MULTIPLE FIELD OF VIEW CALIBRATION PLATE FOR USE IN SEMICONDUCTOR MANUFACTURING

Abstract: An article of manufacture is provided both for coordinating multiple fiels of view of a plurality of cameras (18, 20, 22) so as to facilitate determining the distance between features on a semiconductor wafer, each feature being disposed within a different field of view, and for correcting image distortion within each field of view. The article of the invention includes a substantially rigid dimensionally-stable substrate (10), such as a plate, having a plurality of image distorsion calibration targets (12, 14, 16) that are each located at a known relative position. In a preferred embodiment, each calibration target is disposed at an orientation angle such that a first principle axis of the calibration target is perpendicular to a tangent to the perimeter of the semiconductor wafer. In the embodiment, the substrate includes physical alignment features that facilitate coordination of the multi-camera coordinate system of the article of the invention with the coordinate system of a semiconductor manufacturing machine.

Description: MULTIPLE FIELD OF VIEW CALIBRATION PLATE FOR USE IN SEMICONDUCTOR MANUFACTURING FIELD OF THE INVFNTION This invention relates generally to machine vision, and particularh to machine vision systems having multiple cameras for use in semiconductor manufacturing BACKGROI ND OF THE INVENTION Machine vision systems having multiple cameras are known It is also known to simultaneously acquire images from each of a plurality of cameras in a machine vision system Machine vision systems having a smgle camera for semiconductor wafer inspection, guidance. gauging, and location are also known However, presently, it is not possible to preciseh coordinate the fields of view of the plurality of cameras so that measurements can be performed across multiple fields of view to a precision of fractions of a millimeter, especially when there is significant image distortion in each field of view In most machine vision applications where the object of interest is a semiconductor wafer, or portion thereof, a single standard-resolution camera provides adequate resolution and scene area coverage. In such applications where higher resolution is needed, magnifying optics can be used, but scene area coveraae is reduced. AlternativeK , a higher-resolution camera can be used. thereby preserving scene area coverage, but such cameras can be prohibitively expensive, or unavailable at the resolution required by the application. Resolution is defined as the number of pixels that correspond to each unit area in a physical scene. Thus, resolution determines how much scene area can be represented by each pixel of an image provided by a vision camera. A high resolution camera allocates a large number pixels to each unit area. Thus, each pixel of an image provided by a high-resolution camera represents a smaller portion of the physical scene than each pixel of an image provided by a low- resoiution camera, assuming that both cameras are equipped with the same optics and are located at the same position. In some machine vision applications, both high resolution and coverage of widely separated portions of a large scene are simultaneously required. It is clear that one solution to the problem of achieving high resolution images of a scene area greater than can be covered by a single camera is to use more than one camera, each camera having a field-of-view that covers only a portion of the scene area. However, when there is more than one field of view, the relative physical positions of the respective reference origins of the fields of view are inherently indeterminate without reference to a common coordinate system. Consequently, position information in the image from each field of view cannot be related to position information from the other fields of view. Therefore, information interrelating the coordinate systems of multiple fields of view must somehow be provided. Although such information can be obtained visually, by capturing in each field of view an image of a landmark disposed at a known position in relation to a common coordinate system, position information so-obtained in a machine vision system may not be sufficiently precise for many applications. This is due to lens-related and camera-related distortion effects. Unfortunately, attempting to correct these distortion effects may degrade the final accuracy of the visually acquired landmark position information relative to the common coordinate system. SUMMARY OF THE INVENTION .An article of manufacture is provided for use in a multi-camera machine vision system wherein each of a plurality of cameras simultaneously acquires an image of a different portion of a semiconductor wafer. The article of manufacture of the invention makes it possible to precisely coordinate the fields of view of the plurality of cameras so that accurate measurements can be performed across the fields of view to a precision of fractions of a millimeter. The invention provides a framework for achieving both high resolution, as well as coverage of widely separated portions of a large scene. The invention provides a common coordinate system when there is more than one field of view, allowing position information from each field of view to be related to position information from other