Image position measurement method

Abstract

A method of performing image position measurements. The method comprises performing an image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position. The method comprises determining an image position error in at least partial dependence upon the measured image position 5 and the estimated image position. The method comprises determining an adapted measurement location uncertainty in at least partial dependence upon the image position error. The method comprises performing another image position measurement in at least partial dependence upon the adapted measurement location uncertainty.

Description

IMAGE POSITION MEASUREMENT METHODCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of EP application 24153839.6 which was filed on 25 January 2024 and 24169706.9 which was filed on 11 April 2024 and which are incorporated herein in their entirety by reference.FIELD

[0002] The present disclosure relates to methods of performing image position measurements. The methods may be used as part of an optical alignment process. The alignment process may, for example, involve aligning a patterning device and a substrate as part of a lithographic process.BACKGROUND

[0003] A lithographic apparatus is a machine constructed to apply a desired pattern onto a substrate. A lithographic apparatus can be used, for example, in the manufacture of integrated circuits (ICs). A lithographic apparatus may, for example, project a pattern at a patterning device (e.g., a mask) onto a layer of radiation-sensitive material (resist) provided on a substrate.

[0004] To project a pattern on a substrate a lithographic apparatus may use electromagnetic radiation. The wavelength of this radiation determines the minimum size of features which can be formed on the substrate. A lithographic apparatus, which uses extreme ultraviolet (EUV) radiation, having a wavelength within the range 4-20 nm, for example 6.7 nm or 13.5 nm, may be used to form smaller features on a substrate than a lithographic apparatus which uses, for example, radiation with a wavelength of 193 nm.

[0005] A lithographic apparatus may comprise an optical alignment system which may be used to determine and improve an alignment between the patterning device and the substrate. The patterning device may include a marker that may be imaged by a projection system of the lithographic apparatus. The marker may impart a radiation beam with a pattern or image, such as an aerial image of the marker, which may subsequently be measured in order to derive one or more properties of the lithographic apparatus. The optical alignment system may comprise a sensor apparatus configured to detect the image of the marker and thereby determine a position of the substrate relative to the patterning device.

[0006] It may be desirable to provide an optical alignment system which overcomes or mitigates a problem associated with the prior art. Embodiments of the invention which are described herein may have use in an EUV lithographic apparatus. Embodiments of the invention may have use in a deep ultraviolet (DUV) lithographic apparatus or another form of lithographic apparatus, such as, for example, nano-imprint systems and advanced packaging systems. Embodiments of the invention mayhave use in optical systems that form part of other substrate processing apparatus such as, for example, e-beam systems.SUMMARY

[0007] According to a first aspect of the present disclosure, there is provided a method of performing image position measurements. The method comprises performing an image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position. The method comprises determining an image position error in at least partial dependence upon the measured image position and the estimated image position. The method comprises determining an adapted measurement location uncertainty in at least partial dependence upon the image position error. The method comprises performing another image position measurement in at least partial dependence upon the adapted measurement location uncertainty.

[0008] A problem with known methods is that an accuracy of the estimated image position of the image (e.g. an aerial image) may vary depending on a configuration (e.g. individual components being used, operating parameters, etc.) and / or status (e.g. an age, environment, measurement sequence position, etc.) of an image position measurement system (e.g. a subsystem of a lithographic apparatus) that is used to perform the image position measurement. In known methods, a fixed value is used for the measurement location uncertainty, a parameter that at least partially determines a size of an area over which a sensor (e.g. a scanning image sensor) should search for the image. A fixed value of measurement location uncertainty is defined for the measurement location uncertainty of each image position measurement (e.g. each measurement scan configured to locate an aerial image) in each of three perpendicular axes x, y, z. The fixed values are held constant for all possible measurement directions, settings, contexts, components (e.g. the sensor being used) and systems (e.g. the particular measurement subsystem of a particular lithographic apparatus being used to perform the image position measurement). This means that, in order to guarantee robustness and prevent measurement failures in which the image position cannot be determined (e.g. because the image is not found or “captured” during the measurement scan), the value normally adopted for the measurement location uncertainty is effectively the worst case scenario over an entire population of image position errors, regardless of the specific settings or context that causes each image position error, and / or what tolerances a particular measurement system could normally afford, and / or a performance of the particular measurement system being used, and / or previous usage of the particular measurement system being used, etc. That is, the fixed values of measurement location uncertainty need to be large enough to account for the largest possible image position error (i.e. a worst case scenario) in the estimated image position of the image. As such, all measurement scan distances, and thus their durations, are by design in most cases larger than actually required or, in the best case, suitable. Thisleads to unnecessarily long measurement times when the actual image position error is smaller than the worst case scenario.

[0009] The method of the present disclosure overcomes the problems associated with the known methods by determining an adapted measurement location uncertainty and using the adapted measurement location uncertainty in a subsequent image position measurement. The adapted measurement location uncertainty may be determined by, for example, using previous values of image position error that have occurred in previous image position measurements. In this manner the measurement location uncertainty may be adapted to a value that more closely corresponds to an actual accuracy of the estimated image position which makes the subsequent image position measurement of the present disclosure more efficient compared to known methods. Even by reducing a magnitude of only a single measurement certainty in a series of image position measurements, a throughput of the image position measurement process is improved. However, the method of the present disclosure may be used to incrementally improve the adapted measurement location uncertainty across a series of image position measurements, thereby greatly improving and / or optimizing the throughput of the image position measurement process over time. The method of the present disclosure may be used to reduce the adapted measurement location uncertainty down to its lowest natural limit, thereby reducing measurement duration and increasing measurement throughput. As the amount of available data increases (e.g. as stored as a record in memory), the adapted measurement location uncertainty may rapidly converge to an optimal value, resulting in an image position measurement that is optimized to a shortest possible duration whilst maintaining a desired level of accuracy. In accordance with the present disclosure, an adaptive measurement location uncertainty determination process based on the actual accuracy of previous image position measurements and / or tuned to the specific measurement context (e.g. components, configuration, status, settings, etc.) may ensure that the best of all measurement location uncertainty values (i.e. effectively the smallest of all measurement location uncertainty values that could be safely afforded without risk of measurement failures, and is therefore “acceptable”) is eventually adopted through adaptation.

[0010] In addition, the method of the present disclosure advantageously results in a reduction of sensor contamination when used in the context of a lithographic apparatus. During an image position measurement, the radiation used causes a breakdown of chemicals which results in contamination growth on the sensor that is being used to send the aerial image. By reducing the measurement location uncertainty, the measurement duration is also reduced, so less contamination forms during the measurement. For example, the method of the present disclosure may reduce a carbon growth rate down to about 30% or more of a carbon growth rate associated with known methods.

[0011] Furthermore, the adapted measurement location uncertainty may be used as a key performance indicator for monitoring a performance of a given measurement system that utilizes the method of the present disclosure.

[0012] The measurement location uncertainty may correspond to an area across which a scanning sensor searches for, or attempts to “capture”, an image such as an aerial image of an alignment marker. The measurement location uncertainty may therefore be referred to as a capture range. The adapted measurement location uncertainty may be referred to as an adapted capture range. The image position error may be referred to as a capture error.

[0013] Determining the adapted measurement location uncertainty may comprise providing the image position error as an input to an adaptive algorithm.

[0014] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty in at least partial dependence upon a proportionality relationship between the adapted measurement location uncertainty and the image position error.

[0015] The adaptive algorithm may comprise the following equation: aCR = n ■ max (CE~) where aCR is the adapted measurement location uncertainty, n is a scaling factor, CE is the image position error and max (CE) is a largest previously determined value of image position error.

[0016] The scaling factor n may be any number. The scaling factor n may be a non- zero integer such as, for example, 1, 2, 3, 4, etc., or may be a non-integer such as, for example, 1.2, 3.45, etc. The scaling factor n may be used to increase or reduce a statistical performance of the image position measurement and / or prevent image position measurement failures. The scaling factor n may be selected using, for example, trial and error learning of previous image position measurements.

[0017] The maximum image position error max(CE) may correspond to a largest value of image position error CE across an entire history of image position measurements, or a largest value of image position error CE in a given period of time dT, or a largest value of image position error CE across a number Noof most recent image position measurements, or a largest value of image position error CE in a selected subset of image position measurements.

[0018] The scaling factor n and / or the period of time dT and / or the number Noof most recent image position measurements and / or the subset of image position measurements may be selected in at least partial dependence upon an image position measurement parameter under which the image position measurement occurs. The scaling factor n and / or the period of time dT and / or the number Noof most recent image position measurements and / or the subset of image position measurements may be stored as part of a record of image position measurements (e.g. in memory) in connection with the image position measurement parameter.

[0019] The adaptive algorithm may comprise the following equation: aCR = n ■ max (CE) + mwhere m is a margin of error. The margin of error m may correspond to a minimum value of the adapted measurement location uncertainty. For example, when the measurement location uncertainty represents an area across which a scanning sensor searches for the image, the margin of error m may correspond to a minimum distance in a given direction x, y, z across which the scanning sensor must scan in order to find, and thereby determine the position of, the image.

[0020] The margin of error m may be used to increase or reduce a statistical performance of the image position margin of error m may be selected using, for example, trial and error learning of previous image position measurements.

[0021] The scaling factor n and / or the margin of error m may be selected in at least partial dependence upon a desired statistical performance of the image position measurement. The desired statistical robustness of the image position measurement may be at least partially based on previous image position measurement data. For example, a determined adapted measurement location uncertainty may be compared to a typical or average image position measurement performance to determine a failure rate (i.e. a percentage of image position measurements in which the image position error is greater than the adapted measurement location uncertainty (CE>aCR)) and this comparison may be used to adjust parameters such as scaling factor n and / or the margin of error m to achieve the desired statistical performance.

[0022] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty in at least partial dependence upon a statistical property of a plurality of image position errors.

[0023] The plurality of image position errors may form part of an image position error record. The image position error record may be formed by performing a plurality of image position measurements and recording each resulting image position error to memory.

[0024] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty in at least partial dependence upon a standard deviation of the plurality of image position errors. The adaptive algorithm may comprise the following equation: aCR = n ■ STD CE') + m where STD(CE) is the standard deviation of the plurality of image position errors.

[0025] The standard deviation of the plurality of image position errors STD(CE) may be determined for an entire history of image position measurements. The adaptive algorithm may comprise the following equation:where N is a total number of image position measurements from i = 1 to i = N and CEj is the corresponding image position error associated with a particular measurement i.

[0026] The standard deviation of the plurality of image position errors STD(CE) may be determined for a number of image position errors CE across a given period of time dT. The adaptive algorithm may comprise the following equation:where t is the time of a given image position measurement within the period of time dT which beings at a start time t0and finishes at an end time t0+ dT.

[0027] The standard deviation of the plurality of image position errors STD(CE) may be determined for a number Noof most recent image position measurements. The adaptive algorithm may comprise the following equation:The standard deviation of the plurality of image position errors STD(CE) may be determined for a selected subset of image position measurements.

[0028] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty in at least partial dependence upon a weighting of the plurality of image position errors.

[0029] The examples of relationships and equations provided above may assign equal weights to all image position errors. Alternatively, a weighted moving standard deviation may be determined. The adaptive algorithm may comprise the following equation:' i=N-N0wi ’ CESTD(CE)N(W - 1) where X(wi2) = IV is a weight assigned to an associated image position error CEj. It will be appreciated that weighting may be applied to any of the above standard deviation relationships. An alternative weighting method involving an estimated measurement location uncertainty of the image position errorCEj based on, for example, a fitting process may comprise the following equation:1 W; = — - CEt

[0030] The adaptive algorithm may be configured to attribute a greater weight to more recent image position measurements. The adaptive algorithm may comprise the following equation: w; = / (i - No) such that the data from the oldest image position measurements is assigned a weighting of zero whereas the data from the most recent image position measurement is assigned the largest weighting.

[0031] The adaptive algorithm may be configured to determine an exponentially weighted standard deviation of image position errors. The adaptive algorithm may comprise the following equation:where a = [0,1] is a constant.

[0032] The constant a may be configured to introduce an effective low-pass filtered memory in the standard deviation STD calculation and / or to reduce a weighting of the oldest image position error. The constant a may be configured to reduce the weighting of the oldest image position error asymptotically to zero (instead of, for example, via a discrete step) and thereby provide a smooth convergence of the calculated standard deviation.

[0033] Determining a standard deviation of the image position error may be particularly desirable for situations in which an average of the plurality of image position errors is zero, as represented by the following relationship:This relationship may be assumed implicitly in one or more of the equations and relationships provided above. This relationship may not always be present. For example, a non-zero image position error average may be caused by systematic biases in a determination of the estimated image position, e.g. by a preceding sequence. In these situations, it may be desirable to explicitly subtract the average for all standard deviation calculations discussed above (and using equivalent formulas or weighting, etc., in each case). The adaptive algorithm may comprise the following equation:This equation may not be robust for systematic errors in determining the estimated image position. The adaptive algorithm may account for this by being configured to determine a root mean square of the image position error rather than the standard deviation of the image position error. All implementations and variants discussed above may still be valid, thereby leading to a more conservative calculation of the adapted measurement location uncertainty.

[0034] The average image position error CE across the entire history of image position measurements may provide an indication and / or estimation of any systematic error in the determination of the estimated image position EP. As such, the average image position error CE may be utilized when determining the estimated image position EP. For example, the average image position error CE may be provided as an input to an estimation algorithm. For example, the average image position error CE may be added or subtracted as a feed forward correction when determining the estimated image position EP, which may be represented by the following relationship:EP -> (EP - CE )The image position measurement may then be centered at a best estimated image position, and at the same time, the most accurate estimate of the standard deviation of the image position error (e.g. computed by removing the average image position error CE) may be used to determine the adapted measurement location uncertainty.

[0035] The adaptive algorithm may comprise a threshold relationship configured to set an upper limit and / or a lower limit to the adapted measurement location uncertainty.

[0036] The threshold relationship may take the following form:C^min < ClCR < CRmaxwhere CRminis a lower limit to the adapted measurement location uncertainty and CRmaxis a maximum limit to the adapted measurement location uncertainty.

[0037] When the conditions allow (e.g. a system performing the image position measurements is regarded as being stable and / or the adapted measurement location uncertainty has reached a relatively stationary value) it may be desirable to adapt the scaling factor n and / or the margin of error m to achieve a more optimized adapted measurement location uncertainty due at least in part to a reduced risk of measurement location uncertainty error offered by said conditions (e.g. the measurement system being in equilibrium). The adaptive algorithm may comprise the following equation:aCR(t) = n(t) ■ ST D(CE) + m(t)When these conditions are not present, but long term drifts of values are of concern, the adaptive algorithm may be configured to integrate the determination of the adapted measurement location uncertainty with trending estimators such as, for example, Moving Average Convergence / Divergence (MACD) algorithms which may be configured to detect the convergence / divergence of the adapted measurement location uncertainty to timely correct for it.

[0038] Drifts of values may be determined by other means. For example, drifts may be detected by fitting of the image position error data and / or related statistical properties, e.g. on a purposely chosen time interval. With the same principle, the adaptive algorithm may be configured to correct for periodic variations in the image position error record, e.g. daily and / or weekly and / or seasonal variations, by estimating the signatures of said variations using, for example, Principal Component Analysis. The adaptive algorithm may be configured to make use information from known correlating system Key Performance Indicators, which may highlight performance variation, and act upon it with adapted corrections. The Key Performance Indicators may include one or any combination of, sensor measurement reproducibility, mechanical component (e.g. moveable stage) stabilities, optical component (e.g. lens) stabilities, etc.

