Metrology methods, apparatus and computer programs

Abstract

A method of improving the measurement of a parameter of interest is disclosed. The method comprises obtaining metrology data comprising a plurality of measured values of the parameter of interest relating to one or more targets on a substrate, each measured value relating to a different measurement combination of a target of the one or more targets and a measurement condition of the target, and asymmetry metric data relating to an asymmetry of the one or more targets. Based on an assumption that there is a common true value of the parameter of interest across the measurement combinations, a respective relationship is determined for each of the measurement combinations relating the true value of the parameter of interest to the asymmetry metric data. The relationships are used to improve the measurement of the parameter of interest.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to US application 63 / 049,897, filed July 9, 2020, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present invention relates to measurement methods and apparatus, for example, that can be used to manufacture devices by photolithography, and to methods for manufacturing devices using photolithography. Background Technology

[0004] A photolithography apparatus is a machine that applies a desired pattern onto a substrate (typically onto a target portion of the substrate). Photolithography apparatus can be used, for example, in the fabrication of integrated circuits (ICs). In this case, a patterning apparatus (which is alternatively called a mask or photomask) can be used to generate a circuit pattern to be formed on a single layer of the IC. This pattern can be transferred onto a target portion (e.g., a portion including a die, a die, or several dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is typically performed by imaging onto a layer of radiation-sensitive material (resist) disposed on the substrate. Typically, a single substrate will contain a network of adjacent target portions patterned sequentially. During the photolithography process, it is often necessary to measure the created structure, for example, for process control and verification. Various tools are known for performing such measurements, including scanning electron microscopes, which are typically used to measure critical dimensions (CD), and specialized tools for measuring overlap, the alignment accuracy of two layers in a device. Overlap can be described based on the degree of misalignment between two layers. For example, a reference to a 1nm measurement overlap can describe the case where two layers are misaligned by 1nm.

[0005] Recently, various forms of scatterers have been developed for use in the field of photolithography. These devices guide a radiation beam onto a target and measure one or more properties of the scattered radiation—for example, the intensity varying with wavelength at a given reflection angle; the intensity varying with reflection angle at one or more wavelengths; or the polarization varying with reflection angle—to obtain a “spectrum” from which the properties of interest of the target can be determined. The determination of the properties of interest can be performed using various techniques: for example, reconstructing the target using iterative methods such as rigorous coupled-wave analysis or the finite element method; library retrieval; and principal component analysis.

[0006] Traditional scatterometers use relatively large targets (e.g., 40 μm × 40 μm gratings), and the measurement beam produces a spot smaller than the grating (i.e., the grating is not fully filled). This simplifies the mathematical reconstruction of the target, as it can be considered infinite. However, to reduce the target size, for example to 10 μm × 10 μm or smaller, such that the target can be positioned within a product feature rather than within a scribing, a metrology has been proposed that fabricate the grating smaller than the measurement point (i.e., the grating is overfilled). Typically, such targets are measured using a dark-field scatterometer, where zero-order diffraction (corresponding to specular reflection) is blocked, and only higher orders are processed. Examples of dark-field metrology can be found in international patent applications WO2009 / 078708 and WO2009 / 106279, which are incorporated herein by reference in their entirety. Further developments of this technique have been described in patent publications US20110027704A, US20110043791A, and US20120242940A. The contents of all these applications are also incorporated herein by reference. Diffraction-based overlap using dark-field detection with diffraction order enables overlap measurements on small targets. These targets can be smaller than the illumination spot and can be surrounded by product structures on a wafer. Targets can include multiple gratings that can be measured in a single image.

[0007] In known metrology techniques, overlapping measurements are obtained by measuring a target twice under specific conditions, while simultaneously rotating the target or changing the illumination or imaging mode to obtain the -1 and +1 diffraction order intensities, respectively. The intensity asymmetry of a given target (a comparison of these diffraction order intensities) provides a measurement result of the target asymmetry, i.e., the target's asymmetry. This asymmetry can be used as an indicator of overlap (undesired misalignment between two layers).

[0008] While known dark-field image-based overlap measurements are fast and computationally simple (once calibrated), they may rely on the assumption that layer misalignment (i.e., overlap error and / or intentional bias) is the sole cause of measurement intensity asymmetry. Any other contribution to the measurement intensity asymmetry (such as any process influence within one or both of the overlapping gratings) will also contribute to first-order (and other higher-order) intensity asymmetry. This contribution of intensity asymmetry due to process influences (which are independent of overlap) significantly interferes with overlap measurements, thus giving inaccurate overlap measurement results. Similar problems arise in alignment measurements due to asymmetry in the aligned target or marker being measured. Asymmetry in the bottommost or bottom grating of the target is a common form of process influence. For example, it may originate from wafer processing steps (such as chemical mechanical polishing (CMP)) performed after the initial formation of the bottom grating.

[0009] Therefore, it is desirable to improve the accuracy of overlap and / or alignment measurements. Summary of the Invention

[0010] In a first aspect, a method for improving the measurement of a parameter of interest is provided, the method comprising: obtaining measurement data including multiple measurements of the parameter of interest associated with one or more targets on a substrate, each measurement being associated with a target among the one or more targets and different measurement combinations of measurement conditions used to measure the target; obtaining asymmetry measurement data associated with asymmetry of the one or more targets; determining, based on the assumption that a common true value exists for the parameter of interest in the measurement combinations, a respective relationship in which each measurement combination relates the true value of the parameter of interest to the asymmetry measurement data; and using one or more of the relationships to improve the measurement of the parameter of interest.

[0011] On the other hand, a computer program comprising processor-readable instructions, which, when executed on a suitable processor control device, cause the processor control device to perform the method of the first aspect, and a computer program carrier comprising such a computer program. The processor control device may include a measuring device or a lithography device, or a processor thereof.

[0012] Other features and advantages of the invention, as well as the structure and operation of various embodiments of the invention, are described in detail below with reference to the accompanying drawings. It should be noted that the invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Other embodiments will be apparent to those skilled in the art based on the teachings contained herein. Attached Figure Description

[0013] Embodiments of the invention will now be described by way of example only, with reference to the accompanying schematic diagrams, in which:

[0014] Figure 1 A photolithography apparatus according to an embodiment of the present invention is described;

[0015] Figure 2 A photolithography unit or cluster is depicted according to an embodiment of the present invention;

[0016] Figures 3(a)-3(d) Includes: 3(a) a schematic diagram of a dark field scatterer for measuring a target according to an embodiment of the present invention using a first pair of illumination apertures; 3(b) details of the diffraction spectrum of a target grating for a given illumination direction; 3(c) a second pair of illumination apertures providing an additional illumination mode for diffraction-based overlap measurements using the scatterer; and 3(d) a third pair of illumination apertures combining the first pair of apertures and the second pair of apertures.

[0017] Figure 4 The outlines of multiple grating targets of known forms and measurement spots on the substrate were depicted;

[0018] Figure 5 The image obtained from the scatterer in Figure 3 is depicted. Figure 4 The image of the target;

[0019] Figure 6 This illustrates the steps of using the scatterometer of Figure 3 and applicable to forming the overlap measurement method of embodiments of the present invention;

[0020] Figure 7 It is a plot in asymmetric space that depicts the principle of determining overlap and asymmetry measures from multi-wavelength measurement data of a target with two biases.