fields of view. In addition, the invention provides information interrelating the coordinate systems of multiple fields of view, with improved precision that facilitates use in many applications, without loss of accuracy due to image distortion effects. According to the invention, an article of manufacture is provided both for coordinating multiple fields of view of a plurality of cameras so as to facilitate determining the distance between features on a semiconductor wafer, each feature being disposed within a different field of view, and for correcting image distortion within each field of view. The article includes a substantially rigid dimensionally-stable substrate, such as a plate, having a plurality of image distortion calibration targets for correcting image distortion within a field of view, that are each located at a known relative position. In a preferred embodiment, each calibration target includes a feature that indicates an origin of the calibration target. Preferably, each calibration target is located so that it can be directly viewed by a camera, and there is a calibration target for each possible camera location. In applications where viewing the perimeter of a semiconductor wafer is of interest, each calibration target is disposed on the substrate so the target can be in the field of view of a camera positioned to include a portion of the edge of the semiconductor wafer at run-time. In a preferred embodiment, each calibration target is disposed at an orientation angle such that a first principle axis of the calibration target is perpendicular to a tangent to the perimeter of the semiconductor wafer so as to substantially eliminate jitter along a corresponding first principle axis in a camera aligned parallel to the first principle axis of the calibration target. Jitter along a principle axis of a camera is a source of reduced position measurement accuracy of distances along the principle axis. In applications where viewing the perimeter of a semiconductor wafer is of interest, it is preferred that a surface of the substrate be of a size and shape such that each calibration target disposed on the substrate can be in the field of view of a camera positioned to include a portion of the edge of the semiconductor wafer at run-time. It is further preferred that the surface of the substrate be of a size that is larger than the major surface of the semiconductor wafer such that each calibration target disposed on the substrate can extend across a portion of the edge of the semiconductor wafer in position at run¬ time. It is further preferred that the surface of the substrate be of a size that is larger than the major surface of the semiconductor wafer such that each calibration target disposed on the substrate can extend across a portion of the edge of the semiconductor wafer in position at run¬ time, and the surface of the substrate is sufficiently large that misalignment of the wafer with respect to the position of the plate at train-time can be accommodated, the larger surface of the substrate also providing enhanced structural integrity as compared with a smaller plate. In another preferred embodiment, the substrate includes physical alignment features that facilitate coordination of the multi-camera coordinate system of the aπicle of manufacture of the invention with the coordinate system of a semiconductor manufacturing machine, such as a wafer stepper, a wafer prober, or a wafer handler, for example. Examples of physical alignment features include a notch or a flat formed anywhere on the substrate, such as a notch or a flat of a typical semiconductor wafer. Other examples of physical alignment features include a pair of holes sized to receive a pair of pegs that project from the stage of a semiconductor processing machine, or a pair of pegs sized to be received by a pair of holes formed in the stage of a semiconductor processing machine. In another preferred embodiment, the substrate includes markings that communicate relative displacement information regarding both the linear and angular displacement of each calibration target with respect to each other calibration target. In an alternate embodiment, the apparatus includes a substantially πgid dimensionally- stable substrate having a systematic array of features that are sized such that more than one of the features can fit within a field of view. The array is of a spatial extent such that more than one of the fields of view are substantially filled with the features Also, the systematic array of features is characteπzed by a distribution density suitable for correcting image distoπion withm a field of view. For example, the systematic array can be a regular array, each feature can be separated from each nearest neighboring feature by an equal distance Alternatively, the separation between neighboring features can be a function of position on the substrate, so that given the relative location of two neighboring features, their position on the substrate can be deduced. In addition, the substrate includes more than one landmark, each landmark being located at one of a plurality of possible camera positions, where the possible camera positions are of known relative position, so as to facilitate determining the distance between features each disposed within a different field of view. BRIEF DESCRIPTION OF THE DRAWINGS The invention