[0039] The adaptive algorithm may be configured to respond to the occurrence of a failure of an image position measurement, or of a system involved in the measurement, e.g. an image sensor error, in an appropriate manner. In any of the aggregation methods (i.e. equations and relationships) discussed above, individual data may be ignored in case of, for example, an image position measurement failure and / or image position measurement anomalies or outliers, and omitted from the computation of the adapted measurement location uncertainty. The situation would differ in the event of an image position measurement failure due to an error in the measurement location uncertainty itself. In these situations, the adapted measurement location uncertainty is too small. In these situations, the method may comprise re-initializing one or more records (e.g. the image position error record) and restarting the process of determining the adapted measurement location uncertainty from the beginning. Alternatively or additionally, a correction term may be added to the one or more records. As another alternative or addition, the scaling factor n and / or the margin of error m may be adjusted.

[0040] One or more of the data records (e.g. the image position error record) may be configured to remain in the event of changes to other software and / or a system reboot. The one or more data records may be configured to reset only in the event of a change (e.g. a hardware change) that would make the record history obsolete and worth refreshing.

[0041] The image position measurement may be performed in accordance with an image position measurement parameter. The method may comprise providing the image position error as an input to a plurality of different adaptive algorithms to determine a plurality of adapted measurementuncertainties. The method may comprise storing the adaptive algorithm that produces a smallest acceptable measurement location uncertainty in memory such that the adaptive algorithm that produces the smallest acceptable measurement location uncertainty is associated with the image position measurement parameter. The method may comprise referring to the adaptive algorithm that produces the smallest acceptable measurement location uncertainty in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.

[0042] The adaptive algorithm that produces the smallest acceptable measurement location uncertainty may advantageously be referred to in future image position measurements such that the most suitable and best performing adaptive algorithm is selected immediately. The smallest acceptable measurement location uncertainty may correspond to the measurement location uncertainty that has the smallest value whilst still being large enough to reduce or avoid the risk of image position measurement failures in which the image position is not determined by the image position measurement (e.g. because the measurement location uncertainty is too small and the image cannot be found). The desired robustness of an image position measurement, and thus of the smallest acceptable measurement location uncertainty, will vary between different systems and different users, and may be selected as desired.

[0043] The method may comprise performing a plurality of image position measurements to determine a plurality of image position errors. Determining the adapted measurement location uncertainty may be at least partially dependent upon the plurality of image position errors.

[0044] The method may comprise providing the plurality of image position errors as an input to the adaptive algorithm.

[0045] The method may comprise performing a plurality of image position measurements in accordance with an image position measurement parameter to determine a plurality of adapted measurement uncertainties. The method may comprise storing the plurality of adapted measurement uncertainties in memory such that the plurality of adapted measurement uncertainties is associated with the image position measurement parameter. The method may comprise referring to the plurality of adapted measurement uncertainties in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.

[0046] The adapted measurement uncertainties having the most relevance to the image position measurement parameter may be referred to in future image position measurements performed under said parameter such that the most suitable and best performing adapted measurement location uncertainty is selected immediately.

[0047] The image position measurement parameter may comprise one or any combination of the following parameters: image position measurement sequence value (e.g. the first image position measurement, the second image position measurement, etc.), image position measurement scanning direction (e.g. a horizontal scanning direction along a measurement plane and / or a vertical scanning direction along the measurement plane), illumination setting (e.g. an illumination mode used to formthe image such as, for example, dipole illumination, quadrupole illumination, etc.), time elapsed (i.e. how much time has passed since the first image position measurement in a plurality of image position measurements), image marker properties (i.e. material and / or optical properties of the marker that has been illuminated to form the image such as, for example, marker geometry, optical stack, etc.), image sensor (i.e. the detector used to detect the image), system identity (i.e. what system the image measurements are taking place in), system performance (e.g. an accuracy of the system in which the image measurements are taking place), etc.

[0048] According to an aspect of the present disclosure, there is provided a method of aligning first and second components. The method comprises illuminating a marker to form an image of the marker. The method comprises projecting the image of the marker onto a sensor. The method comprises adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the mark. The method comprises performing image position measurements of the image of the marker in accordance with any preceding aspect.

[0049] The marker may have a known positional relationship relative to the first component. The sensor may have a known positional relationship relative to the second component.

[0050] The method may comprise projecting a patterned beam of radiation onto a substrate. The first component may be a patterning device configured to impart a radiation beam with a pattern in its cross-section to form the patterned radiation beam. The second component may be the substrate.

[0051] According to an aspect of the present disclosure, there is provided a computer program comprising computer readable instructions configured to cause a computer to carry out the method according to any of the previous aspects.

[0052] According to an aspect of the present disclosure, there is provided a computer readable medium carrying a computer program according to the previous aspect.

[0053] According to an aspect of the present disclosure, there is provided a computer apparatus for controlling an image position measurement system. The computer apparatus may comprise a memory storing processor readable instructions. The computer apparatus may comprise a processor arranged to read and execute instructions stored in said memory. Said processor readable instructions may comprise instructions arranged to control the computer to carry out the method of performing image position measurements according to any of the above aspects.

[0054] In accordance with another aspect of the present disclosure, there is provided an optical alignment system. The optical alignment system may comprise an illumination system configured to condition radiation. The optical alignment system may comprise a marker configured to impart the radiation with a pattern to form patterned radiation. The optical alignment system may comprise a projection system configured to collect the patterned radiation and form an image of the marker. The optical alignment system may comprise a sensor apparatus configured to detect the image of the marker. The optical alignment system may comprise a controller configured to control the opticalalignment system to carry out the method according to any of the preceding aspects of the present disclosure.

[0055] In accordance with another aspect of the present disclosure, there is provided a lithographic apparatus. The lithographic apparatus may comprise the optical alignment system in accordance with an aspect of the present disclosure. The lithographic apparatus may comprise a support structure constructed to support a patterning device. The patterning device may be capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam. The marker may form part of the support structure or the patterning device. The lithographic apparatus may comprise a substrate table constructed to hold a substrate. The sensor apparatus may form part of the substrate table. The projection system may be configured to project the patterned radiation beam onto the substrate. The optical alignment system may be configured to determine an alignment between the patterning device and the substrate.

[0056] In accordance with another aspect of the present disclosure, there is provided a lithographic exposure method. The lithographic exposure method may comprise using the method of aligning a first component and a second component in accordance with as aspect of the present disclosure to determine an alignment between a patterning device and a substrate. The method may comprise using the patterning device to impart a radiation beam with a pattern in its cross-section to form a patterned radiation beam. The method may comprise projecting the patterned radiation beam onto the substrate.

[0057] In accordance with another aspect of the present disclosure, there is provided a method of performing an image position measurement. The method comprises determining a movement parameter of the image position measurement. The method comprises determining a sensitivity of the image position measurement in at least partial dependence upon the movement parameter. The method comprises comparing the sensitivity of the image position measurement to a known measurement disturbance. The method comprises adjusting an aspect of the image position measurement in at least partial dependence upon the comparison between the sensitivity of the image position measurement and the known measurement disturbance.

[0058] The method advantageously reduces negative effects associated with a coupling or overlap of the sensitivity and the known measurement disturbance. For example, a reproducibility and / or an accuracy of the image position measurement may be improved.

[0059] The image position measurement may comprise introducing relative movement between a sensor and an image (e.g. an aerial image). The relative movement may comprise a scanning or sweeping movement, a step-and-scan movement, etc. The image may be an image of a marker such as, for example, an alignment marker.

[0060] The movement parameter may comprise one or any combination of a distance, area, velocity, acceleration, frequency, etc. For example, the movement parameter may comprise one or more of a distance and / or area across which the sensor may search for the position of the image; avelocity and / or acceleration of the sensor relative to the image during the search; a frequency of the image position measurement (e.g. frequency of movement of the sensor between two end coordinates of a scanning or sweeping motion during the search for the position of the image); and / or a duration of the image position measurement. The movement parameter may at least partially define a measurement scan.

[0061] The sensitivity may comprise one or more frequencies. For example the sensitivity may comprise one or more mechanical frequencies (e.g. vibrations, movements, etc.) at which the image position measurement is susceptible to unwanted resonance effects.

[0062] The known measurement disturbance may comprise one or more frequencies. For example, the known measurement disturbance may comprise one or more mechanical frequencies (e.g. vibrations, movements, etc.) that may be present during the image position measurement, and thereby influence the image position measurement. The less of a difference between the known measurement disturbance and the sensitivity, the greater the negative impact that the known measurement disturbance may have on the outcome of the image position measurement. For example, if the known measurement disturbance substantially matches the sensitivity, the known measurement disturbance may introduce unwanted resonance effects that negatively affect a quality (e.g. a reproducibility and / or an accuracy) of the image position measurement.

[0063] Determining the sensitivity may comprise one or more of performing a numerical simulation of the image position measurement based on previous data and / or theoretical models, performing analytical modeling of the image position measurement, etc.

[0064] Adjusting the aspect of the image position measurement may comprise changing the movement parameter.

[0065] Changing the movement parameter changes the sensitivity. By changing the movement parameter such that a greater difference is introduced between the sensitivity and the known measurement disturbance, the image position measurement becomes less susceptible to unwanted resonance effects. As such, negative effects associated with the known measurement disturbance are reduced, thereby improving a quality of the image position measurement.

[0066] Adjusting the aspect of the image position measurement may comprise filtering a result of the image position measurement.

[0067] Filtering the result of the image position measurement allows the negative effects of unwanted noise, such as the known measurement disturbance, on the results of the image position measurement to be reduced. The unwanted frequencies selected for filtering out, and thereby producing a reduction or removal of the associated negative effects from the results of the image position measurement, are selected at least partially based on the comparison between the sensitivity and the known measurement disturbance. For example, known measurement disturbance frequencies that substantially match sensitivity frequencies may be selected as frequencies that are to be filtered out of the results of the image position measurement.

[0068] The filtering may be referred to as post-processing filtering. The filtering may comprise time-averaging the result of the image position measurement.

[0069] A combination of changing the movement parameter and filtering the result of the image position measurement at least partially based on the comparison between the sensitivity and the known measurement disturbance may vastly improve a quality of the image position measurement.

[0070] Comparing the sensitivity to the known measurement disturbance may comprise determining a sensitivity frequency spectrum, which may be referred to as a sensitivity curve. Comparing the sensitivity to the known measurement disturbance may comprise determining a known measurement disturbance frequency spectrum. Comparing the sensitivity to the known measurement disturbance may comprise identifying a peak of the sensitivity frequency spectrum that at least partially coincides with a peak of the known measurement disturbance frequency spectrum.

[0071] Adjusting the aspect of the image position measurement at least partially based on the comparison may comprise changing the movement parameter to reduce the overlap between the peaks.

[0072] Adjusting the aspect of the image position measurement at least partially based on the comparison may comprise filtering the result of the image position measurement at a frequency of the at least partially coinciding peaks.

[0073] The method may comprise determining the known measurement disturbance.

[0074] The method may comprise performing the image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position. The method may comprise determining an image position error in at least partial dependence upon the measured image position and the estimated image position. The method may comprise determining an adapted measurement location uncertainty in at least partial dependence upon the image position error. The method may comprise determining an adapted movement parameter in at least partial dependence upon the adapted measurement location uncertainty. The method may comprise determining an adapted sensitivity in at least partial dependence upon the adapted movement parameter. The method may comprise comparing the adapted sensitivity to the known measurement disturbance. The method may comprise adjusting a subsequent image position measurement in at least partial dependence upon the comparison between the adapted sensitivity and the known measurement disturbance, and the adapted measurement location uncertainty.

[0075] Determining the adapted measurement location uncertainty may comprise providing the image position error as an input to an adaptive algorithm.

[0076] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty in at least partial dependence upon a proportionality relationship between the adapted measurement location uncertainty and the image position error.

[0077] According to another aspect of the present disclosure, there is provided a method of aligning first and second components. The method may comprise illuminating a marker to form animage of the marker. The method may comprise projecting the image of the marker onto a sensor. The method may comprise adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the marker. The method may comprise performing an image position measurement of the image of the marker in accordance with any previous aspect.

[0078] The method may comprise projecting a patterned beam of radiation onto a substrate. The first component may be a patterning device configured to impart a radiation beam with a pattern in its cross-section to form the patterned radiation beam. The second component may be the substrate.

[0079] According to another aspect of the present disclosure, there is provided a lithographic exposure method. The method may comprise using the method of a previous aspect to align the patterning device and the substrate. The method may comprise using the patterning device to impart the radiation beam with the pattern in its cross-section to form the patterned radiation beam. The method may comprise projecting the patterned radiation beam onto the substrate.

[0080] According to another aspect of the present disclosure, there is provided a computer program comprising computer readable instructions configured to cause a computer to carry out the method according to any previous aspect.

[0081] According to another aspect of the present disclosure, there is provided a computer readable medium carrying a computer program according to the above aspect.

[0082] According to another aspect of the present disclosure, there is provided a computer apparatus for controlling an image position measurement system. The computer apparatus may comprise a memory storing processor readable instructions. The computer apparatus may comprise a processor arranged to read and execute instructions stored in said memory. Said processor readable instructions may comprise instructions arranged to control the computer to carry out the method according to any of the previous aspects.

[0083] According to another aspect of the present disclosure, there is provided an optical alignment system. The optical alignment system may comprise an illumination system configured to condition radiation. The optical alignment system may comprise a marker configured to impart the radiation with a pattern to form patterned radiation. The optical alignment system may comprise a projection system configured to collect the patterned radiation and form an image of the marker. The optical alignment system may comprise a sensor apparatus configured to detect the image of the marker. The optical alignment system may comprise a controller configured to control the optical alignment system to carry out the method according to any previous aspect.

[0084] According to another aspect of the present disclosure, there is provided a lithographic apparatus. The lithographic apparatus may comprise the optical alignment system of the above aspect. The lithographic apparatus may comprise a support structure constructed to support a patterning device. The patterning device may be capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam. The marker may form part of the supportstructure or the patterning device. The lithographic apparatus may comprise a substrate table constructed to hold a substrate. The sensor apparatus may form part of the substrate table. The projection system may be configured to project the patterned radiation beam onto the substrate. The optical alignment system may be configured to determine an alignment between the patterning device and the substrate.