[0021] Figure 8 It is a plot in asymmetric space depicting the problem of such measurement that may lead to errors in overlap estimation; and

[0022] Figure 9 This is a plot in asymmetric space illustrating how this problem can lead to different errors in the overlapping estimation of different targets. Detailed Implementation

[0023] Before describing the embodiments of the present invention in detail, it is beneficial to present example environments in which the various embodiments of the present invention can be implemented.

[0024] Figure 1 A lithography apparatus LA is schematically depicted. The apparatus includes: an illumination optics system (illuminator) IL configured to modulate a radiation beam B (e.g., UV radiation, DUV radiation); a patterning apparatus support or support structure (e.g., mask stage) MT configured to support a patterning apparatus (e.g., a mask) MA and connected to a first positioner PM configured to accurately position the patterning apparatus according to specific parameters; a substrate stage (e.g., a wafer stage) WT configured to hold a substrate (e.g., a wafer coated with resist) W and connected to a second positioner PW configured to accurately position the substrate according to specific parameters; and a projection optics system (e.g., a refractive projection lens system) PS configured to project a pattern imparted by the radiation beam B by the patterning apparatus MA onto a target portion C (e.g., including one or more dies) of the substrate W.

[0025] Irradiation optical systems may include various types of optical components for guiding, shaping, or controlling radiation, such as refractive, reflective, magnetic, electromagnetic, electrostatic, or other types of optical components, or any combination thereof.

[0026] A patterning apparatus support holds the patterning apparatus in a manner dependent on the orientation of the patterning apparatus, the design of the lithography equipment, and other conditions, such as whether the patterning apparatus is maintained in a vacuum environment. The patterning apparatus support may employ mechanical, vacuum, electrostatic, or other clamping techniques to hold the patterning apparatus. The patterning apparatus support may be a frame or a table, and may be fixed or movable as needed. The patterning apparatus support ensures that the patterning apparatus is in a desired position, for example, relative to a projection system. Any use of the terms "mask" or "mask" herein may be considered synonymous with the more general term "patterning apparatus".

[0027] As used herein, the term "patterning apparatus" should be broadly understood to refer to any apparatus capable of imparting a pattern onto the cross-section of a radiation beam, thereby forming a pattern on a target portion of a substrate. It should be noted that the pattern imparted by the radiation beam may not perfectly correspond to the desired pattern on the target portion of the substrate (e.g., if the pattern includes phase-shifting features or so-called auxiliary features). Typically, the pattern imparted by the radiation beam will correspond to a specific functional layer in a device formed on the target portion, such as an integrated circuit.

[0028] Pattern forming apparatuses can be transmissive or reflective. Examples of pattern forming apparatuses include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in photolithography and include mask types such as binary, alternating phase-shift, and attenuation phase-shift masks, as well as various hybrid mask types. Examples of programmable mirror arrays use a matrix arrangement of small mirrors, each of which can be individually tilted to reflect the incident radiation beam in different directions. The tilted mirrors impart a pattern to the radiation beam reflected by the mirror matrix.

[0029] As described herein, the device is transmissive (e.g., employing a transmissive mask). Alternatively, the device may be reflective (e.g., employing a programmable mirror array type as described above, or employing a reflective mask).

[0030] Photolithography apparatuses can also fall into the category where at least a portion of the substrate can be overlapped by a liquid (e.g., water) with a relatively high refractive index to fill the space between the projection system and the substrate. Immersion liquids can also be applied to other spaces within the photolithography apparatus, such as the space between the mask and the projection system. Immersion techniques are well-known in the art for increasing the numerical aperture of projection systems. The term "immersion" as used herein does not mean that a structure such as the substrate must be submerged in a liquid, but only that the liquid is located between the projection system and the substrate during exposure.

[0031] refer to Figure 1 The irradiator IL receives the radiation beam from the radiation source SO. For example, when the source is an excimer laser, the source and the lithography apparatus can be separate entities. In such cases, the source is not considered part of the lithography apparatus, and the radiation beam is transmitted from the source SO to the irradiator IL by means of a beam delivery system BD, which includes, for example, suitable directional mirrors and / or beam expanders. In other cases, for example, when the source is a mercury lamp, the source can be an integral part of the lithography apparatus. The source SO and the irradiator IL, together with the beam delivery system BD (if necessary), can be referred to as the radiation system.

[0032] The irradiator IL may include an adjuster AD for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer radial range and / or inner radial range (often referred to as σ-outer and σ-inner, respectively) of the intensity distribution in the pupil plane of the irradiator can be adjusted. Additionally, the irradiator IL may include various other components, such as an integrator IN and a concentrator CO. The irradiator IL can be used to adjust the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0033] A radiation beam B is incident on a pattern forming apparatus (e.g., a mask) MA held on a pattern forming apparatus support (e.g., a mask stage MT) and patterned by the pattern forming apparatus. After passing through the pattern forming apparatus (e.g., the mask) MA, the radiation beam B passes through a projection optics system PS, which focuses the beam onto a target portion C of the substrate W, thereby projecting an image of the pattern onto the target portion C. The substrate stage WT can be accurately moved, for example, to position different target portions C within the path of the radiation beam B, by means of a second positioner PW and a position sensor IF (e.g., an interferometric device, a linear encoder, a 2D encoder, or a capacitive sensor). Similarly, a first positioner PM and another position sensor (…) can be used, for example, after mechanical retrieval from a mask library or during scanning. Figure 1 (Not explicitly depicted) to accurately position the pattern forming apparatus (e.g., mask) MA relative to the path of the radiation beam B.

[0034] The patterning apparatus (e.g., a mask) MA and the substrate W can be aligned using mask alignment marks M1, M2 and substrate alignment marks P1, P2. Although the substrate alignment marks (as illustrated) occupy dedicated target portions, these marks can be located in the space between the target portions (these are referred to as scribing alignment marks). Similarly, in cases where more than one die is disposed on the patterning apparatus (e.g., a mask) MA, the mask alignment marks can be located between the dies. In addition to device features, small alignment marks can also be included within the die; in this case, it is desirable that the marks be as small as possible and that no different imaging or process adjustments are required between adjacent features. An alignment system for detecting alignment marks is further described below.

[0035] The lithography apparatus LA in this example is a so-called dual-stage type, which has two substrate stages WTa and WTb, and two stations (exposure station and measurement station) where the substrate stages can be exchanged. While one substrate on one stage is being exposed at the exposure station, another substrate can be loaded onto the other substrate stage at the measurement station, allowing various preparation steps to be performed. Preparation steps may include mapping the surface control of the substrate using a level sensor LS and measuring the position of alignment marks on the substrate using an alignment sensor AS. This significantly increases the apparatus's throughput.

[0036] The depicted apparatus can be used in various modes, including, for example, stepping mode or scanning mode. The construction and operation of lithography apparatus are well known to those skilled in the art and do not require further description for understanding the present invention.

[0037] like Figure 2 As shown, the lithography equipment LA forms part of a lithography system (referred to as a lithography unit LC, lithography cell, or cluster). The lithography unit LC may also include equipment for performing pre-exposure and post-exposure processes on the substrate. Typically, this equipment includes a spin coater SC for depositing a resist layer, a developer DE for developing the exposed resist, a chiller CH, and a baking plate BK. A substrate transport device (or robot) RO picks up the substrate from input / output ports I / O1 and I / O2, moves the substrate W between different processing devices, and then transports the substrate W to the loading stage LB of the lithography equipment. These devices (often collectively referred to as tracks) are controlled by a track control unit TCU, which itself can be controlled by a management control system SCS, which in turn controls the lithography equipment via the lithography control unit LACU. Therefore, different devices can be operated to maximize throughput and processing efficiency.

