the known samples in Figure 5B1. The data is then fit with a function (such as a cubic spline). In Figure 5B2, the data is then interpolated so that it is upsampled to increase resolution (i.e., provide more data points) through model fitting. In Figure 5B3, the data is normalized in the horizontal (X) direction so that the center of the data peak is at zero (0), with the data being normally distributed on either side. 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 will produce data that is normally distributed along the X direction. In Figure 5B4, the model function is fit to the normalized data. As shown, the data is fit with a Gaussian function, however other embodiments may be fit with different functions. The peak function in FIG5B4 is then aligned or mapped using dynamic time warping with an interpolation function used to generate the data in FIG5B2 at a particular sub-portion (a proxy for the data in FIG5B1). For example, the mapping can be done at the region of 10% of the peak height on either side of the peak and also at the center of the peak for each set of data (data associated with each predefined quality value that is adjusted). FIG5B1-4 illustrate examples in which the disclosed method is used to convert the original data set into (i.e., data simulated from a specified probability distribution) is scaled to the value given by For the purpose of mapping the vertical (Y) data from the peak function onto the vertical (Y) data from the interpolation function using dynamic time warping, the correlation range It is then used to generate vertical (Y) data from both the peak function in FIG. 5B4 and the interpolation function used in FIG. 5B2. Figure 5C As can be seen in , each iteration of the method is used to transform or plot the data to the theoretical center so that the peak is defined after the last iteration.

[0032] refer to Figure 6, schematically depicting an embodiment of method 300. As shown, method 300 begins at step 302 by selecting a predefined mass cell to be adjusted in an adjustment mass queue. Each sensor is adjusted at a predetermined mass schedule in the adjustment mass queue. In this way, the sensor is adjusted across the entire mass spectrum, rather than actually being adjusted at each individual mass within the spectrum. Step 304 is then performed for each mass cell in the adjustment mass queue. At step 304, the system accesses the parameters set at the previous adjustment mass and combines this information with historical parameter data from sensors adjusted in the past to fit a conditional probability distribution function and determine the statistically most significant combination of RF and DC voltage settings. This value is then set as the base adjustment for that particular mass.

[0033] Peak width adjustment 306 and mass accuracy adjustment 308 are then performed one or more times using the methods previously described. If the peak width and mass accuracy are within tolerance after or before a predetermined adjustment iteration threshold is reached, the system moves to the next mass unit in the queue 310. If the sensor cannot be adjusted at one of the specified mass units, the automatic adjustment procedure stops and an "incomplete" message or warning signal is issued 312 indicating that technician intervention is required. At this point in time, the automatic calibration stops until the technician intervention has been completed and the calibration is restarted. In an embodiment, the predetermined mass unit plan and maximum number of iterations are preloaded into the controller 160 or control system 200 by the manufacturer. In another embodiment, the predetermined mass unit plan and maximum number of iterations are selected by the end user.

[0034] Steering Figure 7 , an example with five (5) calibration iterations is shown. In this example, the expected peak is at 4m / z, so a spectrum scan is performed between 3m / z and 5m / z. The first iteration (lowest peak), which is indicated as the peak with the minimum intensity, has a peak center at about 4.18m / z. Since this value is outside the predetermined tolerance for the peak center, additional calibration iterations are performed until the peak center and peak width are within the predetermined tolerance. In this example, five iterations are required, with the fifth iteration producing a peak center at 4m / z or within the predetermined tolerance. Similarly, the peak width at the first iteration is about 0.63m / z, which is outside the determined tolerance. By the fifth iteration, the peak width is 0.85m / z, which is within the predetermined tolerance. Therefore, in this example, before reaching the maximum number of iterations, both the peak center and the width are adjusted within the predetermined tolerance.

[0035] Figure 8Another example of the calibration method is illustrated. In this example, the expected peak is at 28m / z. As can be seen, the first iteration (highest peak) is shown as having a peak of about 27.75m / z. Two more iterations are run, with the third iteration having a peak at 28m / z or within a predetermined tolerance. Similarly, the peak width after the first iteration is about 1.58m / z, which is outside the predetermined tolerance. After the third iteration, the peak width is about 0.86m / z, which is within the predetermined tolerance. Therefore, in this example, after the third iteration, both the peak center and the peak width are adjusted within the predetermined tolerance.

[0036] Although the method has been described as it relates to calibration of sensor(s) or a mass spectrometer or residual gas analyzer, it will be appreciated that the method may be tailored to achieve automatic calibration of a variety of sensors for various types of equipment.

[0037] Although the present invention has been specifically shown and described with reference to certain exemplary embodiments, it will be understood by those skilled in the art that various changes in detail may be affected therein without departing from the spirit and scope of the present invention as supported by the written description and the accompanying drawings. In addition, where exemplary embodiments are described with reference to a certain number of elements, it will be understood that the exemplary embodiments may be implemented using either less than or more than the certain number of elements.

Claims

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

2. The method of claim 1, further comprising stopping the calibration process after issuing the signal.

3. The method of claim 1, further comprising calibrating the first mass cell and each next mass cell from a predetermined mass cell schedule, wherein calibration at the predetermined mass cell schedule results in calibration of the entire mass spectrum.

4. The method according to claim 1, wherein: The maximum number of iterations is between three (3) iterations and six (6) iterations.

5. The method according to claim 1, wherein: The at least one sensor is a mass filter sensor.

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

7. The method according to claim 5, wherein: Adjusting the at least one sensor parameter further includes adjusting at least one of a DC and RF voltage setting.

8. The method according to claim 1, wherein: The peak model is Gaussian.

9. A method for automatically calibrating a sensor for a residual gas analyzer, comprising: One or more data storage devices are configured to store a plurality of computer readable instructions configured to be executed to: (a) Selecting mass units from a predefined mass unit plan, (b) using historical parameter data from one or more previous adjustments to determine a base adjustment for the mass unit, (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, (d) performing iterations of mass accuracy adjustment until the mass accuracy is within a predetermined tolerance, wherein a maximum number of iterations of mass accuracy adjustment is assigned, (e) moving to the next mass unit from the predefined mass unit plan and repeating (b)-(d), (f) A warning signal is issued when at least one of the following occurs: (i) reaching the maximum number of iterations of peak width adjustment before the peak width is adjusted to within the predetermined tolerance, or (ii) reaching the maximum number of iterations of mass accuracy adjustment before the peak width is adjusted within the predetermined tolerance, The warning signal results in stopping of calibration of the sensor.

10. The method according to claim 9, wherein: Calibration at the first mass cell and each next mass cell from a predetermined mass cell plan results in calibration of the entire mass spectrum.

11. The method according to claim 9, wherein: The maximum number of iterations of peak width adjustment is between three (3) iterations and six (6) iterations.

12. The method according to claim 9, wherein: The maximum number of iterations of the peak precision adjustment is between three (3) iterations and six (6) iterations.

13. The method according to claim 9, wherein: The at least one sensor is a mass filter sensor.

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

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