[0085] Any of the above aspects may be combined in any way.BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Embodiments of the invention will now be described, by way of example only, with reference to the accompanying schematic drawings, in which:Fig. 1 depicts a lithographic system comprising a lithographic apparatus, a radiation source and an optical alignment system in accordance with the present disclosure.Fig. 2 schematically depicts a simplified cross-sectional view from the side of a sensor system in accordance with the present disclosure.Fig. 3 schematically depicts an example of a scanning movement of a first detector beneath an aerial image of a capture marker in accordance with the present disclosure.Fig. 4 schematically depicts an example of a signal output from a detector when the detector is moved in the Z-direction from below a focal plane to above the focal plane in accordance with the present disclosure.Fig. 5 schematically depicts an image position measurement in accordance with the present disclosure.Fig. 6 schematically depicts another image position measurement in accordance with the present disclosure.Fig. 7 shows a flow chart of an example image position measurement scan in accordance with the method of the present disclosure.Fig. 8 shows an example graph of known measurement disturbances in accordance with the present disclosure.Fig. 9 shows an example of a scanning movement during an image position measurement in accordance with the present disclosure.Fig. 10 shows a graph of sensitivities of an image position measurement in accordance with the present disclosure.Fig. 11A shows an example of an image position measurement in which the image is positioned at a center of the scanning area in accordance with the present disclosure.Fig. 11B shows an example of an image position measurement in which the image is positioned away from the center of the scanning are in accordance with the present disclosure.Fig. 12A shows the graph of Fig. 10 alongside sensitivities of image position measurements in which the aerial image is substantially collocated with respect to the center of the scanning area but performed at different movement parameters in accordance with the present disclosure.Fig. 12B shows the graph of Fig. 10 alongside sensitivities of an image position measurement performed at the same movement parameter but in which the aerial image is translated with respect to the center of the scanning area in accordance with the present disclosure.Fig. 13 shows five graphs of image position measurement sensitivity amplitudes at odd and even multiples of movement frequencies in accordance with the present disclosure.Fig. 14 shows a flowchart of an example method of determining a sensitivity of an image position measurement in accordance with the present disclosure.Fig. 15 shows four graphs demonstrating how known measurement disturbances and image position measurement sensitivities may coincide to affect a quality of an image position measurement in accordance with the present disclosure.Fig. 16A-C show methods of performing an image position measurement in accordance with the present disclosure.DETAILED DESCRIPTION

[0087] Fig. 1 schematically depicts a lithographic system comprising a radiation source SO, a lithographic apparatus LA and an optical alignment system 22, 24. The radiation source SO is configured to generate an EUV radiation beam B and to supply the EUV radiation beam B to the lithographic apparatus LA. The lithographic apparatus LA comprises an illumination system IL, a support structure MT configured to support a patterning device MA (e.g., a mask), a projection system PS and a substrate table WT configured to support a substrate W.

[0088] The illumination system IL is configured to condition the EUV radiation beam B before the EUV radiation beam B is incident upon the patterning device MA. Thereto, the illumination system IL may include a facetted field mirror device 10 and a facetted pupil mirror device 11. The faceted field mirror device 10 and faceted pupil mirror device 11 together provide the EUV radiation beam B with a desired cross-sectional shape and a desired intensity distribution. The illumination system IL may include other mirrors or devices in addition to, or instead of, the faceted field mirror device 10 and faceted pupil mirror device 11.

[0089] After being thus conditioned, the EUV radiation beam B interacts with the patterning device MA. As a result of this interaction, a patterned EUV radiation beam B’ is generated. The projection system PS is configured to project the patterned EUV radiation beam B’ onto the substrate W. For that purpose, the projection system PS may comprise a plurality of mirrors 13,14 which is configured to project the patterned EUV radiation beam B’ onto the substrate W held by the substrate table WT. The projection system PS may apply a reduction factor to the patterned EUV radiation beam B’, thus forming an image with features that are smaller than corresponding features on thepatterning device MA. For example, a reduction factor of 2 or 4 or 8 may be applied. Although the projection system PS is illustrated as having only two mirrors 13, 14 in Fig. 1, the projection system PS may include a different number of mirrors (e.g. six or eight mirrors). It will be appreciated that other types of optical components may be used in other types of lithographic apparatus. For example, a DUV lithographic apparatus may comprises lenses or other transmissive components instead of mirrors 10, 11 13, 14.

[0090] The substrate W may include previously formed patterns. Where this is the case, the lithographic apparatus LA aligns the image, formed by the patterned EUV radiation beam B’, with a pattern previously formed on the substrate W. The position of one or more alignment markers (not shown) on the substrate table are measured with respect to the position of one or more alignment markers 22 on the patterning device MA. This is done using sensor systems 24 which include the one or more alignment markers. This alignment of the patterning device MA with respect to the substrate table WT allows the patterning device MA to be aligned with the substrate W. The alignment markers 22 may comprise various different shapes and / or patterns. At least some of the patterns may be gratings.

[0091] A relative vacuum, i.e. a small amount of gas (e.g. hydrogen) at a pressure well below atmospheric pressure, may be provided in the radiation source SO, in the illumination system IL, and / or in the projection system PS.

[0092] The radiation source SO may be a laser produced plasma (LPP) source, a discharge produced plasma (DPP) source, a free electron laser (FEL) or any other radiation source that is capable of generating EUV radiation.

[0093] As has been described above, a lithographic apparatus LA may be used to expose portions of a substrate W in order to form a pattern in the substrate W. In order to improve the accuracy with which a desired pattern is transferred to a substrate W one or more properties of the lithographic apparatus LA may be measured. Such properties may be measured on a regular basis, for example before and / or after exposure of each substrate W, or may be measured more infrequently, for example, as part of a calibration process. Examples of properties of the lithographic apparatus LA which may be measured include a relative alignment of components of the lithographic apparatus LA. For example, measurements may be made in order to determine the relative alignment of the support structure MT for supporting a patterning device MA and the substrate table WT for supporting a substrate W. Determining the relative alignment of the support structure MT and the substrate table WT assists in projecting a patterned radiation beam B’ onto a desired portion of a substrate W. This may be particularly important when projecting patterned radiation onto a substrate W which includes portions which have already been exposed to radiation, so as to improve alignment of the patterned radiation with the previously exposed regions. Embodiments of the invention reduce a duration of such measurements.

[0094] Contamination will build up on sensors of the sensor system 24. In one example, contamination build up on an optical sensor of the sensor system 24 may cause the optical sensor to provide an inaccurate output. This in turn may cause poor alignment of a projected pattern with respect to a pattern already present on a substrate W. Embodiments of the invention reduce said contamination.

[0095] Measurements, such as the alignment measurement described above may be performed by illuminating a marker 22 (as schematically shown in Fig. 1) with radiation. A marker 22 may, for example, comprise a reflective feature which when placed in the field of view of an optical system (such as the lithographic apparatus LA of Fig. 1) appears in an image produced by the optical system. Alternatively, a transmissive marker may be used (e.g. as part of a DUV radiation measurement). Markers 22 described herein are suitable for use as a point of reference and / or for use as a measure of properties of the image formed by the optical system. For example, radiation that has interacted with the marker 22 may be used to determine an alignment of one or more components of the optical system.

[0096] In the embodiment which is shown in Fig. 1, the marker 22 forms part of a patterning device MA. One or more markers 22 may be provided on patterning devices MA used to perform lithographic exposures. A marker 22 may be positioned outside of a patterned region of the patterning device MA, which is illuminated with radiation during a lithographic exposure. In some embodiments, one or more markers 22 may additionally or alternatively be provided on the support structure MT. For example, a dedicated piece of hardware, often referred to as a fiducial, may be provided on the support structure MT. A fiducial may include one or more markers 22. For the purposes of this description a fiducial is considered to be an example of a patterning device. In some embodiments, a patterning device MA specifically designed for measuring one or more properties of the lithographic apparatus LA may be placed on the support structure MT in order to perform a measurement process. The patterning device MA may include one or more markers 22 for illumination as part of a measurement process.

[0097] The marker 22 may, for example, comprise a multilayer structure comprising layers of two or more materials having different refractive indices. Radiation may be reflected from interfaces between different layers of the marker 22. The layers may be arranged to provide a separation between interfaces which causes constructive interference between radiation reflected at different interfaces. The separation between interfaces which causes constructive interference between radiation reflected at different interfaces depends on the wavelength of the radiation. A multilayer structure may therefore be configured to preferentially reflect radiation of a given wavelength (e.g. EUV radiation) by providing a separation between layer interfaces which causes constructive interference between radiation of the given wavelength reflected from different interfaces. The marker 22 may comprise a plurality of reflective regions disposed on an absorbing layer in a periodic fashion (e.g. in the form of a line and space grating pattern).

[0098] In order to measure one or more properties of the lithographic apparatus LA, a sensor apparatus 24 (as shown schematically in Fig. 1) is provided to measure radiation which is output from the projection system PS. The sensor apparatus 24 may, for example, be provided on the substrate table WT as shown in Fig. 1. In order to perform a measurement process, the support structure MT may be positioned such that the marker 22 on the patterning device MA is illuminated with radiation. The substrate table WT may be positioned such that radiation which is reflected from the marker 22 is projected, by the projection system PS, onto the sensor apparatus 24. The sensor apparatus 24 may be in communication with a controller CN which may determine one or more properties of the lithographic apparatus LA from the measurements made by the sensor apparatus 24. In some embodiments a plurality of markers 22 and / or sensor apparatuses 24 may be provided and properties of the lithographic apparatus LA may be measured at a plurality of different field points (i.e. locations in a field or object plane of the projections system PS).