[0038] Figure 3(a) illustrates a measurement apparatus suitable for an embodiment of the present invention. Figure 3(b) illustrates the target T and the diffracted rays of the measurement radiation used to illuminate the target in more detail. The measurement apparatus shown is of the type called a dark-field measurement apparatus. The measurement apparatus can be a stand-alone device or can be incorporated into a lithography apparatus LA (e.g., at a measurement station) or a lithography unit LC. The optical axis having multiple branches throughout the apparatus is indicated by the dashed line O. In this apparatus, light emitted by a light source 11 (e.g., a xenon lamp) is guided to the substrate W via a beam splitter 15 through an optical system including lenses 12, 14 and objective lens 16. These lenses are arranged in a double sequence of 4F. Different lens arrangements can be used, as long as it provides an image of the substrate to the detector and allows simultaneous access to the intermediate pupil plane for spatial frequency filtering. Therefore, the range of angles at which radiation is incident on the substrate can be selected by defining the spatial intensity distribution in the plane that presents the spatial spectrum of the substrate plane (referred to herein as the (conjugate) pupil plane). Specifically, this can be achieved by inserting a suitable aperture plate 13 between lenses 12 and 14 in the plane of the back-projected image, which serves as the pupil plane of the objective lens. In the example shown, the aperture plate 13 has different forms (labeled 13N and 13S), thereby allowing the selection of different illumination modes. The illumination system in this example forms an off-axis illumination mode. In the first illumination mode, aperture plate 13N provides off-axis illumination in a direction designated "north" (for ease of description only). In the second illumination mode, aperture plate 13S is used to provide similar illumination, but from the opposite direction designated "south". Other illumination modes are possible by using different apertures, such as illumination modes that can simultaneously illuminate and detect from two opposite directions and combine the resulting images separated by the optical wedge. The remainder of the pupil plane is ideally dark, as any unwanted light outside the desired illumination mode will interfere with the desired measurement signal.

[0039] As shown in Figure 3(b), the target T is placed with the substrate W perpendicular to the optical axis O of the objective lens 16. The substrate W may be supported by a support (not shown). The measuring radiation ray I striking the target T at an angle off-axis O produces a zero-order ray (solid line 0) and two first-order rays (dotted line +1 and double-dotted line -1). It should be remembered that in the case of an overfilled small target, these rays are only one of many parallel rays covering the substrate area including the measuring target T and other features. Due to the limited width of the aperture in plate 13 (to allow for a beneficial amount of light), the incident ray I will actually occupy a certain angular range, and the diffracted rays 0 and +1 / -1 will be slightly diffused. Depending on the point spread function of the small target, each +1 and -1 order will be further diffused over a certain angular range, rather than a single ideal ray as shown. Note that the grating pitch and illumination angle of the target can be designed or adjusted so that the first-order rays entering the objective lens are nearly aligned with the central optical axis. The rays shown in Figures 3(a) and 3(b) are depicted slightly off-axis, merely for easier differentiation in the accompanying figures.

[0040] At least the 0th and +1st diffractions from the target T on the substrate W are collected by objective lens 16 and guided back through beam splitter 15. Returning to Figure 3(a), the first and second illumination modes are shown through completely opposite apertures labeled North (N) and South (S). When the incident ray I of the measured radiation comes from the north side of the optical axis, i.e., when the first illumination mode is applied using aperture plate 13N, the +1 diffracted ray (labeled +1(N)) enters objective lens 16. Conversely, when the second illumination mode is applied using aperture plate 13S, the -1 diffracted ray (labeled -1(S)) is the ray entering lens 16.

[0041] The second beam splitter 17 divides the diffracted beam into two measurement branches. In the first measurement branch, the optical system 18 uses the zeroth-order and first-order diffracted beams to form the diffraction spectrum (pupil plane image) of the target on the first sensor 19 (e.g., a CCD or CMOS sensor). Each diffraction order hits a different point on the sensor, so image processing can compare and contrast multiple orders. The pupil plane image captured by the sensor 19 can be used for focusing measurement devices and / or to normalize the intensity measurement of the first-order beam. The pupil plane image can also be used for many measurement purposes, such as reconstruction.

[0042] In the second measurement branch, optical systems 20 and 22 form an image of the target T on sensor 23 (e.g., a CCD or CMOS sensor). In this second measurement branch, an aperture stop 21 is positioned in a plane conjugate to the pupil plane. Aperture stop 21 blocks the zero-order diffraction beam, allowing only a first-order beam (either -1 or +1) to form the target image on sensor 23. The images captured by sensors 19 and 23 are output to a processor PU that processes the images; the functionality of the processor PU will depend on the specific type of measurement being performed. Note that the term "image" as used herein is broad. If only one of the -1 or +1 orders is present, such a grating image will not be formed.

[0043] The specific form of the aperture plate 13 and field stop 21 shown in Figure 3 is merely an example. In another embodiment of the invention, the target is illuminated along its axis, and an aperture stop with an off-axis aperture is used to allow essentially only a first-order diffracted beam to pass through to the sensor. In yet another embodiment, instead of a first-order beam or in addition to a first-order beam, second-order, third-order, and higher-order beams (not shown in Figure 3) may be used in the measurement.

[0044] To enable the measurement of radiation to be applicable to these different types of measurements, the orifice plate 13 may include a plurality of aperture patterns formed around a disk that rotates to introduce the desired pattern into place. Note that the orifice plates 13N or 13S can only be used to measure gratings oriented in one direction (X or Y, depending on the setup). For measurements of orthogonal gratings, target rotation of 90° and 270° can be achieved. Figures 3(c) and (d) illustrate different orifice plates. The use of these, as well as many other variations and applications of the device, are described in the previously disclosed applications mentioned above.

[0045] Figure 4 A (composite) target formed on a substrate according to known practices is depicted. In this example, the target arrangement includes four gratings 32 to 35 positioned close together such that all four gratings are within a measurement spot 31 formed by a measurement radiation beam from a measurement device. Therefore, all four gratings are simultaneously illuminated and imaged onto sensors 19 and 23. In an example specifically for measuring overlap, gratings 32 to 35 are themselves composite gratings formed by overlapping sub-gratings patterned in different layers of a semiconductor device formed on the substrate W. Gratings 32 to 35 can have different offsets to facilitate measuring the overlap between layers forming different portions of the composite grating. Reference will be made below. Figure 7Explain the meaning of overlapping bias. The orientations of gratings 32 to 35 can also be different (as shown), thus diffracting the incident radiation in the X and Y directions. In one example, gratings 32 and 34 are X-direction gratings with +d and -d biases, respectively. Gratings 33 and 35 are Y-direction gratings with +d and -d offsets, respectively. Individual images of these gratings can be identified in the image captured by sensor 23. This is only one example of a target. A target may include more or fewer than four gratings, or may consist of only a single grating.

[0046] Figure 5 This illustrates the use of the device in Figure 3. Figure 4 An example of an image can be formed on and detected by sensor 23 using the aperture plate 13NW or 13SE from Figure 3(d). While the pupil plane image sensor 19 cannot distinguish different individual gratings 32 to 35, the image sensor 23 can. The black rectangle represents the image field on the sensor, within which the illumination spot 31 on the substrate is imaged as a corresponding circular region 41. Within the circular region 41, rectangular regions 42-45 represent images of small target gratings 32 to 35. If the target is located in the product area, the product features are also visible within the outer perimeter of the image field. The image processor and controller PU use pattern recognition to process these images to identify the respective images 42 to 45 of the gratings 32 to 35. In this way, the images do not need to be precisely aligned to a specific location within the sensor frame, which greatly improves the overall throughput of the measuring device.