will be more fully understood from the following detailed descπption. in conjunction with the accompanying figures, wherein. Fig. 1 is a schematic perspective representation of the calibration aπicle of the invention, with three cameras; Fig. 2 is a representation of a calibration target having a regular array of dots for use in camera distoπion correction, and a pair of non-parallel lines for use as a position-determination "landmark"; Fig. 2 A is a representation of a calibration target having a combination of scales and dots; Fig. 3 is a calibration target that includes a square array of dots, characterized by the parameters D , P x , S x , D y , P y , and S y ; Fig. 4 is a substrate having a plurality of non-coplanar planes; one plane for each camera; Fig. 5 is an embodiment of the invention having seven calibration targets disposed at seven possible camera locations, and a pair of holes for receiving a pair of dowel pins; Fig. 6 is a flow chaπ illustrating the major steps of the calibration phase of one way to use the invention; Fig. 7A is an error vector field of radially symmetric image distoπion of an image, each error vector representing the distoπion effect at the coordinates of the origin of the vector; Fig. 7 B is an error vector field of the image of Fig. 7A. after image distoπion coπection; Fig. 8 is a plot of two different polynomial fits through eight radial sections of the error vector field of Fig. 7 A; and Fig. 9 is a flow chaπ illustrating the major steps of the run-time phase of one way to use the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Fig. 1 provides a schematic perspective of an embodiment of the calibration substrate of the invention. In this instance, the substrate is in the form of a rectangular plate 10 having three calibration targets 12, 14, 16, disposed so as to facilitate viewing by three cameras 18. 20, 22, respectively. Calibration targets can be rotated with respect to each other calibration target, and cameras can be rotated with respect to each other camera. A calibration target can be rotated at any angle with respect to an axis of maximum resolution of a camera, but improved performance in some applications can be achieved by aligning a non-diagonal symmetry axis of a calibration target with the axis of maximum resolution of a camera. For example, if a camera has rectangular pixels, the axis of maximum resolution is perpendicular to the longitudinal axis of each rectangular pixel. When the feature of interest is the perimeter of a semiconductor wafer, its radial position can be most accurately measured by orienting the field of view of the camera so that the axis of maximum resolution of the camera is perpendicular to a tangent to the wafer perimeter. The substrate should be rigid, especially when the lens used in the machine vision cameras is not teiecentric. and so would not provide an oπhographic projection. It is even more impoπant that the substrate be dimensionally stable, so as to ensure that the distances between and within calibration targets remains substantially constant. For example, the substrate should be made from a material that has well understood thermal expansion propeπies. Likewise, the substrate should not be made from an elastic material. Examples of suitable materials include glass, transparent crystal, metal, and silicon. To facilitate handling with a vacuum chuck, to rigidly mount the substrate, for example, the underside of the plate 10 is substantially smooth. The substrate can be rigidly mounted in other ways, such as using a vice, or a pneumatic toe clamp. The number of calibration targets on the substrate is determined by the number of possible camera locations that may be used during the run-time phase, i.e.. when the invention is actually used. For example, there may be three or four cameras, and a set of seven possible locations for each of the three or four cameras, requiring a calibration substrate having a calibration target at each of seven positions on the plate 40. as shown in Fig. 5. In general, there can be as many calibration targets on a substrate as the surface area allows. Each calibration target is disposed at a position on the surface so as to facilitate direct viewing by a camera. In some applications, it may be advantageous for the substrate to include a regular array of calibration targets having systematic inter-target spacing. In a preferred embodiment, each camera views the calibration target such that the central axis of the camera lens system is co-linear to the normal direction at the center of the calibration target, i.e., perpendicular to the plane of the calibration target, as shown, for example, in Fig. 4. It should be noted that if the cameras are mounted to a material with thermal expansion propeπies. temperature can affect the position of each camera with respect to the substrate, and therefore must be taken into account, as is well understood by those skilled in the an. With reference to Fig. 2. each of the calibration targets 12, 14, 16 shown in Fig. 1 includes a regular array of dots 24 for use in camera distoπion coπection, and a pair of non-parallel lines 26 for use as a position-determination "landmark". Preferably, the pair of non-parallel lines is mutually perpendicular. Alternatively, a combination of scales and dots can be used, as shown in Fig. 2A. Advantageously, a "checkerboard" pattern, such as alternating black and white, or grey and white squares can be used. In general, any graphical design that provides a regular array of features can be used as a calibration target. In an alternate embodiment, each calibration target includes relative position and orientation information, such as the position and orientation of the calibration target with respect to at least one other calibration target, or with respect to an origin anywhere on the substrate. Alternatively, each calibration target can include an index value that leads to relative position and orientation information stored elsewhere. The relative position and orientation information is advantageously encoded in a manner that enhances machine-readability, such as a 1 -D or 2-D "barcode", or simply presented in a machine-readable font. In an alternate embodiment, a single calibration target covers the entire surface area of the substrate, poπions of which are used by each camera, including multiple instantiations of a "landmark", each landmark being disposed at a possible camera location. To anticipate most possible camera locations, a regular array of landmarks can be included. In another version of this alternate embodiment, each landmark includes relative position and orientation information that provides position and orientation information relative to at least one other landmark or to an origin point, or includes an index value leading to relative position information stored elsewhere. The position and orientation information is advantageously encoded so as to optimize machine readability, such as by using a 1-D or 2-D "barcode", or simply presented in a machine-readable font. Fig. 3 shows a calibration target that primarily includes a square array of dots, characterized by the parameters Nχ 3 Ny, D, P, and S. Here, N'χ is the number of dots along the X-axis, NY is the number of dots along the Y-axis. D is the diameter of each dot, P is the pitch or spacing of the array of dots, and S is the size of the area covered by the dots. In most applications N τ χ is equal to Nγ. For example, to locate the position and orientation of an object having a boundary that traverses the field of view of a plurality of cameras, the values of Nχ 5 NY, D, P, and S can advantageously be about 20 dots. 20 dots, 15 pixels. 30 pixels, and 600 pixels, respectively. Many vendors can manufacture a plate having a plurality of such calibration targets, such as Max Levy Autograph. Inc.. Philadelphia, PA. and Applied Image Corporation. Rochester. NY. It is possible to manufacture a plate having calibration targets where the maximum dot location error is on the order of one micron. Referring again to Fig. 2, a calibration target as included in the invention can also include a "landmark". According to the invention, a landmark is a feature of a calibration target characterized by a known position with respect to at least one other landmark. Thus, knowing the position of an image feature with respect to a first landmark can. in combination with knowledge of the distance between the first landmark and a second landmark, provide knowledge of the position of the image feature with respect to the second landmark. Further, knowledge of the distance of a landmark with respect to an origin of a global reference frame that includes a plurality of fields of view can, in combination with knowledge of the distance between the landmark and an image feature, provide the coordinates of the image feature in the global reference frame, regardless of which field of view includes the image feature. For example, a pair of perpendicular lines 26 can be used as a "landmark". Alternatively, any other graphical feature that specifies a point in a calibration target can serve as a landmark, such as a pair of non-parallel lines, a cross-hair, or a bull's-eye. for example. It is also possible to use a group of dots, or other poπion of a calibration target as a landmark. For example, the lower left dot, or lower left square grouping of four dots, can also serve as a landmark. Also, a missing dot in an otherwise regular array of dots can serve as a landmark. Although Fig. 1 shows a substrate that is in the form of a rectangular plate 10 having three calibration targets 12. 14, 16, the substrate of the invention can take many other forms. For example, the substrate can include a plurality of non-coplanar planes; one plane for each camera, as shown in Fig. 4. In this case, each of the three planes 28. 30. 32 can include a plurality of calibration targets, or can include a single calibration target that covers a substantial poπion of each plane. Although only a single camera is oriented towards each of the planes, more than one camera can be used in conjunction with each of the planes 28, 30, 32. The substrate of the invention can also be a substantially circular plate, as shown in Fig. 5. The circular plate 40 can advantageously be sized so as to resemble a semiconductor wafer, for example. The plate 40 has six calibration targets 42 disposed at six possible camera locations. In addition, the plate 42 includes two dowel pin holes 44 that can be used to repeatably fix the plate 40 with respect to the coordinate frame of a mechanical device, such as a wafer aligner. Given the position of a pair of dowel pins that are rigidly affixed to the mechanical