[0099] The position of an image (e.g. an aerial image) of the marker 22 in the radiation beam B may be measured by a sensor apparatus 24 positioned at a substrate W level (e.g. on the substrate table WT as shown in Fig. 1). The sensor apparatus 24 may be operable to detect the position of the image of the marker 22 in the radiation incident upon it. This may allow the alignment of the substrate table WT relative to the marker 22 on the pattering device MA to be determined. With knowledge of the relative alignment of the patterning device MA and the substrate table WT, the patterning device MA and the substrate table WT may be moved relative to each other so as to form a pattern (using the patterned radiation beam B’ reflected from the patterning device MA) at a desired location on the substrate W. The position of the substrate W on the substrate table WT may be determined using a separate measurement process.[000100] The sensor apparatus 24 may, for example, be a Transmission Image Sensor (TIS). A TIS is a sensor that may be used to measure the position at substrate W level of a projected aerial image of a marker 22 at the patterning device (which may be referred to as a mask or reticle) MA level. The TIS is configured to measure the image of the marker 22 using a transmission pattern with a radiation sensor 24 located underneath the transmission pattern. The transmission pattern may correspond to the pattern of the marker 22. For example, the transmission pattern may comprise a structure having substantially the same shape and / or substantially the same geometry of the projected aerial image of the marker 22. The measurement data produced by the sensor apparatus 24 may be used to measure the position of the mask MA with respect to the substrate table WT in six degrees of freedom (three in translation and three in rotation). In addition, the magnification and scaling of the projected image of the marker 22 may be measured.[000101] In use, the optical sensor 24 may be used to ensure alignment of the patterning device MA with the substrate table W (see Figure 1). A manner in which this may be achieved is illustrated schematically in Fig. 3. It will be appreciated that the method of the present disclosure may be used in combination with other types of markers (e.g. markers comprising a grating pattern) and other typesof scans (e.g. vertical scans and / or grating scans) than that shown in Fig. 3, and that the marker and scan shown in Fig. 3 merely represents one example of an image position measurement. The EUV radiation beam B is used to illuminate an alignment marker 22 on the patterning device MA. The resulting patterned beam B’ is projected by the projection system PS (see Fig. 1) to form an aerial image 44 of the alignment marker 22. The projection system PS typically includes a reduction factor, and as a result the aerial image 44 may be smaller than the alignment marker 22. The aerial image 44 is formed above (or on) the detector 28 of the optical sensor 24. Although only one alignment marker 44 and optical sensor 24 is depicted in Fig. 3, the same method may be used for other alignment markers and optical sensors.[000102] When aligning the patterning device MA relative to the substrate WT, the alignment may be performed in two parts. In a first part a so-called coarse-align is performed. In the coarse-align the substrate table WT is moved by a considerable distance in a scanning movement, typically a few hundred microns (e.g. 200 microns), such that the alignment marker 22 aerial image 44 passes fully across the first detector 28. The alignment marker 22 may be referred to as a capture marker, because it allows the coarse position of the substrate table WT with respect to the patterning device MA to be ‘captured’. The capture marker 22 may be a square. However, the alignment marker 22 may have any shape. Similarly, although the first detector 28 is square, it may have any shape. The detector 28 may have an area which is greater than the area of the alignment marker 22 aerial image 44. At the same time that the capture marker 22 aerial image 44 passes over the first detector 28, multiple periods of an aerial image of an X-direction grating may pass over an X-direction grating detector (not shown).[000103] The substrate table WT is then moved diagonally (in the +X and -Y directions). Following this, the substrate table WT is moved with a scanning movement in the Y-direction, typically by hundreds of microns (e.g. 200 microns). As a result, the capture marker 22 aerial image 44 passes fully over the first detector 28 in the Y -direction. At the same time that the capture marker 22 aerial image 44 passes over the first detector 28, a Y-direction grating aerial image may pass over multiple periods of a Y-direction grating detector (not shown). It will be appreciated that the method of the present disclosure may be used in combination with any measurement scan type and any marker type (e.g. aligning the alignment marker 22 to the detector 28 and / or aligning the X-direction grating to the X-direction grating detector and / or aligning the Y-direction grating to the Y-direction grating detector) and any measurement scan sequence combination. The X-direction grating and / or the Y-direction grating may also be referred to as a capture marker.[000104] The coarse-align allows the position of the substrate table WT to be determined with an accuracy of around a few tens of nanometers or better. Following this, the second alignment process is performed. The second alignment process may be referred to as a fine-align. In the fine-align, the substrate table WT is positioned such that the capture marker 22 aerial image 44 is positioned centrally over the detector 28, and X and Y direction grating aerial images are also centrallypositioned with respect to the X and Y grating detectors (not shown). A small movement, typically up to 2 microns in a diagonal direction, is then performed. The resulting signal from the X and Y grating detectors indicates the relative positions of the aerial images of the gratings and the X and Y grating detectors. This signal may be used to align the substrate table WT to the patterning device MA with an accuracy of a nanometer or with sub-nanometer accuracy. It will be appreciated that the method of the present disclosure may be used to align X and Y direction grating aerial images with respect to the X and Y grating detectors.[000105] During the coarse-align and the fine align, the alignment markers 22 on the patterning device MA are illuminated by the EUV radiation beam B, and as a result EUV radiation is incident upon the sensor system 24. This generates contamination due to interaction between the EUV radiation and gas molecules or other contaminants in the vicinity of the sensor system 24. During the fine align, as noted above, the aerial image 44 of the capture marker 22 is centrally located over the detector 28. During the coarse-align the substrate table WT and sensor system 24 are moved in a scanning motion beneath the aerial images 44 of the markers 22. As a result, EUV radiation is consistently incident upon a central area of the detector 28, and contamination builds up in the central areas. The contamination generally has the shape of the aerial image 44 of the capture marker 22. The same applies for other markers and detectors of the sensor system 24.[000106] Fig. 2 schematically depicts a simplified cross-sectional view from the side of a sensor system 24 in accordance with the present disclosure. The sensor system 24 may be provided on the substrate table WT. The sensor system 24 may comprise a plurality of optical sensors. The sensor system 24 comprises a substrate 40 within which one or more detectors 28 are formed. A layer of opaque material 42 is provided on the substrate 40, and apertures are formed in the opaque material above the detectors 28. The layer of opaque material 42 may be considered opaque relative to the multilayer structure of the marker 22. The layer of opaque material 42 may, for example, comprise a low refractive index attenuated phase shift mask absorber. The layer of opaque material 42 may comprise other materials. The size of each aperture controls the amount of EUV radiation which is incident on each detector 28. The sensor system 24 may comprise semiconductors and may be formed using lithography. The detector 28 may comprise a photodiode. A second optical sensor (not shown) may comprise a grating that is formed in the layer of opaque material 42. The grating may extend in the X-direction. A third optical sensor (not shown) may comprise a grating which is formed in the layer of opaque material 42. The grating may extend in the Y-direction.[000107] Fig. 3 schematically depicts a scanning movement of the first detector 28 beneath the aerial image 44 of the capture marker 22 during a coarse-align, and schematically depicts a signal S output from the first detector 28 when this is done. Other movements of the first detector 28 may be used to obtain an output signal. The signal S is proportional to the intensity I of light detected by the detector 28 as a function of time t. As depicted, the aerial image 44 starts outside of the area of the detector 28, and the signal from the first detector 28 is zero. The aerial image 44 moves in the X-direction relative to the detector 28, as schematically depicted by the dashed arrows. Whilst the dashed arrows of Fig. 3 depict the movement of the aerial image 44 relative to the detector 28, it will be understood that in practice it is movement of the detector 28 that causes the relative changes in position between the aerial image 44 and the detector 28. As the aerial image 44 overlaps the detector 28, the output signal increases linearly. The output signal reaches a maximum when the aerial image 44 is fully over the detector 28. The output signal remains at the maximum value until the aerial image 44 of the capture marker 22 starts to move off the detector 28 (i.e. the overlap between the aerial image 44 and the detector 28 starts to reduce). The signal falls to zero. The aerial image 44 is then moved diagonally relative to the detector 28 (as indicated by the dashed arrow), until the aerial image 44 is aligned with the detector 28 in the X-direction but separated from the detector in the Y- direction. A Y-direction scan is then performed. This generates the same output signal. The scanning movement of the detector 28 relative to the aerial image 44 is performed in perpendicular directions. References in this document to movement of the detector 28 may be interpreted as referring to movement of the sensor system 24.[000108] The sensor system 24 may also be used to ensure that the substrate table WT has the correct Z-direction position with respect to the focal plane of the projection system PS of the lithographic apparatus LA. For this, the substrate table WT and sensor system 24 may be moved in the Z-direction by a distance of, for example, about 100 microns. The Z-direction position may be determined with an accuracy of a few nanometers or better.[000109] Referring to Fig. 4, the effect of moving the detector 28 in the Z-direction when the aerial image 44 of the capture marker 22 is aligned with the center of the detector 28 is schematically depicted. The detector 28 is schematically depicted in the focal plane of the projection system PS. The patterned EUV radiation beam B’ which carries the aerial image 44 is also schematically depicted. The patterned radiation beam B’ has a minimum diameter at the focal plane of the projection system PS. When the detector 28 lies in the focal plane, an aerial image of the alignment marker 22 is in focus at the detector 28. As the detector 28 moves in the Z-direction away from the focal plane, the alignment marker image becomes less and less focused. That is, the alignment marker image is spread over a larger and larger distance.[000110] Fig. 4 schematically depicts a signal S output from the detector 28 when the detector is moved in the Z-direction (from below the focal plane to above the focal plane). The detector 28 starts below the focal plane, and thus the projected image 44 of the capture marker 22 is out of focus. As a result the EUV radiation is spread out, and some of the EUV radiation lies outside of the detector 28. As the detector 28 moves towards the focal plane of the projection system PS, the aerial image 44 of the capture marker 22 becomes focussed and thus smaller. The amount of EUV radiation incident upon the detector 28 increases, and the signal S increases. When the aerial image 44 of the capture marker 22 is close to being in focus, all of the EUV radiation is incident upon the detector 28, and the output signal S reaches a maximum. The signal S remains at the maximum whilst the image of thealignment marker 22 fully overlies the detector 28. The signal S once again decreases as the detector 28 moves away from the focal plane and part of the EUV radiation falls outside of the detector 28.[000111] Fig. 5 schematically depicts an image position measurement in accordance with the present disclosure. Performing an image position measurement, such as the coarse-align described above in relation to Fig. 3 (i.e. positioning the capture marker 22 aerial image 44 over the detector 28 and / or positioning the X and Y direction grating aerial images with respect to the X and Y grating detectors) or Fig. 4, may comprise determining a scan length that is necessary to find or “capture” the image 44. The scan length may be at least partially determined by a size (e.g. an area) of the image 44 of the marker 22 (e.g. because it may be desirable to sample the entire image 44, or a majority portion thereof, to accurately determine a center position of the image 44) and a measurement location uncertainty 52, 54 (e.g. lengths in the x and y directions which define an area in which it is estimated that the image 44 may be found). The measurement location uncertainty 52, 54 may correspond to the uncertainty of a previous image position measurement. The measurement location uncertainty 52, 54 may at least partially depend upon an accuracy of any positional movements and / or measurements performed before the image position measurement (e.g. in the example of Fig. 1, initial movements or sequences of the substrate table WT and / or the support structure MT to reach an initial position) and / or a stability of the components used to create and measure the image (e.g. the presence of mechanical vibrations that may adjust relative positions of said components). For example, an initial measurement location uncertainty 52, 54 may correspond to a worse-case scenario to ensure that the position of the image 44 of the marker 22 can be determined in an initial measurement. The measurement location uncertainty 52, 54 may therefore be understood as an area (or a distance if measuring along the z axis) across which the detector 28 scans in an attempt to find, or “capture”, the image 44. The measurement location uncertainty may be a length for a one dimensional measurement scan. The measurement location uncertainty 52, 54 may be an area for a two dimensional measurement scan. The measurement location uncertainty may be a volume for a three dimensional measurement scan. The measurement location uncertainty 52, 54 may therefore be referred to as a capture range.[000112] A method of performing image position measurements in accordance with the present disclosure comprises performing an image position measurement in at least partial dependence upon an estimated image position 50 and the measurement location uncertainty 52, 54 to determine a measured image position 56. The estimated image position 50 may be at least partially determined by, for example, knowledge of previous system calibrations (e.g. calibrations of the system or of modules of the system that perform the image position measurements) and / or previous image position measurements (e.g. learning from previous measurements performed the system and / or modules of the system) and / or knowledge of the optical system and coarse relative positions of the components therein (e.g. knowledge of the lithographic apparatus FA, coarse relative positions of the marker 22, the support structure MT and / or patterning device MA, an aperture of the projection system PS, etc.).The estimated image position 50 may be at least partially determined by one or more feed forward corrections (e.g. used to compensate for drifts of the system and / or modules of the system). The estimated image position 50 may at least partially depend upon one or any combination of, for example, an extent of the marker 22 and / or an extent of the detector 24 and / or an extent of a pupil plane of the optical system (e.g. the pupil of the lithographic apparatus LA of Fig. 1) and / or an illumination mode that is used to illuminate the marker 22. The illumination mode may be understood, for example, with reference to Fig. 1. The illumination system IL may be configured to condition the radiation beam B to form different illumination modes. The illumination modes may be defined by the number of regions of the pupil plane of the lithographic apparatus LA that are illuminated by the radiation beam B. For example, a dipole illumination mode may comprise two opposing portions of the pupil plane that are illuminated with radiation, a quadrupole illumination mode may comprise four regions of the pupil plane that are illuminated with radiation, etc. The pupil plane may be defined by the numerical aperture (NA) of the lithographic apparatus LA. That is, the pupil plane may be defined by the maximum angular distribution of radiation accepted by the lithographic apparatus LA. The pupil plane may be a Fourier transform plane of the plane in which the substrate W is disposed (which may be referred to as an object plane). Therefore, the distribution of electric field strength of the radiation in the pupil plane may be related to a Fourier transform of an object (for example, the marker 22) disposed in the object plane. In particular, the distribution of electric field strength of the radiation in the pupil plane (i.e. the angular distribution of radiation that is scattered by the object, such as the marker 22) may be given by a convolution of: (a) the distribution of electric field strength of the radiation in an illumination pupil plane (i.e. the angular distribution of radiation that illuminates the object, e.g. the marker 22) and (b) a Fourier transform of the object. [000113] In the example of Fig. 5, the detector 28 has an initial position in which a center of the detector 28 is collocated with the estimated image position 50. Relative movement is then introduced (e.g. as described with respect to Fig. 3) such that the detector 28 scans through the capture range 52, 54 in an attempt to find or “capture” the image 44. When the image 44 is found or “captured”, a measured position 56 of the image is determined. An image position error 60, 62 may then be determined in at least partial dependence upon the measured image position 56 and the estimated image position 50. For example, the image position error 60, 62 may correspond to the distance between the estimated image position 50 and the measured image position 56 along the x and y axes (or along the z axis if measuring a focus such as that shown in Fig. 4). The scan length and scan duration may scale proportionally. As can be seen in the example of Fig. 5, the image 44 is actually closer to the estimated image position 50 than would otherwise be suggested by the size of the capture range 52, 54. As such, it is desirable to reduce the capture range 52, 54 from the worst-case scenario for a subsequent image position measurement such that the scan length and duration are reduced. This increases a throughput of image position measurements and reduces sensor contamination whichmay be associated with a breakdown of chemicals caused by the radiation used to illuminate the marker 22.[000114] The method of performing image position measurements in accordance with the present disclosure comprises a step of determining an adapted measurement location uncertainty in at least partial dependence upon the image position error 60, 62. As previously discussed, the first measurement location uncertainty may correspond to a worst case scenario level of accuracy, whereas the image position error 60, 62 provides information regarding an actual accuracy of the estimated image position 50. As such, by using the image position error 60, 62, the measurement location uncertainty may be adapted to a value that more closely corresponds to an actual accuracy of the estimated image position 50, which makes a subsequent image position measurement of the present disclosure more efficient compared to known methods. If measurement location uncertainty is too small the image cannot be found, which results in a measurement failure and potentially a system halt needed to recalibrate and find an appropriate measurement location uncertainty. The worst case scenario may be devised to avoid such measurement failures at the cost of an increased measurement duration. Fig. 6 schematically depicts another image position measurement in accordance with the present disclosure. The subsequent image position measurement is performed in at least partial dependence upon the adapted measurement location uncertainty 72, 74. As can be seen on comparison between Fig. 5 and Fig. 6, the adapted measurement location uncertainty 72, 74 is smaller than the previous measurement location uncertainty 52, 54. That is, the adapted capture range 72, 74 of Fig. 6 is smaller than the initial capture range 52, 54 of Fig. 5. This means that the sensor 28 has a smaller area to scan when searching for the image 44, which in turn reduces a duration of the image position measurement of Fig. 6 compared to that of Fig. 5. The estimated image position 80 of Fig. 6 may correspond to the measured image position 56 of Fig. 5. As can be seen on comparison between Fig. 5 and Fig. 6, the image 44 of the marker 22 is in a different position. The position of the image 44 may vary between measurements due to, for example, mechanical noise in the system performing the image position measurement, limited accuracy in control loops used to control the system, inaccuracy in the positioning system used to move the detector, drifts such as thermal drifts, etc. Once the image 44 has been captured by the sensor 28, another image position error 90, 92 may be determined and logged to a memory. The subsequent image position error 90, 92 may then be used to determine another adapted measurement location uncertainty for use in a subsequent image position measurement. In this way, iterative improvement of the adapted measurement location uncertainty, and thus the image position measurement, may be achieved over time.[000115] Whilst Figs. 5 and 6 provide examples of image position measurements performed in the x, y plane (e.g. as described in relation to Fig. 3), it will be appreciated that the method of the present disclosure may similarly be performed in the z plane (e.g. as part of a focus measurement such as that described in relation to Fig. 4).[000116] Determining the adapted measurement location uncertainty 72, 74 may comprise providing the previous image position error 60, 62 as an input to an adaptive algorithm. The adaptive algorithm may be configured to determine the adapted measurement location uncertainty 72, 74 in at least partial dependence upon a proportionality relationship between the adapted measurement location uncertainty 72, 74 and the image position error 60, 62. For example, the adaptive algorithm may comprise the following equation: aCR = n ■ max (CE) where aCR is the adapted measurement location uncertainty 72, 74, n is a scaling factor, CE is the image position error 60, 62 and max (CE) is a largest previously determined value of image position error 62, 64. The scaling factor n may be any number. The scaling factor n may be a non-zero integer such as, for example, 1, 2, 3, 4, etc., or may be a non-integer such as, for example, 1.2, 3.45, etc. The scaling factor n may be used to increase or reduce a statistical performance of the image position measurement and / or prevent image position measurement failures (i.e. measurements in which the image 44 is not captured by the sensor 28 because the image 44 is outside of the capture range 52, 54). The scaling factor n may be selected using, for example, trial and error learning of previous image position measurements. The maximum image position error max(CE) may correspond to a largest value of image position error CE across an entire history of image position measurements. The maximum image position error max(CE) may correspond to a largest value of image position error CE in a given period of time dT. The maximum image position error max(CE) may correspond to a largest value of image position error CE across a number Noof most recent image position measurements. The maximum image position error max(CE) may correspond to a largest value of image position error CE in a selected subset of image position measurements.[000117] The scaling factor n and / or the period of time dT and / or the number Noof most recent image position measurements and / or the subset of image position measurements may be selected in at least partial dependence upon an image position measurement parameter under which the image position measurement occurs. The scaling factor n and / or the period of time dT and / or the number Noof most recent image position measurements and / or the subset of image position measurements may be stored as part of a record of image position measurements (e.g. in memory) in connection with the image position measurement parameter.[000118] As another example, the adaptive algorithm may comprise the following equation: aCR = n ■ max (CE) + mwhere m is a margin of error. The margin of error m may correspond to a minimum value of the adapted measurement location uncertainty 72, 74. For example, when the measurement location uncertainty represents an area across which a scanning sensor 28 searches for the image 44, the margin of error m may correspond to a minimum distance in a given direction x, y, z across which the scanning sensor 28 must scan in order to find, and thereby determine the position of, the image 44. For example, the margin of error m may at least partially depend upon an extent (e.g. an area) of the image 44. The margin of error m may be used to increase or reduce a statistical performance of the image position measurement. The margin of error m may be selected using, for example, trial and error learning of previous image position measurements.