[0047] Once the separated images of the grating have been identified, the intensity of those individual images can be measured, for example, by averaging or summing the intensity values ​​of selected pixels within the identified regions. The intensity and / or other properties of the images can be compared. These results can be combined to measure different parameters of the photolithography process. Overlap performance is an important example of such a parameter.

[0048] Figure 6 This illustrates how, for example, using the method described in application WO 2011 / 012624, the overlap (i.e., undesirable and unintentional misalignment) between two layers containing component gratings 32 to 35 is measured. This measurement is performed through target asymmetry, as revealed by measurements of intensity asymmetry obtained by comparing the intensity in +1-order and -1-order dark-field images (and possibly comparing the intensity of other corresponding higher orders, such as +2-order and -2-order). In step S1, the photolithography apparatus (such as...) Figure 2The photolithography unit processes the substrate (e.g., a semiconductor wafer) once or multiple times to create a target including gratings 32-35. In S2, using the measurement device of FIG3, an image of gratings 32 to 35 is obtained using only one of the first-order diffraction beams (e.g., -1). In step S3, a second image of the grating can be obtained using another first-order diffraction beam (+1), either by changing the illumination mode, by changing the imaging mode, or by rotating the substrate W 180° in the field of view of the measurement device. Therefore, the +1 diffraction radiation is captured in the second image. Note that, as described above, these first and second images can be obtained simultaneously in simultaneous illumination in two opposite directions.

[0049] Note that since each image includes only half of the first-order diffraction radiation, the "images" referred to herein are not traditional dark-field microscopy images. Individual target lines will not be distinguishable. Each target will be simply represented by a region of some intensity level. In step S4, a region of interest (ROI) is identified within the image of each component target, and the intensity level is measured from each component target.

[0050] After identifying the ROI of each individual target and measuring its intensity, the target asymmetry, and thus overlap, can be determined. This is done in step S5 (e.g., via processor PU) by comparing the intensity values ​​obtained for each target at +1 and -1 orders of 32-35 to identify their intensity asymmetry (e.g., any differences in their intensity). The term "difference" does not simply refer to subtraction. Differences can be calculated in the form of ratios. In step S6, using the measured intensity asymmetry of multiple targets and knowledge of any known applied overlap biases of these targets, one or more performance parameters of the lithography process near target T are calculated. In the applications described herein, measurements using two or more different measurement conditions or "alternative schemes" will be included. One performance parameter of great interest is overlap. As described later, the new method also allows for the calculation of other performance parameters of the lithography process. These parameters can be fed back to improve the lithography process and / or to improve... Figure 6 The measurement and calculation process itself.

[0051] In the existing applications mentioned above, various techniques for improving the quality of overlap measurements using the basic methods described above have been disclosed. These techniques will not be explained in detail herein. These techniques can be used in conjunction with the techniques newly disclosed in this application, which will now be described.

[0052] It is known that, based on the assumption that overlap depends on intensity asymmetry, process effects such as bottom grating asymmetry or other undesirable asymmetries in the target can influence overlap measurements. Structural asymmetry in the bottom grating of the target is a common form of process effect. For example, it might originate from substrate processing steps (such as chemical mechanical polishing (CMP)) performed after the initial formation of the first structure. However, it will be understood that this is only a single example of process effects. As a result of these process effects, the overall target asymmetry of the target will include the overlap contribution due to process effects, in addition to target overlap (and any intentional bias). When passed through Figure 6 When the method uses only two bias gratings and a single illumination condition (e.g., wavelength and / or polarization or a combination thereof) to measure overlap, it cannot distinguish process effects from the overlap, thus making the overlap measurement unreliable.

[0053] For an "ideal" target with zero offset and no process-effect intensity asymmetry A, the plotting between the overlap OV and the intensity asymmetry A has a non-linear periodic relationship (e.g., a sinusoidal relationship) that varies with overlap. The period P of the sinusoidal variation corresponds to the period or pitch P of the grating (which is, of course, converted to an appropriate scale). In this example, the sinusoidal form is pure, but in reality it may include harmonics.

[0054] As described above, a bias grating (with a known applied overlap bias) can be used to measure overlap without relying on a single measurement. This bias has a known value defined in the patterning apparatus (e.g., a mask) that generates the bias, and this known value is used for on-wafer overlap calibration corresponding to the measured intensity asymmetry. In steps S1-S5, intensity asymmetry measurement results A+d and Ad are obtained for gratings with applied biases +d and –d, respectively. Knowing the bias, OV can be calculated.

[0055] A method for addressing the aforementioned process influence problem is described in WO2015018625A1, which is incorporated herein by reference. Graphically (and of course, the method can be implemented algorithmically), the method essentially describes calculating overlap by measuring a composite target under more than one measurement condition and plotting these measurements on a graph in an asymmetric space. In this case, the asymmetric space comprises a graph of intensity asymmetric measurements (A+d measurements) for a positively biased (+d) target against intensity asymmetric measurements (Ad measurements) for a negatively biased (-d) target for each measurement condition. Overlap is estimated based on the slope of the regression by fitting a regression to each point (but not necessarily the origin) on the asymmetric space graph (e.g., in a linear fit). The method described in WO2015018625A1 relies on the assumption that the relationship between the A+d and Ad measurements is substantially linear. However, the concepts described herein are not limited to methods using linear models, but rather nonlinear extended models can be used and compared.

[0056] For a perfect target, the plot would pass through the origin, as previous methods assumed; for example, in this method, a single wavelength measurement is plotted as a single point, and a regression line is plotted through that point and the origin, with overlap determined by the slope of the line. WO2015018625A1 teaches that a better estimate of overlap can be achieved by excluding the origin from the regression line, where the offset of the regression line relative to the origin (referred to herein as the distance to the origin or DTO value) indicates process influence. This method can be based on the assumption that each point associated with two or more measurement conditions will produce a value of the measurement result, which, when plotted in asymmetric space, lies substantially on the regression line representing overlap (i.e., all measurements lie substantially on the same line with a slope representing overlap). The DTO can be used as an additional metric or a measure of process asymmetry, and for each set of data, it can include the shortest distance from the plotting origin to the line (i.e., the line best fitted by passing through the points on the graph at 90° from the plotting origin). The DTO is a useful indicator of the characteristic of the target or process asymmetry and is largely independent of actual overlap.

[0057] Figure 7The concepts as currently understood are illustrated. A solid line OV=0 drawn through the origin O and black measurement points represent a perfect target with no process effects or any overlap or bias. A dashed line OV>0 is the regression line for a perfect target except for overlap or bias in the first direction, and a dotted-dash line OV<0 is the regression line for a perfect target except for overlap or bias in the second direction (measurement points are not shown for this). A solid line OV'=0 regressed through gray measurement points represents an imperfect target (e.g., with process asymmetry) but without any overlap or bias; that is, it is the imperfect version of line OV=0. Because lines OV=0 and OV'=0 have the same slope, the method of WO2015018625A1 is understood to be able to determine the overlap of imperfect targets independently of other asymmetries and process effects. The DTO metric is an indicative measure of these other asymmetries and process effects.