device, one can then know positions of image points with respect to the coordinate frame of the mechanical device. As an alternative to the holes 44. the circular plate 40 can include a notch or flat in its perimeter, instead of the holes 44, the notch or flat being cooperative with contacting pins of the mechanical device. Note that the invention does not require fixation of the substrate with respect to the coordinate frame of a mechanical device, such as a "wafer stepper" for performing the step-and- repeat operations of semiconductor device fabrication. However, in some applications, such fixation may provide additional benefits. How To Lfse The Invention The aπicle of manufacture of the invention can be used to determine the relative displacement of a first point in a first corrected field of view with respect to a second point in a second coπected field of view. A method for using the invention includes a calibration phase, as shown in Fig. 6, and a run-time phase, as shown in Fig. 9. Note that method steps are indicated in the text below by enclosure in parentheses. Referring to Figs. 1 and 6, the calibration phase of the example method explained herein for using the invention includes the step (46) of positioning the cameras 18, 20. and 22 in fixed relationship with the calibration plate 10. such that a calibration target 12. 14, or 16 falls within the field of view of each camera 18. 20. and 22. The calibration plate 10 can be suppoπed by an X-Y-Θ motion stage that can precisely translate and rotate the plate 10, and can even be secured to the motion stage using a vacuum chuck for enhanced stability. The X-Y-Θ motion stage can be controlled so as to bring the plate 10 into coπect position with respect to the cameras 18. 20, and 22. Next, a distoπion correction map must be estimated (48) for each camera 18, 20. and 22. First, each camera 18, 20, and 22 acquires an image of a calibration target, such as a target as shown in Fig. 2. This calibration target includes a plurality of regularly spaced dots 26. Since it is known that the spacing of the dots 26 is regular, any departures from regularity evidenced by the acquired image of the dots represents distoπion in some aspect of the image capture process. This distoπion can be due to a variety of factors, to be discussed below. The distoπion map can also compensate for translation and rotation of the acquired image of the calibration target with respect to a desired location and orientation of the calibration target in the field of view. Image distoπion in the acquired image can be due to one or more of the following factors: lens irregularities, such as astigmatism and coma; tilt of the lens in either the X- or Y-axis with respect to the plane of the imaging device, such as a CCD (charge coupled device); and tilt of the camera in either the X- or Y-axis with respect to the plane of the calibration target. Other types of image distoπion include: perspective distoπion, chromatic distoπion. and many other types of distoπion, as discussed in "Optics" by Hecht & Zajac, pp. 175-194, Copyright 1974, Addison- Wesiey Publishing Company, Reading MA. Also, normal lens behavior inevitably introduces some degree of image distoπion. Further, the distoπion map can account for scale factors, such as the magnification of the camera lens system, and the image pixel-to-physical distance conversion, i.e., the amount of actual surface area of the physical image plane that is represented by each pixel in a paπicular camera's field of view. Referring to Figs. 7A and 7B, one way to visualize the effects of image distoπion in a single field of view is to plot the displacement of each dot of a calibration target, such as shown in Fig. 2. from its ideal position. For each dot in a field of view, there can be a displacement due to image distoπion that is represented as a vector having a magnitude and a direction, and originating from an ideal dot position. Such a representation of a plurality of displacements, one displacement vector for each dot of a calibration target, is shown in Fig. 7A. An image distoπion correction map is a transformation that operates upon a distorted image so as to provide an undistoπed image, i.e., to correct the distorted image. Thus, an image distortion correction map that would correct the image distortion shown in Fig. 7A is effectively the inverse of the map of Fig. 7A. When such a distortion correction map operates upon a distoπed image of a calibration target, the result is substantially similar to the ideal image of the calibration target. Fig. 7B shows the small remaining deviations from ideal, as indicated by an array of short vectors, each of which looks like a dash oriented at an angle ranging from 0° to 360°. In more formal terms, a distortion coπection map is a function that transforms acquired image coordinates into corrected physical coordinates. The calibration target is used to establish a uniform distance metric for determining a corrected physical coordinate system, and the landmark feature of the calibration target is used to establish a local origin for the coπected physical coordinate system. In the context of multiple fields of view, a distortion correction map is a function that transforms image coordinates in a particular field of view to the corrected local physical coordinates that correspond to that field