[000119] The scaling factor n and / or the margin of error m may be selected in at least partial dependence upon a desired statistical performance of the image position measurement. The desired statistical performance of the image position measurement may be at least partially based on previous image position measurement data. For example, a determined adapted measurement location uncertainty 72, 74 may be compared to a typical or average image position measurement performance to determine a failure rate (i.e. a percentage of image position measurements in which the image position error 60, 62 is greater than the adapted measurement location uncertainty 72, 74 (i.e. CE>aCR)) and this comparison may be used to adjust parameters such as the scaling factor n and / or the margin of error m to achieve the desired statistical performance.[000120] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty 72, 74 in at least partial dependence upon a statistical property of a plurality of image position errors 60, 62. It will be appreciated that only previously determined image position errors 60, 62 may be used for subsequent calculations of the adapted measurement location uncertainty 72, 74. The plurality of image position errors 60, 62, 90, 92 may form part of an image position error record. The image position error record may be formed by performing a plurality of image position measurements (such as those shown in Figs. 5 and 6) and recording each resulting image position error 60, 62, 90, 92 to memory. The adaptive algorithm may be configured to determine the adapted measurement location uncertainty 72, 74 in at least partial dependence upon a standard deviation of the plurality of image position errors 60, 62, 90, 92. The adaptive algorithm may comprise the following equation: aCR = n ■ STD(CE) + m where STD(CE) is the standard deviation of the plurality of image position errors 60, 62, 90, 92.[000121] The standard deviation of the plurality of image position errors STD(CE) may be determined for an entire history of image position measurements. The adaptive algorithm may comprise the following equation:where N is a total number of image position measurements from i = 1 to i = N and CEj is the corresponding image position error 60, 62, 90, 92 associated with a particular image position measurement i.[000122] The standard deviation of the plurality of image position errors STD(CE) may be determined for a number of image position errors CE across a given period of time dT. The adaptive algorithm may comprise the following equation:Et>tQ+dTCE(t)2STD(CE)(dT) =(N — l)dT where t is the time of a given image position measurement within the period of time dT which beings at a start time t0and finishes at an end time t0+ dT.[000123] The standard deviation of the plurality of image position errors STD(CE) may be determined for a number Noof most recent image position measurements. The adaptive algorithm may comprise the following equation:[000124] The adaptive algorithm may be configured to determine the adapted measurement location uncertainty 72, 74 in at least partial dependence upon a weighting of the plurality of image position errors 60, 62, 90, 92. The examples of relationships and equations provided above may assign equal weights to all image position errors 60, 62, 90, 92. Alternatively, a weighted moving standard deviation may be determined. The adaptive algorithm may comprise the following equation:where X(Wj2) = IV is a weight assigned to an associated image position error CEj. It will be appreciated that weighting may be applied to any of the above standard deviation relationships. An alternative weighting method involving an estimated measurement location uncertainty of the image position errorCE) based on, for example, a fitting process may comprise the following equation:[000125] W{ = — — The adaptive algorithm may be configured to attribute a greater weight to more XCEi recent image position measurements. The adaptive algorithm may comprise the following equation: w; = / (i - No) such that the data from the oldest image position measurements is assigned a weighting of zero whereas the data from the most recent image position measurement is assigned the largest weighting.[000126] The adaptive algorithm may be configured to determine an exponentially weighted standard deviation of image position errors. The adaptive algorithm may comprise the following equation:where a = [0,1] is a constant. The constant a may be configured to introduce an effective low-pass filtered memory in the standard deviation STD calculation and / or to reduce a weighting Wj of the oldest image position error 60, 62. The constant a may be configured to reduce the weighting of the oldest image position error 60, 62 asymptotically to zero (instead of, for example, via a discrete step) and thereby provide a smooth convergence of the calculated standard deviation.[000127] Determining a standard deviation of the image position error 60, 62, 90, 92 may be particularly desirable for situations in which an average of the plurality of image position errors 60, 62, 90, 92 is zero, as represented by the following relationship:This relationship may be assumed implicitly in one or more of the equations and relationships provided above. This relationship may not always be present. For example, a non-zero image position error average may be caused by systematic biases in a determination of the estimated image position 50, 80, e.g. by a preceding sequence. In these situations, it may be desirable to explicitly subtract the average for all standard deviation calculations discussed above (and using equivalent formulas or weighting, etc., in each case). The adaptive algorithm may comprise the following equation:This equation may not be robust for systematic errors in determining the estimated image position. The adaptive algorithm may account for this by being configured to determine a root mean square of the image position error 60, 62, 90, 92 rather than the standard deviation of the image position error. All implementations and variants discussed above may still be valid, thereby leading to a more conservative calculation of the adapted measurement location uncertainty.[000128] The average image position error CE across the entire history of image position measurements may provide an indication and / or estimation of any systematic error in the determination of the estimated image position EP. As such, the average image position error CE may be utilized when determining the estimated image position EP. For example, the average image position error CE may be provided as an input to an estimation algorithm. For example, the average image position error CE may be added or subtracted as a feed forward correction when determining the estimated image position EP, which may be represented by the following relationship:EP -> (EP - CE )The image position measurement may then be centered at a best estimated image position, and at the same time, the most accurate estimate of the standard deviation of the image position error 60, 62, 90, 92 (e.g. computed by removing the average image position error CE) may be used to determine the adapted measurement location uncertainty 72, 74.[000129] The adaptive algorithm may comprise a threshold relationship configured to set an upper limit and / or a lower limit to the adapted measurement location uncertainty 72, 74. The threshold relationship may take the following form:CRmin < aCR < CRmaxwhere CRminis a lower limit to the adapted measurement location uncertainty 72, 74 and CRmaxis a maximum limit to the adapted measurement location uncertainty 72, 74.[000130] When conditions allow (e.g. a system, such as the optical alignment system 22, 24 that forms part of the lithographic apparatus LA of Fig. 1, performing the image position measurements is regarded as being stable and / or the adapted measurement location uncertainty 72, 74 has reached a relatively stationary value) it may be desirable to adapt the scaling factor n and / or the margin of error m accordingly. For example, the scaling factor n and / or the margin of error may be adapted to achieve a more optimized adapted measurement location uncertainty 72, 74 due at least in part to a reduced risk of measurement location uncertainty error that is offered by said conditions (e.g. the measurement system 22, 24 being closer to a stable, equilibrium state). The adaptive algorithm may comprise the following equation:aCR(t) = n(t) ■ STD(CE) + m(t) where t is the time of a given image position measurement at which that measurement system 22, 24 is relatively stable and / or the measurement location uncertainty 72, 74 has reached a relatively stationary value.[000131] When these relatively stable conditions are not present, but long term drifts of values is of concern, the adaptive algorithm may be configured to integrate the determination of the adapted measurement location uncertainty 72, 74 with trending estimators such as, for example, Moving Average Convergence / Divergence (MACD) algorithms which may be configured to detect the convergence and / or divergence of the adapted measurement location uncertainty 72, 74 to timely correct for said drifts. Drifts of values may be determined by other means. For example, drifts may be detected by a fitting process of the image position error data 60, 62, 90, 92 and / or related statistical properties, e.g. on a purposely chosen time interval dT. Using a similar principle, the adaptive algorithm may be configured to correct for periodic variations in the image position error record 60, 62, 90, 92 (e.g. to account for daily and / or weekly and / or seasonal variations) by estimating one or more signatures of said variations using, for example, Principal Component Analysis. The adaptive algorithm may be configured to make use of information from known, correlating system Key Performance Indicators, which may highlight performance variation, and act upon it with adapted corrections. The Key Performance Indicators may include one or any combination of, for example, sensor measurement reproducibility, mechanical component (e.g. moveable stage) stabilities, optical component (e.g. lens) stabilities, etc.[000132] The adaptive algorithm may be configured to respond to the occurrence of a failure of an image position measurement, or of a system involved in the measurement, e.g. an image sensor error, in an appropriate manner. In any of the aggregation methods (i.e. equations and relationships) discussed above, individual data may be ignored in case of, for example, an image position measurement failure and / or image position measurement anomalies or outliers, and omitted from the computation of the adapted measurement location uncertainty. The situation may differ in the event of an image position measurement failure due to an error in the measurement location uncertainty itself. In these situations, the adapted measurement location uncertainty may be determined to be too small. In these situations, the method may comprise re-initializing one or more records (e.g. the image position error 60, 62, 90, 92 record) and restarting the process of determining the adapted measurement location uncertainty 72, 74 from the beginning. Alternatively or additionally, a correction term may be added to the one or more records. As another alternative or addition, the scaling factor n and / or the margin of error m may be adjusted.[000133] One or more of the data records (e.g. the image position error record 60, 62, 90, 92) may be configured to remain in the event of changes to other software and / or a system restart or reboot.The one or more data records may be configured to reset only in the event of a change (e.g. a hardware change) that would make the record history obsolete and worth refreshing.[000134] Fig. 7 shows a flow chart of an example TIS scan in accordance with the method of the present disclosure. A first step 100 of the method comprises performing an image position measurement 102 in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position AIpos104. In the example of Fig. 7, the image position measurement 102 is a TIS scan (such as the coarse align described above in relation to Figs. 2 and 5), and the image 44 is an aerial image (Al) of the marker 22.[000135] A second step 110 of the method comprises determining an image position error CE = ExPp0S~ ^Ipos in atleast partial dependence upon the measured image position 104 and the estimated image position Exppos. A third step 120 of the method comprises determining an adapted measurement location uncertainty a.CR(CE~) in at least partial dependence upon the image position error CE. The third step 120 may optionally comprise determining an updated statistical average of the image position error <CE>. A fourth step 130 of the method comprises performing another image position measurement 134 in at least partial dependence upon the adapted measurement location uncertainty a.CR(CE~). In the example of Fig. 7, the fourth step 130 comprises determining a TIS scan length 132 in dependence upon the size of the aerial image (Al size) 136 and the adapted measurement location uncertainty a.CR(CE~). In the example of Fig. 7, the expected aerial image size 136 at the sensor is determined through knowledge of the pupil of the alignment system (e.g. the pupil plane of the lithographic apparatus LA and / or the illumination mode of the illumination system IL and / or the magnification factor of the projection system PS of Fig. 1) and the sensor (e.g. the position of the sensor 24 of Fig. 1) 138. This information 138 may be determined through metrology measurements and calculations 140 (e.g. which may involve use of the measured position of the aerial image 104) and / or other system calibrations. Said metrology measurements and calculations 140 may also be used to determine an expected position (EXPpos) 142 of the aerial image in the next TIS scan 134. For example, the updated statistical average of the image position error <CE> may be used to correct the expected position (EXPpos). A starting point of the TIS scan in the capture range that corresponds to the adapted measurement location uncertainty a.CR(CE) may correspond to the expected position (EXPpos) 142 of the aerial image.[000136] As shown by the reduction in capture area between Fig. 5 and Fig. 6, even by reducing a magnitude of only a single measurement location uncertainty in a series of image position measurements, a throughput of the image position measurement process is improved. However, and as discussed above in connection with the adaptive algorithm, the method of the present disclosure may be used to incrementally improve the adapted measurement location uncertainty across a series of image position measurements, thereby greatly improving and / or optimizing the throughput of the image position measurement process over time. The method of the present disclosure may be used toreduce the adapted measurement location uncertainty down to its lowest natural limit, thereby reducing measurement duration and increasing measurement throughput. As the amount of available data increases (e.g. as stored as a record in memory), the adapted measurement location uncertainty 72, 74 may rapidly converge to an optimal value, resulting in an image position measurement that is optimized to a shortest possible duration whilst maintaining a desired level of accuracy.[000137] An image position measurement in accordance with the present disclosure may be performed in accordance with an image position measurement parameter. The method may comprise providing the image position error 60, 62 as an input to a plurality of different adaptive algorithms to determine a plurality of adapted measurement uncertainties 72, 74. The method may comprise storing the adaptive algorithm that produces a smallest acceptable measurement location uncertainty in memory such that the adaptive algorithm that produces the smallest acceptable measurement location uncertainty is associated with the image position measurement parameter. The method may comprise referring to the adaptive algorithm that produces the smallest acceptable measurement location uncertainty in a subsequent image position measurement that is performed in accordance with the image position measurement parameter. The smallest acceptable measurement location uncertainty may correspond to the measurement location uncertainty that has the smallest value whilst still being large enough to reduce or avoid the risk of image position measurement failures in which the image position is not determined by the image position measurement (e.g. because the measurement location uncertainty is too small and the image cannot be found). The desired robustness of an image position measurement, and thus of the smallest acceptable measurement location uncertainty, will vary between different systems and different users, and may be selected as desired.[000138] The method of performing image position measurements in accordance with the present disclosure may comprise performing a plurality of image position measurements to determine a plurality of image position errors 60, 62, 90, 92. Determining the adapted measurement location uncertainty 72, 74 may be at least partially dependent upon the plurality of image position errors 60, 62, 90, 92. The method may further comprise providing the plurality of image position errors 60, 62, 90, 92 as an input to the adaptive algorithm.[000139] The method in accordance with the present disclosure may comprise performing a plurality of image position measurements (such as those shown in Figs. 2-6) in accordance with an image position measurement parameter to determine a plurality of adapted measurement uncertainties 72, 74. The method may comprise storing the plurality of adapted measurement uncertainties 72, 74 in memory such that the plurality of adapted measurement uncertainties is associated with the image position measurement parameter. The method may comprise referring to the plurality of adapted measurement uncertainties in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.[000140] The image position measurement parameter may comprise one or any combination of the following parameters: image position measurement sequence value (e.g. the first image positionmeasurement (i.e. 1), the second image position measurement (i.e. 2), etc.), image position measurement scanning direction (e.g. perpendicular directions x, y along a measurement plane and / or a vertical scanning direction z), illumination setting (e.g. an illumination mode used to form the image such as, for example, dipole illumination, quadrupole illumination, etc.), time elapsed (i.e. how much time has passed since the first image position measurement in a plurality of image position measurements), image marker properties (i.e. material and / or optical properties of the marker that has been illuminated to form the image such as, for example, marker geometry, optical stack, etc.), image sensor (i.e. the detector used to detect the image), system identity (i.e. what system the image measurements are taking place in), system performance (e.g. an accuracy of the system in which the image measurements are taking place), etc.[000141] Image position measurements take place in systems that experience measurement disturbances. Fig. 8 shows an example graph of known measurement disturbances in accordance with the present disclosure. The known measurement disturbances may, for example, be present in the lithographic apparatus LA of Fig. 1 during an image position measurement such as, for example, an alignment process between the substrate W and the patterning device MA. The known measurement disturbances may, for example, comprise one or more mechanical frequencies (e.g. vibrations, movements, etc.) that may be present during an image position measurement, and may negatively affect a quality of the image position measurement. For example, measurement disturbances such as unwanted mechanical vibrations of components may reduce a contrast of an image formed during the image position measurement, which may in turn reduce an accuracy and / or reproducibility of the image position measurement. In the example of Fig. 8, the known measurement disturbances comprise vibrational frequencies ranging between 0Hz and 1000Hz. The known measurement disturbances may be referred to as noise due to the unwanted influence they have on image position measurements. A magnitude or intensity of the noise may vary across different frequencies. In the example of Fig. 8, relatively intense noise occurs at frequencies of about 120Hz, about 480Hz, about 660Hz, about 720Hz and about 910Hz. Different systems may comprise different intensities and / or frequencies of measurement disturbances.[000142] The known measurement disturbances may arise from a variety of sources. For example, with reference to Fig. 1, operational movement of components such as, for example, the support structure MT, the substrate table WT, reticle masking blades (not shown), illumination system IL components, such as the facetted field and pupil mirror devices 10, 11 (or comparable transmissive components in DUV systems), projection system PS components, such as the plurality of mirrors 13, 14 (or comparable transmissive components in DUV systems), etc. may contribute to the known measurement disturbances. As further examples, thermal effects associated with, for example, the generation of radiation in the source SO and / or the interaction of the radiation with various components (e.g. mirrors 10, 11, 13, 14 or comparable transmissive components in DUV systems) in the lithographic apparatus LA, and / or cooling systems (e.g. configured to provides flows of coolingfluid) may contribute to the known measurement disturbances. Some known measurement disturbances may substantially coincide with natural resonances of various components (e.g. mirrors 10, 11, 13, 14 or comparable transmissive components in DUV systems) of the lithographic apparatus LA, which may increase a negative effect of the measurement disturbances on the quality of image position measurements.[000143] The known measurement disturbances for a given system may be determined experimentally. For example, a wavefront interferometer sensor device (e.g. a sinusoidal phase modulation (SPM) interferometer) may be used to determine the presence and characteristics of measurement disturbances such as mechanical vibrations. Other sensor devices capable of measuring a displacement of an object or stage such as, for example, stage encoders may be used. In general, any suitable movement or displacement measurement system may be used. By monitoring the readings of an optical alignment system 22, 24 (such as that shown in Fig. 1), which measures the position of an alignment mark 22 on the substrate table WT (e.g. a TIS sensing system), and monitoring the readings of the wavefront interferometer sensor device (e.g. an interferometer device configured to measure the position of the substrate table WT) while the substrate table WT is not actuated, an analysis may be made of the measurement disturbances behaviour of the system. It will be understood that, when the substrate table WT is not actuated, in principle, the readings of both the alignment system 22, 24 and the interferometer (not shown) should match. However, in practice, this is not the case, due to the presence of unwanted measurement disturbances such as, for example, all kinds of undesired vibrations, undesired movements, measurement errors, etc.[000144] By analysing the readings of both the alignment system 22, 24 and the interferometer, the measurement disturbances may be determined experimentally. For example, the system may be assessed by generating Fourier transforms to the frequency domain of the intensities as measured in time by the alignment system 22, 24 and the position as measured in time by the interferometer. This analysis may provide information about resonance frequencies of the components of the system, e.g. the lithographic apparatus LA, such as the substrate table WT, (parts of) the projection system PL, the support structure MT and / or the illumination system IL. An output of this analysis includes a time series of an Aerial Image (Al) noise or error, i.e. a displacement (e.g. measured in nm) of the aerial image in time with respect to an equilibrium position. For example, the Y axis of the graph of Fig. 8 shows the results of the Fourier analysis of the time series of Aerial Image (Al) noise. In the example of Fig. 8, this is plotted as Amplitude Spectral Density (ASD) having units of nm / Hz1 / 2, which may be understood as the square root of the Power Spectral Density having units nm2 / Hz. The quantity ASD(f) * (df)1 / 2is the Root Mean Square (RMS) of the noise in the frequency bin df at frequency f. That is, Sum(ASD2(f) * df))1 / 2= RMS, where df is the frequency bin width. Not all measurement disturbances may contribute equally to the image position measurement (e.g. an aligned position measurement), so the sensitivity (SENS) of the image position measurement reproducibility is defined in a given direction, i.e. horizontal, vertical etc., to the measurement disturbances in the givendirection. The total reproducibility may be defined as (Sum(ASDA2(f) * SENS2(f)* df))1 / 2. It will be appreciated that other representations of noise or errors may be used.[000145] A common frequency component to both Fourier transforms may be due to measurement disturbances, e.g. vibrations of the substrate table WT, as these common frequency components will show in the readings of both the alignment sensor 22, 24 and the interferometer. Non-correlated frequency components or peaks may have other causes. For example, a frequency component that is only visible in the readings of the alignment sensor 22, 24, and not in the readings of the interferometer may be caused by, for example, noise in the electronics of the alignment system 22, 24. As another example, a frequency component that is only visible in the readings of the interferometer may relate to causes that only influence the interferometer measurement such as, for example, light noise (e.g. laser noise) of the interferometer. As further examples, a frequency component that is only visible in the readings of the alignment sensor 22, 24 and not in the readings of the interferometer may be caused by a movement of the interferometer relative to the alignment sensor 22, 24 that is not detected by the alignment sensor 22, 24, and / or out of phase relative movements, and / or movements detected by the alignment sensor 22, 24 but not by the interferometer (or vice versa). By analysing the variations of the measurement signals, the system may be analysed. By comparing the measurement results of the different measurement devices, causes may be analysed and distinguished. It will be appreciated that interferometer devices may be used to measure unwanted movements of components other than the substrate table WT to determine further measurement disturbances (e.g. measurement disturbances that affect a position of the aerial image 44 of the marker 22). Other movement detection devices and methods may be used.