[0058] Methods such as those described in WO2015018625A1 describe the use of measurements under several or more measurement conditions or wavelengths / polarizations to determine robust regression lines and thus overlap. For linear regression lines, only two measurements should be required; in practice, points in the asymmetric space associated with certain measurement conditions may deviate significantly from the overlap line, and / or the A+d / Ad regression line may be fundamentally nonlinear for a particular set of measurement conditions or targets. However, when monitoring overlap during device fabrication, measuring 10, 20, or more measurements across the entire range (each associated with a different measurement condition) to ensure a good fit and thus reliable overlap values ​​would be quite slow. Therefore, a small subset (e.g., two or three measurement conditions) is typically selected for production monitoring, where this subset of measurement conditions can be referred to as a measurement fit. However, the measurement quality varies for different subsets of these measurement conditions for a target, and the optimal subset differs between targets. Therefore, it is crucial to carefully select the subset of measurement conditions or measurement fits used for production monitoring on a per-stack or per-target basis. This option can use the regression line of a more comprehensive set of measurements performed on a specific target / stack during the calibration phase as a reference.

[0059] This method performs a measurement scheme for optimizing the target for each target without knowing other targets / selections. There is currently no concept of combining information from different targets / selections. With these methods of the present invention, the inventors have observed that a single target can exhibit very good KPIs (indicating the optimized selection for that target), but still measures incorrect overlap (or alignment) values. Thus, it has been observed that different overlap measurements within a common wafer region (e.g., due to different targets, multiple regions of the same target, and / or related to different irradiation characteristics), and therefore the same overlap should be measured, actually show significant variation between measurements. Furthermore, when measuring with the optimized measurement scheme, measurements from different targets (or regions of targets) also show significant target-to-target variations. There are target asymmetry patterns that make current multi-wavelength optimization methods less accurate. Similar behavior has been observed across different stacking types during alignment, where target-to-target variations exceeding 1 nm have been found in measured alignments for targets that are physically close to each other (e.g., <10 μm spacing).

[0060] Figure 8 The possible root cause of the failure (in terms of overlap) is shown. A combination of phase and amplitude asymmetry is proposed as an explanation. The thin dashed line LP represents a perfect target. The above approach is based on the assumption that non-overlapping target asymmetry only causes a shift in the regression line, but the slope remains unchanged; this is represented by the solid expected regression line LEXP. It has been observed that other asymmetry modes (e.g., target “center of gravity” shift) can cause phase asymmetry and rotation of the regression line (e.g., represented by the observed line LOB). This rotation will result in different and incorrect overlap values ​​(all three lines are associated with a target with zero overlap).

[0061] Further observation revealed that this rotation and displacement may differ for each target design. This is in Figure 9 This is explained in the text. Figure 9 The diagram shows four regression lines T1, T2, T3, and T4 for four objectives, which have the same (zero) true overlap, indicated by the dashed line OV. It can be seen that the regression lines for each objective have different slopes and DTOs.

[0062] To address these issues, a method is proposed for optimizing measurement fitting schemes and / or determining corrections for multiple measurements of one or more targets, minimizing differences in the measured parameters of interest (e.g., overlap or location) between the measurements. Each measurement may involve different combinations of measurement results for the target and the measurement conditions used to measure that target. These combinations may differ in terms of the target, measurement conditions, or both to obtain each measurement result. For example, each target may have one measurement. Alternatively, each target may have more than one measurement; for example, measurements are obtained for each target using different measurement fitting schemes or measurement conditions to obtain different samples of the asymmetric content of each target. Measurement results may come from only one target (e.g., using more than one measurement fitting scheme) or from more than one target (e.g., each target using one or more measurement fitting schemes). Different measurements can also be obtained from different regions of the same target (e.g., each fitting scheme); for example, by further dividing the target into multiple parts or regions.

[0063] In this approach, it is assumed that all measurements should be identical, and any variation is due to other asymmetries. Therefore, it is assumed that the parameter of interest is universal for all measurements. Consequently, multiple measurements should be obtained from targets (e.g., in the case of more than one target), all located in the same proximity or common wafer region. In this way, it can be expected that all measurements and all targets have the same overlap. In a multi-target embodiment, each of the multiple targets can differ from one another in aspects other than the expected overlap, such that their non-overlapping asymmetries can differ.

[0064] This method also applies to alignment, where any overlap mentioned can be replaced by position. Specific alignment embodiments will be described later.

[0065] The concepts disclosed herein are based on the observation that, for a given measurement scheme or setup, the overlap of the measured targets exhibits a significant linear dependence on a target-related measure of process asymmetry or non-overlapping asymmetry. For example, when the parameter of interest is overlap, this measure of process asymmetry could be the already described DTO. However, any other measure of process asymmetry can be used, which quantifies the degree of non-overlapping asymmetry in the target (or, in an alignment setup, any target asymmetry). Thus, the method involves finding a relationship that correlates the true overlap with the measure of process asymmetry (e.g., a scaling constant for the linear example described below, but other functions describing more complex relationships are also possible). Since the true overlap is unknown, the method is based on performing optimizations that minimize the differences between measured overlap values ​​between schemes, between targets (when measuring multiple targets), and optionally between polarizations, and measurements expected to have the same true overlap (e.g., the same adjacent area on the wafer). For example, the same adjacent area could be spaced no more than 1.5 mm or 1 mm (e.g., a spacing between 10 μm and 1.5 mm).

[0066] Therefore, in the overlapping example, it is recommended that all objectives satisfy the following relationship:

[0067] OV real =OV meas n，P +C n,P *DTO n，P (1)

[0068] Among them, OV real It is a true overlap, OV meas N,P The measurement overlap is defined by C as a constant and DTO as the distance to the origin as described (or another measure of non-overlapping asymmetry). N and P refer to the target and polarization, respectively, such that all parameters except for the true overlap depend on the target and measurement conditions (e.g., different measurement conditions can vary in one or more aspects such as wavelength, bandwidth, polarization, and angle of incidence). Assume OV real The optimized measurement conditions are constant (e.g., all within a specific region or within the distance between each other). However, there are multiple such regions on the wafer to establish the correlation defined by equation (1). As already mentioned, linear relationships such as those described herein are merely one example, and the proposed method can use other predictable relationships between overlapping and (non-overlapping) asymmetry measures. Although only one non-overlapping asymmetry measure is mentioned in equation (1), it should be understood that equation (1) can be extended to include more than one non-overlapping asymmetry measure.

[0069] The main assumption is: true overlap of OV realThe change should be zero (provided the targets are sufficiently close to each other); however, there are multiple overlapping measurements from different regions of the wafer. Therefore, an optimization method is proposed that finds the value of OV. real The changes in CN and P are relatively small (minimized). This can be achieved by minimizing the differences in all individual OV values ​​at a certain location (e.g., if all differences are used, 3 targets would provide 15 such differences in the case of two polarizations).

[0070] It can be noted that the calibration regression line of the prior art described above (e.g., using 20 wavelengths) can be performed for each target and each polarization (e.g., two polarization states). Similar processing can be used in the method disclosed herein, while different datasets and optimization problems are proposed not only for each target but also for each measurement condition.

[0071] Now we will describe more details about the optimization and how to solve it. Equation 1 can be restated in its general form.