[000146] As discussed above, image position measurements often involve introducing relative movement between an image (such as an aerial image of an alignment mark) and a detector. Such relative movement may comprise, for example, movement of the sensor 24 relative to the position of the projection lens system PF and the mask MA in three orthogonal directions X, Y, and Z. By scanning along these three directions the intensity of the aerial image can be mapped as a function of the XYZ position of the sensor 24, for example in an image map (a 3D map), which comprises the coordinates of sampling locations and the intensity sampled at each location.[000147] Fig. 9 shows an example of a scanning movement during an image position measurement in accordance with the present disclosure. The arrows of Fig. 9 show a direction of movement of the substrate table WT along the scan path relative to the projection system PS during the scanning movement. Fig. 9 shows an example of a two dimensional measurement scan in the X-Y plane. A scan involving the Z axis may also be performed. In the example of Fig. 9, the substrate table WT, and thus the detector 24, is initially moved from left to right (i.e. from about -150nm to about 150nm along the horizontal axis). As the substrate table WT reaches the end of the left-to-right movement, the substrate table WT is moved in a positive direction along the vertical axis by about 50 nm (i.e. from about -250nm to about -200nm along the vertical axis). The substrate table WT is then movedfrom right to left in its new vertical position (i.e. from about 150nm to about -150nm along the horizontal axis). As the substrate table WT reaches the end of the right-to-left movement, the substrate table is moved in a positive direction along the vertical axis by about 50nm (i.e. from about - 200nm to about -150nm along the vertical axis). These horizontal and vertical movements of the substrate table WT are then repeated, forming a scanning pattern or path than scans a desired area. With reference to Figs. 5 and 6, the area to be scanned may correspond to the capture range 52, 54, 72, 74 when searching for a position of the aerial image 44 of the alignment marker 22 during an image position measurement as described above. The scan pattern shown in the example of Fig. 9 may be referred to as a warehouse scan, wapper, a supermarket path or supermarket scan. Other scan patterns may be used.[000148] A movement involved in an image position measurement, such as the one shown in the example of Fig. 9, may be characterized by one or more movement parameters. A movement parameter may comprise one or any combination of, for example, a distance (e.g. the horizontal distances of about 300nm and vertical distances of about 50nm in the example of Fig. 9), an area (e.g. the capture range 52, 54, 72, 74 of Figs. 5 and 6), a velocity (e.g. a velocity of the substrate table WT during the image position measurement), an acceleration (e.g. an acceleration of the substrate table WT during the image position measurement), a frequency (e.g. a frequency with which the substrate table WT moves between two end coordinates of a scanning or sweeping motion, such as the frequency of horizontal movements between -150nm to 150nm in the example of Fig. 9, during the search for the position of the image), and a duration of the image position measurement (e.g. the time it takes for the substrate table WT to complete the supermarket path in the example of Fig. 9).[000149] Due to, for example, sensor production tolerances, variations in image exposure settings, scan trajectory optimizations, etc., the scan move (and thereby one or more movement parameters) may vary, e.g. system by system LA, sensor by sensor 24, pupil illumination mode by pupil illumination mode, etc.. Movement parameters may be determined along different movement directions (e.g. the XYZ axes) and may vary along said directions. Movement parameters may be determined in at least partial dependence upon one or more of, for example, a size of a capture range 52, 54, 72, 74, a size of the detector 24 and / or the aerial image 44, limitations of an actuation system used to introduce the relative movement between the aerial image and the sensor 24 (e.g. maximum velocities, accelerations, etc.), sampling rates of the alignment system 22, 24, the alignment marker 22 being used, avoiding unwanted resonances in the wafer stage itself, etc. In general, movement parameters may be selected as desired in order to perform any desired image position measurement.[000150] The inventors have found that one or more movement parameters of an image position measurement may at least partially determine a sensitivity of the image position measurement to external disturbances, such the measurement disturbances discussed above. The sensitivity may comprise one or more frequencies at which a quality of the image position measurement is significantly negatively influenced. For example, the sensitivity may comprise one or moremechanical frequencies (e.g. vibrations, movements, etc.) at which the image position measurement is susceptible to unwanted resonance effects. The unwanted resonance effects may significantly reduce a quality (e.g. an accuracy and / or reproducibility) of the image position measurement.[000151] Fig. 10 shows a graph of sensitivities of an image position measurement in accordance with the present disclosure. The sensitivities of Fig. 10 may, for example, be present in the lithographic apparatus LA of Fig. 1 during an image position measurement such as, for example, an alignment process between the substrate W and the patterning device MA. The alignment process may, for example, involve a measurement scan such as the one shown in the example of Fig. 9. In the example of Fig. 10, the sensitivities comprise vibrational frequencies ranging between 0Hz and 1000Hz. A magnitude or intensity of the sensitivities may vary across different frequencies. In the example of Fig. 10, relatively intense sensitivities occur at frequencies of about 120Hz, about 240Hz, about 360Hz, about 480Hz, about 600Hz, about 720Hz, about 840Hz and about 960Hz. Different image position measurements may comprise different intensities and / or frequencies of sensitivities. In the example of Fig. 10, the sensitivities are associated with the horizontal axis. Sensitivities to external disturbances, which may themselves arise from different directions, may be determined along said different directions (e.g. the XYZ axes) and may vary along said directions. The reductions of image position measurement quality associated with the sensitivities may also vary along said directions.[000152] As discussed above, the inventors have found that one or more movement parameters of an image position measurement may at least partially determine a sensitivity of the image position measurement to external disturbances, such as the measurement disturbances discussed above. For example, frequencies of the most intense sensitivity peaks (e.g. see Fig. 10) may be least partially dependent upon both a movement frequency (e.g. a frequency with which the substrate table WT is moved between scan end points along a given axis) of an image position measurement, and a relative positioning between the image and a center of the scanning area (e.g. the capture range) in which the detector searches for the image. Fig. 11A shows an example of an image position measurement in which the image is positioned at a center of the scanning area in accordance with the present disclosure. Fig. 11B shows an example of an image position measurement in which the image is positioned away from the center of the scanning are in accordance with the present disclosure. Both Figs.11 A and B involve a supermarket scan path similar to that of Fig. 9. In the example of Figs. 11 A and B a center 200 of the scanning area is defined at coordinates of zero, zero along the horizontal and vertical axes. As can be seen on comparison, in Fig. 11A the aerial image 44 is collocated with the center 200 of the scanning area, whereas in Fig. 1 IB the aerial image 44 is offset with respect to the center 200 of the scanning area. In the example of Fig. 11B, a center of the aerial image 44 is located at coordinates of about -50nm along the horizontal axis and about Onm along the vertical axis.[000153] The inventors have found that for an image position measurement in which the aerial image is substantially collocated with respect to the center of the scanning area (e.g. in the example ofFig. 11 A), the most intense sensitivity peaks occur at even multiples of the movement frequency of the image position measurement. Fig. 10 is an example of this. In the example of Fig. 10, the movement frequency (i.e. a movement parameter) of the image position measurement is 60Hz, and the aerial image was substantially collocated with the center of the scanning area (e.g. as shown in Fig. 11A). As such, the most intense sensitivity peaks are found at even multiples of the movement frequency of the image position measurement, i.e. 2 X 60 = 120Hz; 4 X 60 = 240Hz; 6 X 60 = 360Hz; etc. Fig. 12A shows the graph of Fig. 10 alongside sensitivities of image position measurements in which the aerial image is substantially collocated with respect to the center of the scanning area (i.e. corresponding with Fig. 11 A) but performed at different movement parameters in accordance with the present disclosure. In the example of Fig. 12A, the movement parameter comprises the warehouse frequency or “wapper” frequency. In a first image position measurement the wapper frequency is 60Hz (and therefore corresponds to the graph of Fig. 10), in a second image position measurement the wapper frequency is 70Hz and in a third image position measurement the wapper frequency is 80Hz. As can be seen, the sensitivities of the image position measurement change when the movement parameter is adjusted. That is, changing the movement parameter changes the sensitivity of the image position measurement.[000154] The inventors have also found that for an image position measurement in which the aerial image is offset with respect to the center of the scanning area (e.g. in the example of Fig. 1 IB), relatively intense sensitivity peaks begin to arise at odd multiples (including a multiple of one, i.e. the movement frequency itself) of the movement frequency of the image position measurement. An example of this is shown in Fig. 12B, which shows the graph of Fig. 10 alongside sensitivities of an image position measurement performed at the same movement parameter but in which the aerial image is translated with respect to the center of the scanning area in accordance with the present disclosure. In the example of Fig. 12B, the dashed line shows the sensitivities when the aerial image is translated by 50nm with respect to the center of the scanning area (i.e. corresponding with Fig. 1 IB). As can be seen, due to the aerial image being offset rather than collocated with respect to the center of the scanning area, relatively intense sensitivity peaks are now found at odd multiples of the movement frequency of the image position measurement, i.e. 1 X 60 = 60Hz; 3 X 60 = 180Hz; 5 X 60 = 300Hz; etc. The offset between the aerial image and the center of the scanning area may be understood as a translation, i.e. a distance between an aligned position of the aerial image and the center of the scanning area. The offset between the aerial image and the center of the scanning area may be understood as a ratio of translation with respect to the horizontal scan length, i.e. how much the aerial image is offset with respect to the actual scanning movement.[000155] An intensity of each sensitivity peak at odd and / or even multiples of the movement frequency of the image position measurement may at least partially depend upon the offset or translation between the aerial image and the center of the scanning area. For example, an offset that is closer to zero (i.e. the aerial image is substantially collocated with the center of the scanning area)may introduce greater sensitivity peaks at even multiples of the movement frequency compared with odd multiples of the movement frequency, whilst an offset that is further from zero (i.e. the aerial image is not substantially collocated with the center of the scanning area) may introduce greater sensitivity peaks and odd multiples of the movement frequency compared with even multiples of the movement frequency. With increasing translation between the position of the aerial image of the center of the scanning area, the sensitivity peaks at even multiples of the movement frequency of the image position measurement may experience a reduction in intensity, whilst new relatively intense sensitivity peaks begin to appear and grow at odd multiples of the movement frequency of the image position measurement.[000156] Fig. 13 shows five graphs of image position measurement sensitivity amplitudes at odd and even multiples of movement frequencies in accordance with the present disclosure. The upper left graph shows sensitivity amplitudes at the movement frequency of the image position measurement (i.e. 1 X f). The upper right graph shows sensitivity amplitudes at two times the movement frequency of the image position measurement (i.e. 2 X f). The middle left graph shows sensitivity amplitudes at three times the movement frequency of the image position measurement (i.e. 3 X f). The middle right graph shows sensitivity amplitudes at four times the movement frequency of the image position measurement (i.e. 4 X f). The lower graph shows sensitivity amplitudes at five times the movement frequency of the image position measurement (i.e. 5 X f). For the upper left, middle left and lower graphs (i.e. at odd multiples of movement frequency), it can be seen that for translations at or near zero (i.e. when the aerial image and the center of the scanning area are substantially collocated), the sensitivity amplitudes are relatively low, whereas for translations further away from zero (i.e. when the aerial image and the center of the scanning area are not substantially collocated) the sensitivity amplitudes increase. In contrast, for the upper right and middle right graphs (i.e. at even multiples of movement frequency), it can be seen that for translations at or near zero (i.e. when the aerial image and the center of the scanning area are substantially collocated), the sensitivity amplitudes are relatively high, whereas for translations further away from zero (i.e. when the aerial image and the center of the scanning area are not substantially collocated) the sensitivity amplitudes decrease.[000157] It will be appreciated that movement parameters of an image position measurement (e.g. scan length and / or movement frequency) are determined before the image position measurement tasks place. As previously discussed, the measurement disturbances for a given system may be determined experimentally, and are therefore also known. However, the translation of the aerial image relative to the center of the scanning area is, by definition, unknown before the image position measurement takes place. That is, one does not need to perform an image position measurement if the aligned position of the image is already known. The translation of the aerial image relative to the center of the scanning area may vary between different image position measurements, leading to potentially large variations in the quality of image position measurements.[000158] The sensitivity of an image position measurement may be determined in multiple ways including one or more of, for example, offline via numerical simulation (using known measurement data); inline, via complex analytical modelling of the image position measurement movement parameters (e.g. a measurement scan movement) and its fit model, and / or via numerical curves preliminarily determined via simulations using theoretical aerial images, and within an image position measurement driver purposely scaled with respect to the relevant parameters such as, for example, the image position measurement movement frequency and aerial image translation with respect to the center of the scanning area. In general, determining the sensitivity of an image position measurement may comprise introducing a known measurement disturbance to the image position measurement and determining how the known measurement disturbance effects the results of the image position measurement compared to when the known measurement disturbance is not present. This may be repeated for a plurality of different known measurement disturbances and a more complete picture of the sensitivity of the image position measurement across a plurality of disturbance frequencies may be built.[000159] Fig. 14 shows a flowchart of an example method of determining a sensitivity of an image position measurement in accordance with the present disclosure. A first step 210 of the method comprises performing a first image position measurement. The first position measurement may be performed experimentally or via a numerical simulation. In the example of Fig. 14, this involves performing an alignment measurement, e.g. using the optical alignment system 22, 24 of Fig. 1, to determine a alignment of an aerial image (e.g. with respect to the center of a scanning area, such as the image position measurements shown in Figs. 11A and 11B). A second step 215 of the method comprises determining measurement data based on the first step 210. In the example of Fig. 14, this involves extracting aerial image position and intensity data based on the first image position measurement 210. A third step 220 of the method comprises introducing a known measurement disturbance to the image position measurement. In the example of Fig. 14, this involves injecting a simulated sinusoidal disturbance (e.g. having a known amplitude D, frequency to = 2TT , and phasep) along a direction (e.g. a horizontal direction or plane) such that the position of the aerial image changes in accordance with the following relationship: positionposition* = position + D ■ sin (rot + p~). Sub steps 221-224 of the third step 220 of the method are shown in Fig. 14. The first sub step 221 shows the measurement data of the second step 215 (i.e. position (Pos) and intensity (Int) data). The second sub step 222 shows the form of the known disturbance that is applied to the image position measurement which, in the example of Fig. 14, is a sinusoidal disturbance. Other forms of known disturbance may be used. The third sub step 223 shows how the position (Pos*) of the aerial image varies from its original measured position (Pos) as a result of the influence of the known disturbance. The fourth sub step 224 shows the varied measurement data, including the changed image position (Pos*) as a result of the known disturbance. A fourth step 225 of the method comprises determining the position of the image based on the varied measurement data 224. In theexample of Fig. 14, this involves processing the varied measurement data* (i.e. position* and intensity), e.g. using a known algorithm used to determine alignment information, to determine the aligned position of the aerial image (e.g. for all directions such as horizontal and vertical) following the application of the known disturbance. The third and fourth steps 220, 225 are repeated with a plurality of different phases 226 being used in the known measurement disturbance 222 to determine a plurality of image positions associated with a plurality of different known measurement disturbances. A fifth step 230 of the method comprises determining a variation of the image position with the plurality of different phases 226. In the example of Fig. 14, this involves determining three standard deviations 3STD of the resulting aligned positions AP* for each direction (e.g. XYZ) across all known measurement disturbances having different phases p. It will be appreciated that three standard deviations 3STD is merely an example, and that other measures of variation may be used. A sixth step 235 of the method comprises determining the sensitivity of the image position measurement at the chosen known frequency of measurement disturbance. In the example of Fig. 14, this involves determining the Root Mean Square RMS of the injected known measurement disturbance as follows:D RMS = -= V2 where D is the known amplitude of the known measurement disturbance. The Root Mean Square is then used to determine the sensitivity at the given frequency to as follows:3STD Sens( i)') = — — — v7RMSThe sensitivity may be determined along each different direction XYZ. Steps three to six 220, 225, 230, 235 of the method are repeated at different frequencies to of known measurement disturbance 237 to determine the sensitivity of the image position measurement at a plurality of different frequencies of interest. A seventh step 240 of the method comprises plotting the results of repetitions of the sixth step for different frequencies to form a sensitivity curve for the image position measurement, such as those shown in Figs. 10, 12A and 12B.[000160] Fig. 15 shows four graphs demonstrating how known measurement disturbances and image position measurement sensitivities may coincide to affect a quality of an image position measurement in accordance with the present disclosure. The top graph 250 of Fig. 15 shows the known measurement disturbances of Fig. 8. The upper middle graph 260 of Fig. 15 shows the image position measurement sensitivities at the three different wapper frequencies (i.e. 60Hz, 70Hz, 80Hz) of Fig. 12A. The lower middle graph 270 of Fig. 15 shows a contribution of the known measurement disturbances to the quality of the image position measurements associated with the sensitivity curvesof Fig. 12A. The lower middle graph 270 of Fig. 15 may be understood as being a convolution of the upper and upper middle graphs 250, 260 of Fig. 15. The lower graph 280 of Fig. 15 shows a cumulative contribution of the known measurement disturbances 250 of Fig. 8 to the quality of the image position measurements associated with the sensitivity curves 260 of Fig. 12A.[000161] As previously discussed, in the example of Fig. 8 (i.e. the upper graph 250 of Fig. 15), relatively intense measurement disturbances (e.g. mechanical vibrations) occurs at frequencies of about 120Hz, about 480Hz, about 660Hz, about 720Hz and about 910Hz. . As shown in the upper middle graph 260 of Fig. 15, and as previously discussed with respect to Fig. 10, the sensitivity curve associated with a wapper frequency of 60Hz (i.e. before an aspect of the image position measurement is adjusted in accordance with the present disclosure) has relatively intense sensitivity amplitudes at frequencies of about 120Hz, about 240Hz, about 360Hz, about 480Hz, about 600Hz, about 720Hz, about 840Hz and about 960Hz. . By comparing the sensitivity of the image position measurement to the known measurement disturbance (e.g. compared Fig. 8 to Fig. 10), at least partially overlapping sensitivities and measurement disturbances may be identified. In the example of Fig. 15, measurement disturbance frequencies of about 120Hz, 480Hz and about 720Hz substantially coincide with image position measurement sensitivities frequencies at about 120Hz, about 4800Hz and about 720Hz. Dashed lines are provided in Fig. 15 to show at least some of the frequencies at which the measurement disturbances and the sensitivities substantially coincide. As can be seen on the lower middle graph 270 of Fig. 15, these three substantially coinciding measurement disturbance and sensitivity frequencies are associated with relatively large contributions that negatively affect the quality of the image position measurement. A method of performing an image position measurement in accordance with the present disclosure may be used to reduce these negative effects on the quality of the image position measurement.[000162] Figs. 16A-C show methods of performing an image position measurement in accordance with the present disclosure. Like reference numerals are used to indicate like method steps. Each of the three methods begin with the same five steps 400-440. A first step 400 comprises initiating an image position measurement process. In the example of Figs. 16A-C, this is expressed as “Request scan”. For example, a user or program operating the lithographic apparatus LA of Fig. 1 may request that an image position measurement be performed, e.g. to align the reticle MA and substrate W. The image position measurement may involve a scanning movement such as, for example, that shown in Fig- 9.[000163] Second and third steps 410, 420 comprise determining a movement parameter of the image position measurement. In the example of Figs. 16A-C, this is split into two steps, namely the second step 410 which is expressed as “Define scan trajectory” and the third step 420 which is expressed as “Determine wapper freq”. The scan trajectory may, for example, correspond to a wapper, warehouse scan or supermarket scan path such as that shown in Fig. 9. Other scan paths may be used. The abovementioned movement frequency (e.g. a frequency of moving between twohorizontal end points of the supermarket scan path of Fig. 9) of an image position measurement may be referred to in the art as a wapper frequency or warehouse frequency or supermarket path frequency. As such, both the “scan trajectory” and the “wapper freq” are examples of movement parameters of the image position measurement. As previously discussed, the movement parameter may be determined in at least partial dependence upon one or more of, for example, a size of a capture range 52, 54, 72, 74, a size of the detector 24 and / or the aerial image 44, limitations of an actuation system used to introduce the relative movement between the aerial image and the sensor 24 (e.g. maximum velocities, accelerations, etc.), sampling rates of the alignment system 22, 24, the alignment marker 22 being used, avoiding unwanted resonances in the wafer stage itself, etc. In general, movement parameters may be selected as desired in order to perform any desired image position measurement. [000164] A fourth step 430 comprises determining a sensitivity of the image position measurement in at least partial dependence upon the movement parameter. In the example of Figs.l6A-C, this is expressed as “Determine sensitivity peaks”. For example, this may comprise determining sensitivity curves and / or amplitudes such as those shown in Figs. 10, 12A and 12B. As previously discussed, leading to potentially large variations in the quality of image position measurements. The sensitivity of an image position measurement may be determined in multiple ways as discussed above and as exemplified by Fig. 14.[000165] A fifth step 440 comprises comparing the sensitivity of the image position measurement to a known measurement disturbance. In the example of Figs. 16A-C, this is expressed as “Compare with known disturbance frequency list”. That is, the known measurement disturbances and the determined sensitivities may be provided in the form lists which may be compared to identify substantially coinciding data points. Alternatively, the known measurement disturbances and the determined sensitivities may be plotted on a graph, such as those shown in Fig. 15, and substantially coinciding peaks may be identified.[000166] The method according to the present disclosure comprises adjusting an aspect of the image position measurement in at least partial dependence upon the comparison between the sensitivity of the image position measurement and the known measurement disturbance. This adjustment may be performed in one or more different ways. In the example of Fig. 16A, the adjustment is performed at a sixth step 450, which comprises changing the movement parameter. In the example of Fig. 16A this is expressed as “Tune trajectory to avoid disturbance”. As previously discussed, the movement parameter at least partially determines the sensitivities of the image position measurement. By adjusting one or more movement parameters of the image position measurement, a sensitivity of the image position measurement that otherwise would substantially coincide with a known measurement disturbance may shifted away from the known measurement disturbance. For example, with reference to Fig. 15, a movement parameter, such as the movement frequency or wapper frequency, of the image position measurement may be changed such that the sensitivity frequencies at about 120Hz, about 480Hz and about 720Hz are removed or shifted to differentfrequencies (e.g. by changing the wapper frequency to 70Hz or 80Hz as shown in Fig. 15). In the example of Fig. 15, after the adjustment, the sensitivity curves associated with a wapper frequency of 70Hz and 80Hz no longer include peaks at frequencies of about 120Hz, about 480Hz and about 720Hz. Instead, the sensitivity curves include peaks at other frequencies, none of which substantially coincide with relatively intense known measurement disturbances at about 120Hz, about 480Hz and about 720Hz. The positive effect of this on the image position measurement quality can be seen in the lower middle and lower graphs 270, 280 of Fig. 15, in which the contributions after the adjustment (i.e. 70Hz and 80Hz) are less than the contributions before the adjustment (i.e. 60Hz) due to a reduction in overlapping sensitivity and measurement disturbance peaks. The movement frequency may be changed by, for example, changing a distance of the horizontal scan sections (e.g. changing from a horizontal scan length of 300nm) and / or changing a speed with which the substrate table is moved.[000167] As demonstrated by Fig. 15, not all sensitivity frequencies need to be removed or shifted to improve a quality of the image position measurement. The final step of Fig. 16A comprises performing the image position measurement with the adjusted movement parameter. This is expressed in Fig. 16A as “Perform scan”.[000168] Another way of adjusting an aspect of the image position measurement in at least partial dependence upon the comparison between the sensitivity of the image position measurement and the known measurement disturbance is shown in Fig. 16B. Unlike Fig. 16A, once the comparison between the known disturbances and the sensitivities has taken place and substantially coinciding (and therefore problematic) frequencies have been identified, the method proceeds to a penultimate step 460 comprising performing the image position measurement without adjusting the movement parameter, which is again expressed as “Perform scan”. However, in Fig. 16B this is followed by a final step in which adjusting the aspect of the image position measurement comprises filtering a result of the image position measurement. In the example of Fig. 16B, this is expressed as “Filter out unwanted frequencies”.[000169] Substantially coinciding frequencies identified on comparison between the known measurement disturbances and the determined sensitivities may be selected as frequencies to be filtered out of the results of the image position measurement. Selectively filtering out one or more problematic frequencies effectively mitigates or suppresses noise contributions associated with said frequencies. Once filtered out, the negative effects associated with those frequencies is reduced or removed, thereby improving a quality of the image position measurement. The filtering may be performed as a post-processing step, and may therefore be referred to as post-processing filtering. As an example of filters that could be applied in post-processing of an image position measurement such as an alignment scan, a ‘notch’ filter may be used. A notch filter maybe understood a filter that suppresses noise at a specified frequency while keeping all other frequencies unaltered. As such, a notch filter may be used to reduce or eliminate a specific unwanted disturbance (i.e. noise) from theimage position measurement. For example, a notch filter may be applied to a digitized scan intensity signal^, ... , IN~) of an image position measurement. The ‘notch’ may be implemented, for example, using a Finite Impulse Response (FIR), which may also be referred to in the art as a Moving average (MA), digital filter. In this example, the filtered image position measurement intensity signal ( / i, ... ,JN~) is a non-recursive linear combination of unfiltered scan readouts (l , ... , IN~) i.e.:where k = 1, ... , N and the hLcoefficients are determined based on the filter transfer function H(z) = £=ohj+1■ z~l, where z = e7", j = l and a> = 2nf. Alternatively, an Infinite Impulse Response (HR), which may also be referred to in the art as an Auto Regressive Moving Average (ARMA), digital filter may be used. In this example, the filtered image position measurement intensity signal may be a recursive linear combination of both the input signal (xlt... , xN~) and previous filtered values / k, i.e.:where the aLand ^coefficients are determined based on the filter desired transfer function H(z) = (X^obl+1- z-[) / (Ei^iai+i ’z')• ItwiH beappreciated that alternative filtering methods, including more complex solutions and filter types, may be used.[000170] In order to filter out identified frequencies, a sampling rate or sampling frequency of the image position measurement may be greater than the range of known measurement disturbances. A sampling rate of the image position measurement may be, for example, about 4 kHz or more, e.g. about 10 kHz. Given that the relative positioning of the aerial image and the center of the scanning area is known once the image position measurement has been performed, this information may be used to at least partially determine the frequencies for filtering. For example, the negative effects on image position measurement quality associated with odd multiples of movement frequency (i.e. the aerial image not being collocated with the center of the scanning area) may be reduced by filtering the image position measurement results so as to remove frequencies that match the measurement movement frequency from the results.[000171] In Fig. 16C, the adjustments used in Fig. 16A and Fig. 16B are combined. That is, in the example of Fig. 16C, once the sensitivities and known measurement disturbances have been compared, a sixth step 450 comprises adjusting the aspect of the image position measurement based on the comparison by changing the movement parameter as was the case in Fig. 16A. A seventh step 460 comprises performing the image position measurement using the changed movement parametersuch that the extent of substantially overlapping sensitivity and known measurement disturbance frequencies is reduced, e.g. as demonstrated in Fig. 15. An eighth step 470 comprises adjusting an aspect of the image position measurement by filtering the result of the image position measurement to further reduce the negative contribution of problematic frequencies to the quality of the image position measurement.[000172] The method of performing image position measurements in accordance with the present disclosure may be used as part of a method of aligning first and second components. For example, the method may be used to align the patterning device MA and the substrate W of the lithographic apparatus LA of Fig. 1 through the use of, for example, a plurality of TIS scans performed using the marker 22 and sensor 24. The alignment method may comprise illuminating a marker 22 to form an image 44 of the marker 22. The method may comprise projecting the image 44 of the marker 22 onto a sensor 24 (e.g. using the projection system PS of Fig. 1). The method may comprise adjusting a relative positioning between the first component (e.g. the patterning device MA) and the second component (e.g. the substrate W) and using the sensor 24 to detect the image 44 of the marker 22. The method may comprise performing image position measurements of the image 44 of the marker 22 in accordance with the method of performing image position measurements described in any of the examples provided above. The method may comprise projecting (e.g. using the projection system PS) a patterned beam of radiation B’ onto a substrate W. The first component may be a patterning device MA (e.g. a reticle) configured to impart a radiation beam B with a pattern in its cross-section to form the patterned radiation beam B’ . The second component may be the substrate W.[000173] A method according to an embodiment of the invention may be performed by a computing device. The device may comprise a central processing unit (“CPU”) to which is connected a memory. The method described herein may be implemented in code (software) stored on a memory comprising one or more storage media, and arranged for execution on a processor comprising on or more processing units. The storage media may be integrated into and / or separate from the CPU. The code, which may be referred to as instructions, is configured to be fetched from the memory and executed on the processor to perform operations in line with embodiments discussed herein. Alternatively it is not excluded that some or all of the functionality of the CPU is implemented in dedicated hardware circuitry, or configurable hardware circuitry like an FPGA. In general, the method may be performed by a processor.[000174] The computing device may comprise an input configured to enable a user to input data into a software program running on the CPU. The input device may comprise a mouse, keyboard, touchscreen, microphone etc. The computing device may further comprise an output device configured to output results of measurements to a user.[000175] For example, a computer program comprising computer readable instructions may be configured to cause a computer to carry out the method of performing image position measurements described in any of the examples provided above. In addition, a computer readable medium maycarry the computer program. A computer apparatus (such as the controller CN of Fig. 1) for controlling an image position measurement system (such as the alignment system 22, 24 of the lithographic apparatus LA of Fig. 1) may comprise a memory storing processor readable instructions. The computer apparatus CN may comprise a processor arranged to read and execute instructions stored in said memory. Said processor readable instructions may comprise instructions arranged to control the computer to carry out the method of performing image position measurements described in any of the examples provided above.[000176] Although specific reference may be made in this text to the use of lithographic apparatus in the manufacture of ICs, it should be understood that the lithographic apparatus described herein may have other applications. Possible other applications include the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, flat-panel displays, liquidcrystal displays (LCDs), thin-film magnetic heads, etc.[000177] Although specific reference may be made in this text to embodiments of the invention in the context of a lithographic apparatus, embodiments of the invention may be used in other apparatus. Embodiments of the invention may form part of a mask inspection apparatus, a metrology apparatus, or any apparatus that measures or processes an object such as a wafer (or other substrate) or mask (or other patterning device). These apparatus may be generally referred to as lithographic tools. Such a lithographic tool may use vacuum conditions or ambient (non-vacuum) conditions.[000178] Although specific reference may have been made above to the use of embodiments of the invention in the context of optical lithography, it will be appreciated that the invention, where the context allows, is not limited to optical lithography and may be used in other applications, for example imprint lithography.[000179] Where the context allows, embodiments of the invention may be implemented in hardware, firmware, software, or any combination thereof. Embodiments of the invention may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g. carrier waves, infrared signals, digital signals, etc.), and others. Further, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be appreciated that such descriptions are merely for convenience and that such actions in fact result from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc. and in doing that may cause actuators or other devices to interact with the physical world.[000180] While specific embodiments of the invention have been described above, it will be appreciated that the invention may be practiced otherwise than as described. The descriptions aboveare intended to be illustrative, not limiting. Thus it will be apparent to one skilled in the art that modifications may be made to the invention as described without departing from the scope of the claims set out below. Other aspects of the invention are set-out as in the following numbered clauses.1. A method of performing image position measurements comprising: performing an image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position; determining an image position error in at least partial dependence upon the measured image position and the estimated image position; determining an adapted measurement location uncertainty in at least partial dependence upon the image position error; and, performing another image position measurement in at least partial dependence upon the adapted measurement location uncertainty.2. The method of clause 1, wherein determining the adapted measurement location uncertainty comprises providing the image position error as an input to an adaptive algorithm.3. The method of clause 2, wherein the adaptive algorithm is configured to determine the adapted measurement location uncertainty in at least partial dependence upon a proportionality relationship between the adapted measurement location uncertainty and the image position error.4. The method of clause 2 or clause 3, wherein the adaptive algorithm is configured to determine the adapted measurement location uncertainty in at least partial dependence upon a statistical property of a plurality of image position errors.5. The method of clause 4, wherein the adaptive algorithm is configured to determine the adapted measurement location uncertainty in at least partial dependence upon a weighting of the plurality of image position errors.6. The method of any of clauses 2 to 5, wherein the adaptive algorithm comprises a threshold relationship configured to set an upper limit and / or a lower limit to the adapted measurement location uncertainty.7. The method of any of clauses 2 to 6, wherein the image position measurement is performed in accordance with an image position measurement parameter, wherein the method comprises: providing the image position error as an input to a plurality of different adaptive algorithms to determine a plurality of adapted measurement uncertainties; storing the adaptive algorithm that produces a smallest acceptable measurement location uncertainty in memory such that the adaptive algorithm that produces the smallest acceptable measurement location uncertainty is associated with the image position measurement parameter; and, referring to the adaptive algorithm that produces the smallest acceptable measurement location uncertainty in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.8. The method of any preceding clause, comprising performing a plurality of image position measurements to determine a plurality of image position errors, wherein determining the adapted measurement location uncertainty is at least partially dependent upon the plurality of image position errors.9. The method of clause 8 when dependent on any of clauses 2 to 8, comprising providing the plurality of image position errors as an input to the adaptive algorithm.10. The method of any preceding clause, comprising: performing a plurality of image position measurements in accordance with an image position measurement parameter to determine a plurality of adapted measurement uncertainties; storing the plurality of adapted measurement uncertainties in memory such that the plurality of adapted measurement uncertainties is associated with the image position measurement parameter; and, referring to the plurality of adapted measurement uncertainties in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.11. A method of aligning first and second components comprising: illuminating a marker to form an image of the marker; projecting the image of the marker onto a sensor; adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the mark; and, performing image position measurements of the image of the marker in accordance with any preceding clause.12. The method of clause 11, comprising projecting a patterned beam of radiation onto a substrate, wherein the first component is a patterning device configured to impart a radiation beam with a pattern in its cross-section to form the patterned radiation beam, and wherein the second component is the substrate.13. A computer program comprising computer readable instructions configured to cause a computer to carry out the method according to any preceding clause.14. A computer readable medium carrying a computer program according to clause 13.15. A computer apparatus for controlling an image position measurement system comprising: a memory storing processor readable instructions; and a processor arranged to read and execute instructions stored in said memory; wherein said processor readable instructions comprise instructions arranged to control the computer to carry out the method according to any of clauses 1 to 12.16. An optical alignment system comprising: an illumination system configured to condition radiation; a marker configured to impart the radiation with a pattern to form patterned radiation; a projection system configured to collect the patterned radiation and form an image of the marker;a sensor apparatus configured to detect the image of the marker; a controller configured to control the optical alignment system to carry out the method according to any of clauses 1 to 12.17. A lithographic apparatus comprising: the optical alignment system of clause 16; a support structure constructed to support a patterning device, the patterning device being capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam, wherein the marker forms part of the support structure or the patterning device; and, a substrate table constructed to hold a substrate, wherein the sensor apparatus forms part of the substrate table, wherein the projection system is configured to project the patterned radiation beam onto the substrate, wherein the optical alignment system is configured to determine an alignment between the patterning device and the substrate.18. A lithographic exposure method comprising: using the method of clause 12 to align the patterning device and the substrate; using the patterning device to impart the radiation beam with the pattern in its cross-section to form the patterned radiation beam; and, projecting the patterned radiation beam onto the substrate.19. A method of performing an image position measurement comprising: determining a movement parameter of the image position measurement; determining a sensitivity of the image position measurement in at least partial dependence upon the movement parameter; comparing the sensitivity of the image position measurement to a known measurement disturbance; and, adjusting an aspect of the image position measurement in at least partial dependence upon the comparison between the sensitivity of the image position measurement and the known measurement disturbance.20. The method of clause 19, wherein adjusting the aspect of the image position measurement comprises changing the movement parameter.21. The method of clause 19 or clause 20, wherein adjusting the aspect of the image position measurement comprises filtering a result of the image position measurement.22. The method of any of clauses 19 to 21, wherein comparing the sensitivity to the known measurement disturbance comprises: determining a sensitivity frequency spectrum; determining a known measurement disturbance frequency spectrum; and, identifying a peak of the sensitivity frequency spectrum that at least partially coincides with a peak of the known measurement disturbance frequency spectrum.23. The method of any of clauses 19 to 22, comprising: performing the image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position; determining an image position error in at least partial dependence upon the measured image position and the estimated image position; determining an adapted measurement location uncertainty in at least partial dependence upon the image position error; determining an adapted movement parameter in at least partial dependence upon the adapted measurement location uncertainty; determining an adapted sensitivity in at least partial dependence upon the adapted movement parameter; comparing the adapted sensitivity to the known measurement disturbance; adjusting a subsequent image position measurement in at least partial dependence upon: the comparison between the adapted sensitivity and the known measurement disturbance; and, the adapted measurement location uncertainty.24. The method of clause 23, wherein determining the adapted measurement location uncertainty comprises providing the image position error as an input to an adaptive algorithm.25. The method of clause 24, wherein the adaptive algorithm is configured to determine the adapted measurement location uncertainty in at least partial dependence upon a proportionality relationship between the adapted measurement location uncertainty and the image position error.26. A method of aligning first and second components comprising: illuminating a marker to form an image of the marker; projecting the image of the marker onto a sensor; adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the marker; and, performing an image position measurement of the image of the marker in accordance with any of clauses 19 to 25.27. The method of clause 26, comprising projecting a patterned beam of radiation onto a substrate, wherein the first component is a patterning device configured to impart a radiation beam with a pattern in its cross-section to form the patterned radiation beam, and wherein the second component is the substrate.28. A lithographic exposure method comprising: using the method of clause 26 to align the patterning device and the substrate; using the patterning device to impart the radiation beam with the pattern in its cross-section to form the patterned radiation beam; and, projecting the patterned radiation beam onto the substrate.29. A computer program comprising computer readable instructions configured to cause a computer to carry out the method according to any of clauses 19 to 28.30. A computer readable medium carrying a computer program according to clause 29.31. A computer apparatus for controlling an image position measurement system comprising: a memory storing processor readable instructions; and a processor arranged to read and execute instructions stored in said memory; wherein said processor readable instructions comprise instructions arranged to control the computer to carry out the method according to any of clauses 19 to 28.32. An optical alignment system comprising: an illumination system configured to condition radiation; a marker configured to impart the radiation with a pattern to form patterned radiation; a projection system configured to collect the patterned radiation and form an image of the marker; a sensor apparatus configured to detect the image of the marker; a controller configured to control the optical alignment system to carry out the method according to any of clauses 19 to 28.33. A lithographic apparatus comprising: the optical alignment system of clause 32; a support structure constructed to support a patterning device, the patterning device being capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam, wherein the marker forms part of the support structure or the patterning device; and, a substrate table constructed to hold a substrate, wherein the sensor apparatus forms part of the substrate table, wherein the projection system is configured to project the patterned radiation beam onto the substrate, wherein the optical alignment system is configured to determine an alignment between the patterning device and the substrate.