[0072] y n = x n + c n · z n (2)

[0073] Where y is the true value of the parameter of interest, x is the measured value of the parameter of interest, c is the constant to be found, and z is the asymmetric offset term (which may be a DTO term in the overlapping example); n∈{1,2,3,…,N} and N is the total number of measurement conditions (e.g., target / polarization combination). For example, for an example with two polarizations and three different markers or marker types, N could be 6.

[0074] By juxtaposing all column vectors along the row direction, all N equations can be combined into one equation, thus yielding:

[0075]

[0076] The optimization problem can be formulated as (using the Frobenius norm):

[0077]

[0078] in, The matrix is ​​used to calculate all overlapping differences (and optionally, may also include / provide weights for the estimated residuals). For the example of N=6, the matrix... There will be 6 rows and 15 columns, with each possible difference corresponding to one column (fewer than all possible differences can be calculated, in which case there will be fewer columns).

[0079] Extending this optimization problem will result in:

[0080]

[0081] Cost function relative to the unknown c The gradient is equal to:

[0082]

[0083] It uses the following equation:

[0084]

[0085]

[0086] For example, the determined gradient can be used to solve the optimization problem using the steepest descent algorithm.

[0087] In one embodiment, the proposed calibration can be used to determine a proportionality constant C (or other relational function) dependent on the target. This can then be stored and subsequently used to correct measurements of the parameter of interest in the corresponding target type or measurement setting (e.g., in a production setting).

[0088] In another embodiment, the foregoing description can be used to optimize measurement schemes for each target or target type. This approach can perform optimization and determine a scaling constant / other relationships (as described), but then use the results of the optimization to determine a preferred measurement scheme (e.g., the scheme that minimizes the variation of the measured parameter of interest between targets (optionally between polarizations)). In this approach, the determined relationships may not actually be used to correct measurements in a production setting (although this can still be done). The optimized measurement scheme for a target can be further refined to ensure high accuracy of measurements under all conditions.

[0089] Methods for optimizing measurement selection schemes may include determining a preferred subset (e.g., fewer than six, such as three or two) of wavelengths from a larger subset (e.g., more than 10, 20, or 30) for each target or target type. It should be understood that while measurement points using these multiple wavelengths for a single target will show a linear trend for each target when plotted in asymmetric space, they will not lie exactly on the same line. This means that each regression line through different subsets of measurements (e.g., wavelength pairs) will obtain different slopes (and thus effectively estimate different measurement overlaps). Figure 9 This is clear in the text. Figure 9The regression line for target T1 is shown through two measurement points associated with a specific wavelength pair. It is clear that the regression can produce significantly different slopes and different linear dependencies on its asymmetry indices (e.g., DTO) CN and P, depending on the wavelength pair.

[0090] Because asymmetry indicators (e.g., DTO) are typically parameters dependent on measurement conditions (e.g., wavelength, bandwidth, polarization, angle of incidence), each measurement condition (e.g., a subset or pair of wavelengths) will have different respective CN and P. Similarly, the asymmetry measure in alignment is also dependent on the measurement setup and should be learned with unique weights for each measurement setup. Thus, the above-described optimization of Equation 1 can be performed for all combinations of candidate measurement settings (e.g., wavelength pairs / subsets and target or target / polarization combinations) in terms of optimizing the constant C. Candidate measurement settings may include some or all possible combinations of, for example, two or three wavelengths used. The method may then include determining which wavelength pair / subset will result in the minimum overlap (or other parameter of interest) variation on the target / polarization. Methods for evaluating this may include determining which wavelength subset makes the measurement data match the expected model (e.g., as described in Equation (1)) and / or which wavelength subset best minimizes the difference in parameters of interest between the measurements. This can include determining which subset of wavelengths makes the relationship between overlap variation and asymmetry metric most linear (for the linear example described herein), most closely matches the model (if not linear), or best minimizes the difference in the parameters of interest between measurements.

[0091] While within the scope of this disclosure, it is clear that the number of combinations may be inconvenient or infeasible for solving optimization problems using brute force (at least given current processing speeds). By way of a concrete example, if N = 6 (e.g., 3 targets and 2 polarizations) and the number of measured wavelengths is 33, then the total number of combinations for calculating the optimal wavelength pair for each target / polarization pair (i.e., a subset of measurements of 2) would be (33 * 32 / 2)^6. This is on the order of 10^16 combinations.

[0092] Therefore, one approach to solving this problem may include limiting the number of combinations by fixing or constraining a subset of N target / polarization pairs to have the same optimal wavelength pair. This can be done, for example, based on an evaluation of their respective wobble curves (e.g., based on similarity). The wobble curve may comprise a plot of measurement parameters relative to wavelength. Suitable measurement parameters for such a wobble curve plot may include intensity, signal strength, stack sensitivity, or overlap sensitivity. Such wobble curves are known in the art and, for example, can be used to optimize target selection for individual targets.

[0093] By plotting one or more oscillation curves for each target / polarization pair (e.g., for each of the different measured parameters, two or more curves can be plotted and compared for robustness), they can be compared to find combinations that show signs of similar responses. From this, it can be inferred or assumed that the optimized pairings will be identical, or at least sufficiently similar, such that a pairing optimized for one such target / polarization pair will show good performance for targets / polarization pairs considered similar. Similarity comparisons can be evaluated based on any suitable similarity metric or even by observation. The number of combinations grouped together can be predetermined to minimize the number of combinations, and can be based purely on their similarity or a hybrid approach. The number of target / polarization pairs in a group can be finite or unlimited. By way of concrete example, if six target / polarization pairs are grouped into three similar groups, the number of combinations becomes (33 * 32 / 2)^3, which is on the order of 10^8. However, this is still a large number. The number of combinations can be further reduced by considering fewer wavelengths, but this is not ideal.

[0094] Other methods that make optimization more suitable may include the use of combinatorial optimization techniques, such as simulated annealing and / or local search. This approach is known in the art and will not be further disclosed.

[0095] A more practical approach can be based on an existing selection scheme setup and optimization process called Holistic Measurement Qualification (HMQ). This method aims to find the optimal (single-wavelength / multi-wavelength) selection scheme based on subsequent sparse-dense sampling of targets / locations under multiple illumination settings (wavelengths and other selection scheme settings, such as polarization, aperture). The details of this process can vary, but in one example, HMQ may include performing a pre-selection step using the available full-wavelength spectrum (or a large number of wavelengths) on a relatively small number of targets. For example, the number of targets measured in this step could be less than 20, more specifically between 3 and 15. The number of wavelengths could be, for example, greater than 30. A subset of wavelengths with better performance is selected for the optimization step (e.g., including 10 to 20 wavelengths, or about 15 wavelengths). The optimization step may include measuring the dense number of targets at the selected (e.g., about 15) wavelengths. For example, the number of targets could be greater than 50, greater than 70, greater than 90, or about 100. Optimization includes an evaluation, where the accuracy and robustness of the measurements are assessed under different illumination conditions. This evaluation step can use a reference overlap value for the actual overlap value. Since the actual overlap value is often not known, previous methods for determining the reference include those described in patent application WO 2015 / 018625. The result of this method is an optimized measurement matching scheme.

[0096] This paper proposes using the calibration disclosed herein to determine a multi-target reference for use as a reference overlap value. The main objective of the matching scheme optimization is to find a target-matching scheme combination that is least affected by target asymmetry. By using a multi-target reference, the influence of target deformation is mitigated, thus the optimally matched dual-wavelength matching scheme will also have the least impact from target deformation.