Claims

CLAIMS1. A method of performing image position measurements comprising: performing an image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position; determining an image position error in at least partial dependence upon the measured image position and the estimated image position; determining an adapted measurement location uncertainty in at least partial dependence upon the image position error; and, performing another image position measurement in at least partial dependence upon the adapted measurement location uncertainty.

2. The method of claim 1, wherein determining the adapted measurement location uncertainty comprises providing the image position error as an input to an adaptive algorithm.

3. The method of any preceding claim, comprising performing a plurality of image position measurements to determine a plurality of image position errors, wherein determining the adapted measurement location uncertainty is at least partially dependent upon the plurality of image position errors.

4. The method of any preceding claim, comprising: performing a plurality of image position measurements in accordance with an image position measurement parameter to determine a plurality of adapted measurement uncertainties; storing the plurality of adapted measurement uncertainties in memory such that the plurality of adapted measurement uncertainties is associated with the image position measurement parameter; and, referring to the plurality of adapted measurement uncertainties in a subsequent image position measurement that is performed in accordance with the image position measurement parameter.

5. A method of aligning first and second components comprising: illuminating a marker to form an image of the marker; projecting the image of the marker onto a sensor; adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the mark; and, performing image position measurements of the image of the marker in accordance with any preceding claim.

6. An optical alignment system comprising: an illumination system configured to condition radiation; a marker configured to impart the radiation with a pattern to form patterned radiation; a projection system configured to collect the patterned radiation and form an image of the marker; a sensor apparatus configured to detect the image of the marker; a controller configured to control the optical alignment system to carry out the method according to any of claims 1 to 5.

7. A lithographic apparatus comprising: the optical alignment system of claim 6; a support structure constructed to support a patterning device, the patterning device being capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam, wherein the marker forms part of the support structure or the patterning device; and, a substrate table constructed to hold a substrate, wherein the sensor apparatus forms part of the substrate table, wherein the projection system is configured to project the patterned radiation beam onto the substrate, wherein the optical alignment system is configured to determine an alignment between the patterning device and the substrate.

8. A method of performing an image position measurement comprising: determining a movement parameter of the image position measurement; determining a sensitivity of the image position measurement in at least partial dependence upon the movement parameter; comparing the sensitivity of the image position measurement to a known measurement disturbance; and, adjusting an aspect of the image position measurement in at least partial dependence upon the comparison between the sensitivity of the image position measurement and the known measurement disturbance.

9. The method of claim 8, wherein adjusting the aspect of the image position measurement comprises changing the movement parameter.

10. The method of claim 8 or claim 9, wherein adjusting the aspect of the image position measurement comprises filtering a result of the image position measurement.

11. The method of any of claims 8 to 10, wherein comparing the sensitivity to the known measurement disturbance comprises: determining a sensitivity frequency spectrum; determining a known measurement disturbance frequency spectrum; and, identifying a peak of the sensitivity frequency spectrum that at least partially coincides with a peak of the known measurement disturbance frequency spectrum.

12. The method of any of claims 8 to 11, comprising: performing the image position measurement in at least partial dependence upon an estimated image position and a measurement location uncertainty to determine a measured image position; determining an image position error in at least partial dependence upon the measured image position and the estimated image position; determining an adapted measurement location uncertainty in at least partial dependence upon the image position error; determining an adapted movement parameter in at least partial dependence upon the adapted measurement location uncertainty; determining an adapted sensitivity in at least partial dependence upon the adapted movement parameter; comparing the adapted sensitivity to the known measurement disturbance; adjusting a subsequent image position measurement in at least partial dependence upon: the comparison between the adapted sensitivity and the known measurement disturbance; and, the adapted measurement location uncertainty.

13. A method of aligning first and second components comprising: illuminating a marker to form an image of the marker; projecting the image of the marker onto a sensor; adjusting a relative positioning between the first component and the second component and using the sensor to detect the image of the marker; and, performing an image position measurement of the image of the marker in accordance with any of claims 8 to 12.

14. An optical alignment system comprising: an illumination system configured to condition radiation; a marker configured to impart the radiation with a pattern to form patterned radiation; a projection system configured to collect the patterned radiation and form an image of the marker; a sensor apparatus configured to detect the image of the marker;a controller configured to control the optical alignment system to carry out the method according to any of claims 8 to 13.

15. A lithographic apparatus comprising: the optical alignment system of claim 14; a support structure constructed to support a patterning device, the patterning device being capable of imparting the radiation with a pattern in its cross-section to form a patterned radiation beam, wherein the marker forms part of the support structure or the patterning device; and, a substrate table constructed to hold a substrate, wherein the sensor apparatus forms part of the substrate table, wherein the projection system is configured to project the patterned radiation beam onto the substrate, wherein the optical alignment system is configured to determine an alignment between the patterning device and the substrate.

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