[0097] This method may include determining a multi-target reference on the wafer using the techniques already described; that is, optimizing a relation term to minimize the differences between measurements under multiple measurement conditions for optionally multiple targets, and using the optimization results to calculate the true overlap. This will determine the true overlap value for each target. An HMQ process or a similar process may then be performed, which evaluates each subset of measurement wavelengths by performing measurements with each subset of wavelengths (in a two-stage approach as described in previous paragraphs or otherwise). The subset of wavelengths that provides the best match for the actual overlap reference is then selected as the optimized measurement fit for that target. This can be repeated for all targets. During production, the performance of the selected measurement fit can be monitored using standard metrics such as DTO, stack sensitivity, etc. Therefore, an added advantage is that the entire existing process remains unchanged except for determining the overlap reference.

[0098] While the above description addresses the concepts of overlap, it should be understood that these concepts also apply to alignment. In such an embodiment, the same fundamental assumption is that for all measurements of one or more targets (alignment marks) within the same adjacent region, there exists only one true alignment position AL. real There also exists an asymmetry measure called Asym for alignment. n,p This can replace the DTO determination. For example, such asymmetry measures can include asymmetry measures between colors, intensity difference measures (difference between two complementary diffraction orders), (bottom grating) asymmetry measurements performed using another device (such as a scatterometer that can be used for overlap measurements), marker deformation estimates based on external algorithms (such as Kramers-Kronig type inference schemes), or derived estimates (such as the derivative or ratio of diffraction order intensity differences with respect to wavelength). In this example, equation (1) becomes:

[0099] AL real =AL meas n，P +C n，P *Asym n，P (8)

[0100] Among them, AL meas n,p It measures the alignment value.

[0101] The above describes only a single asymmetry observable, but multiple asymmetry observables (intensity differences or ratios between diffraction orders and / or their derivatives with respect to wavelength) can also be deployed per marker, where the type weight of each asymmetry observable is uniquely learned. The determination of C can be performed on the setup or calibration wafer. n,p The optimization of the weights can be achieved by various regularization techniques motivated by prior information, such as physical equations motivated by Tikhonov regularization. Similarly, the choice of which wavelength combination to use and the number of wavelengths employed can also be motivated by prior knowledge and / or measurement uncertainty / noise / bias. The use of multiple asymmetry measures and / or regularizations is also applicable to the described overlapping embodiments (and other parameters of interest).

[0102] The determined C n,p (More than one mark type) can be used to correct the measured alignment during production. The advantage of using multiple targets for optimization is that there is no wafer deformation term and significantly different C values ​​can be obtained. n,p This value provides good separation sensitivity. Alternatively, optimization can be used to calculate a true alignment reference, which can be used to evaluate alignment measurement options (wavelength subsets) similar to the described overlapping embodiments.

[0103] While the aforementioned targets are measurement targets specifically designed and formed for measurement purposes, in other embodiments, the properties of targets formed on a substrate as functional components of a device can be measured. Many devices have regular grating-like structures. The terms "target grating" and "target" as used herein do not require a structure specifically provided for the measurement performed. Furthermore, the pitch P of the measurement target is close to the resolution limit of the optical system of the scattering instrument, but can be much larger than the size of a typical product feature fabricated in the target portion C by a photolithography process. In practice, the lines and / or spaces of overlapping gratings within the target can be fabricated to include smaller structures with dimensions similar to product features.

[0104] Associated with a physical grating structure of a target realized on a substrate and a patterning apparatus, embodiments may include a computer program comprising one or more machine-readable instruction sequences describing methods for measuring the target on the substrate and / or analyzing the measurement results to obtain information about the photolithography process. The computer program may, for example, be in a unit PU in the apparatus of FIG3 and / or Figure 2The computer program is executed within the LACU (Label Control Unit). A data storage medium (e.g., semiconductor memory, magnetic disk, or optical disk) in which such a computer program is stored may also be provided. Where existing measuring devices (e.g., those of the type shown in FIG3) are already in production and / or use, the present invention can be implemented by providing an updated computer program product that enables the processor to execute the methods disclosed herein.

[0105] Although the embodiments disclosed above are described in terms of diffraction-based overlap measurements (e.g., measurements performed using the second measurement branch of the device shown in FIG3(a)), in principle, the same model can be used for pupil-based overlap measurements (e.g., measurements performed using the first measurement branch of the device shown in FIG3(a)). Therefore, it should be understood that the concepts described herein are equally applicable to both diffraction-based and pupil-based overlap measurements.

[0106] Other embodiments of the invention are described in the following numbered entries:

[0107] 1. A method for improving the measurement of a parameter of interest, the method comprising:

[0108] Obtain measurement data, which includes multiple measurements of parameters of interest associated with one or more targets on a substrate, each measurement being associated with a target among the one or more targets and different measurement combinations of measurement conditions used to measure the target;

[0109] Obtain asymmetry measurement data related to the asymmetry of the one or more objectives;

[0110] Based on the assumption that the parameters of interest in the measurement combinations have a common true value, determine the respective relationship between each measurement combination in the measurement combinations and the true value of the parameter of interest and the asymmetry measurement data; and

[0111] Use one or more of the relationships to improve the measurement of the parameter of interest.

[0112] 2. The method according to item 1, wherein the relationship is linear and is described by a proportionality constant.

[0113] 3. The method according to clause 1 or 2, wherein the one or more targets include a plurality of targets and / or target portions of the one or more targets, the targets and / or target portions being located within 1.5 mm of each other.

[0114] 4. The method according to any one of the preceding clauses, wherein, for each of the one or more targets, the measurement data includes two or more measurements of the parameter of interest, each measurement of each target being associated with different measurement conditions.

[0115] 5. The method according to clause 4, wherein the different measurement conditions relate to different polarization conditions of the measurement radiation used to obtain the measurement values.

[0116] 6. The method according to any one of the preceding clauses, comprising performing optimization, said optimization determining the relationship for each measurement combination in order to minimize the difference in the measured values ​​of the parameter of interest in the measurement combination.

[0117] 7. The method according to any one of the preceding clauses, wherein improving the measurement of the parameter of interest using one or more of the relationships comprises: correcting subsequent measurements of the parameter of interest from the corresponding target and / or the corresponding measurement combination using the relationships.

[0118] 8. The method according to any one of the preceding clauses, wherein improving the measurement of the parameter of interest using one or more of the relationships comprises: using the relationships to determine a measurement matching scheme for measurement illumination used in subsequent measurements of the parameter of interest from the corresponding target and / or associated with the corresponding measurement combination.

[0119] 9. The method according to clause 8, wherein determining the measurement selection scheme comprises: performing calibration to determine a preferred subset of measurement wavelengths for each target and / or measurement combination from a plurality of wavelengths.

[0120] 10. The method according to clause 9, wherein two or three wavelengths are included in a subset of the measurement wavelengths.

[0121] 11. The method according to item 9 or 10, wherein the method comprises:

[0122] Identify a subset of candidate wavelengths from the plurality of wavelengths;

[0123] The optimization process involves determining the relationship and candidate wavelength subset for each measurement combination to minimize the difference in the measured values ​​of the parameter of interest within the measurement combinations and candidate subsets; and

[0124] Determine which wavelength subset minimizes the variation of the parameter of interest in the measurement combination, and / or which wavelength subset best matches the measurement data with the expected model.

[0125] 12. The method according to clause 11 includes limiting the number of optimized measurement combinations by fixing or constraining one or more subsets of the targets to have the same preferred subset of measurement wavelengths based on the similarity of their respective response profiles.

[0126] 13. The method according to clause 9 or 10, comprising using one or more of the relationships to determine a reference value, the reference value representing the true value of the parameter of interest; and

[0127] The candidate wavelength subsets among the plurality of wavelengths are evaluated by comparing the measured values ​​of the parameters of interest for each candidate subset of the target with the reference values.

[0128] 14. The method according to clause 13, comprising, for each measurement combination or target, selecting a subset of candidate wavelengths that best match the measured values ​​of the parameter of interest with the reference values ​​as a preferred subset.

[0129] 15. The method according to any one of the preceding clauses, wherein the parameter of interest is overlap.

[0130] 16. The method according to any one of clauses 1 to 14, wherein the parameter of interest is the alignment position.

[0131] 17. A computer program comprising program instructions that, when run on a suitable device, are operable to perform the method according to any one of the preceding clauses.

[0132] 18. A non-transient computer program carrier, comprising the computer program as described in clause 17.

[0133] 19. A processing apparatus, comprising:

[0134] The non-transient computer program carrier as described in clause 18; and

[0135] The processor is operable to run a computer program included on the non-transient computer program carrier.

[0136] 20. A photolithography apparatus, comprising:

[0137] Align with the sensor;

[0138] A support for a pattern forming apparatus, used to support the pattern forming apparatus;

[0139] Substrate support for supporting the substrate; and

[0140] The processing apparatus according to clause 19.

[0141] 21. A measuring device, comprising:

[0142] Support for substrates;

[0143] An optical system for illuminating the structure with measuring radiation;

[0144] A detector for detecting the measured radiation scattered by the structure; and

[0145] The processing apparatus according to clause 19.

[0146] Although the use of embodiments of the invention may have been specifically referenced above in the context of optical lithography, it should be understood that the invention is not limited to optical lithography, and may be used in other applications, such as imprint lithography, where the context permits. In imprint lithography, the morphology of a patterning apparatus defines a pattern created on a substrate. The morphology of the patterning apparatus can be pressed into a resist layer provided to the substrate, and the resist is then cured by applying electromagnetic radiation, heat, pressure, or a combination thereof. After the resist has cured, the patterning apparatus is removed from the resist, leaving the pattern therein.

[0147] The terms “radiation” and “beam” as used herein encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., wavelengths of or about 365 nm, 355 nm, 248 nm, 193 nm, 157 nm, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., wavelengths in the range of 5–20 nm), as well as particle beams (such as ion beams or electron beams).

[0148] Where the context permits, the term “lens” can refer to any one or a combination of various types of optical components, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical components.

[0149] The foregoing description of the specific embodiments so fully reveals the general nature of the invention that others, by applying knowledge of those skilled in the art, can readily modify and / or adapt these specific embodiments to various applications without excessive experimentation and without departing from the overall concept of the invention. Therefore, based on the teachings and guidance presented herein, such adjustments and modifications are intended to fall within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that phrases or terms in this specification are for illustrative purposes and not for limitation, and that the terminology or phrases in this specification will be interpreted by those skilled in the art based on the teachings and guidance.

[0150] The breadth and scope of this invention should not be limited by any of the exemplary embodiments described above, but should be defined only by the appended claims and their equivalents.

Claims

1. A method for improving the measurement of a parameter of interest in device fabrication using photolithography equipment, the method comprising: Obtain measurement data, which includes multiple measurements of parameters of interest associated with one or more targets on a substrate, each measurement being associated with a target among the one or more targets and different measurement combinations of measurement conditions used to measure the target; Obtain asymmetry measurement data related to the asymmetry of the one or more objectives; Based on the assumption that the parameters of interest in the measurement combinations have a common true value, the respective relationship between each measurement combination in the measurement combinations and the true value of the parameters of interest is determined and the asymmetric measurement data is determined. as well as Use one or more of the relationships to improve the measurement of the parameter of interest.

2. The method according to claim 1, wherein, The relationship is linear and is described by a proportionality constant.

3. The method according to claim 1 or 2, wherein, The one or more targets include multiple targets and / or target portions of the one or more targets, all of which are located within 1.5 mm of each other.

4. The method according to any one of the preceding claims, wherein, For each of the one or more targets, the measurement data includes two or more measurements of the parameter of interest, each measurement of each target being associated with different measurement conditions.

5. The method according to claim 4, wherein, The different measurement conditions refer to different polarization conditions of the measuring radiation used to obtain the measured values.

6. The method according to any one of the preceding claims, comprising performing optimization, said optimization determining the relationship for each measurement combination in order to minimize the difference in the measured values ​​of the parameter of interest in the measurement combination.

7. The method according to any one of the preceding claims, wherein, Improving the measurement of the parameter of interest using one or more of the relationships includes: using the relationships to correct subsequent measurements of the parameter of interest from the corresponding target and / or the corresponding measurement combination.

8. The method according to any one of the preceding claims, wherein, Improving the measurement of the parameter of interest using one or more of the relationships includes: using the relationships to determine a measurement matching scheme for the measurement illumination used in subsequent measurements of the parameter of interest from the corresponding target and / or associated with the corresponding measurement combination.

9. The method according to claim 8, wherein, Determining the measurement selection scheme includes performing calibration to determine a preferred subset of measurement wavelengths for each target and / or measurement combination from multiple wavelengths.

10. The method of claim 9, wherein two or three wavelengths are included in a subset of the measurement wavelengths.

11. The method according to claim 9 or 10, wherein, The method includes: Identify a subset of candidate wavelengths from the plurality of wavelengths; The optimization process involves determining the relationship and candidate wavelength subset for each measurement combination to minimize the difference in the measured values ​​of the parameter of interest within the measurement combinations and candidate subsets; and Determine which candidate wavelength subset minimizes the variation of the parameter of interest in the measurement combination, and / or which wavelength subset best matches the measurement data with the expected model.

12. The method of claim 11, further comprising limiting the number of optimized measurement combinations by fixing or constraining one or more subsets of the targets to have the same preferred subset of measurement wavelengths based on the similarity of their respective response profiles.

13. The method of claim 9 or 10, further comprising using one or more of the relationships to determine a reference value, the reference value representing the true value of the parameter of interest; and The candidate wavelength subsets among the plurality of wavelengths are evaluated by comparing the measured values ​​of the parameters of interest for each candidate subset of the target with the reference values.

14. The method of claim 13, further comprising, for each measurement combination or target, selecting a subset of candidate wavelengths that best match the measured values ​​of the parameter of interest with the reference values ​​as a preferred subset.

15. The method according to any one of the preceding claims, wherein, The parameters of interest are overlapping.

16. The method according to any one of claims 1 to 14, wherein, The parameter of interest is the alignment position.

17. A computer program comprising program instructions that, when run on a suitable device, are operable to perform the method according to any one of the preceding claims.

18. A non-transient computer program carrier, comprising the computer program according to claim 17.

19. A processing apparatus, comprising: The non-transient computer program carrier according to claim 18; as well as The processor is operable to run computer programs stored on the non-transient computer program carrier.

20. A photolithography apparatus, comprising: Align with the sensor; A support for a pattern forming apparatus, used to support the pattern forming apparatus; Substrate support, used to support the substrate; as well as The processing apparatus according to claim 19.

21. A measuring device, comprising: Support for substrates; An optical system used to illuminate a structure with measuring radiation; A detector for detecting the measured radiation scattered by the structure; as well as The processing apparatus according to claim 19.

Images