of view. There can be a different distortion correction map for each camera of a multi-camera system. Then, to obtain the corrected local physical coordinates in a global physical reference frame that includes each field of view from all of the cameras, each set of corrected local physical coordinates must be transformed into global physical coordinates. There is a specific local-to-global coordinate transformation for the field of view of each camera, which transformation is estimated in step (50) of Fig. 6. It is possible to generate a composite transformation that achieves the equivalent of an image correction transform followed by a local-to-giobal coordinate transform for each field of view in a muiti-camera system. Thus, given local pixel coordinate data within a field of view, and an associated camera index value, a single transform can be obtained that provides global physical coordinate data, while also coπecting image distortion. Returning to Fig. 6, to estimate an image correction calibration map for a particular camera (48), a least-squares-fit of two corresponding sets of points is performed, which will now be described in detail. A calibration map is a function that transforms image coordinates to corrected physical coordinates. Let's state this more precisely and formally. Call a point tn image coordinates { u. v} , and a point physical space { x, y } . Then a calibration map is a vector function F. { x. y} = F i { u. v } ) ( 1 ) This function can be represented bv cwo real-valued functions: How do we determine (i.e.. compute) G,. and G ? First, we decide on the form of the functions themselves. Tnat is. G . = G v i ; (3ϊ for some ( potentially large) set of parameters a.. G, can oe α polyno¬ mial, an enormous lookup table, or aeurai network! Once we decide on the form, com utir.g the map is reαucec. :o ήnding the parameters a, that control the ber.a'-ior of the map. Topically this is done by assembling a set of corresponding points that represent examples of what we want the map :o compute. In other words, we produce sets of corresponding points for which we desire these maps x » G (a . v ' ■ G > u , A ) Compuuπc the map ;s often alleα i:::n~. because our map usualh represents some inc of best tit between :ne coorαinate sets i a v ( ana ( , y, . The computauonal ta≤ is simpiy this: given tr.e sets of coordinates, compute the mapping parameters a.. The central difficulty of the πtung task is that we want the map to be an excellent ήt between all points within the image and physical points that correspond to them, not just the small subset we ' ve actu¬ ally measured. We achieve this goal by choosing an appropnate form for the mapping funcuon, and carefully selecting and easuπng the subset of calibration DOints. or features. There are two basic approacnes to the nonlinear calibration mapping function. Stted funcuon and grid interpolation. In the first approach we have a function {x'. y'} = F (x. y) (5 that can achieve an acceptable fit for the type of distoπion encoun¬ tered. In other words. (Error = | ( x', y'} - F(x. y) ,|) < ε (6) for all points in the image, and ε is su ficiendy small. You then find the coefficients of F via least-squares fitting. The gπd interpolation approach is to subdivide the image into small regions: in each region a correction is applied. The correction in one region has no particular relationship to correcαoπ applied elsewhere in the image. Grid interpolation has the advantage that any type of distortion can be corrected: you don ' t nave to ήnd a function "*nose symooiic form happens to form a good map. The closed-form approach can be more accurate because it will not be so influenced by measurement error, an incorrect gπd point location can distort a region of the image Here are some ways of implementing an interpolation map. GCL (General Calibration ( jfe- y iS.orJ Tόoi. ' ) computes a gπd of linear transforms. each of which is applied co its local paten. The borders of the regions are tπangular. The transforms are adjusted so the overall map is continuous across paten boundar.es B ilinear interpolation of error vectors. Fit a linear transform to the calibration gπd data. Find residual vectors from the gnd. Store the vectors in a look-up table. Do bilinear interpolation on the gπd to do complete correction. ' se the bare linear transform for pre -lead-measurement inspection tasks. • Splined interpolation of error vectors, using bicubic splines. Coons patches, etc. This results in a smoother (but not necessaπly more accurate) map than the bilinear mterpolauon. Here arc some ways of implementing a fitted function map- Radial fit function to error vectors For the uncorrected image in Figure!, note that the errors are predominantly radial m nature. around the ooαcal center. Fit a polynomial or other closed-form 17B function to the data. This avoids the interpolation problems, and gives a differentiable (smoother) map. This oπiy works for certain kinds of distortion, such as that seen in Figure T. Figure 9 shows two different polynomial fits through S ra ial sections of the dis¬ tortion vector field in Figure7 Note that a 5' Λ order polynomial seems to approximate the errors rather well. You can see the haz¬ ards of using a high-order polynomial to try for too close a fit to the measured data: mere are large oscillations that would lead to very large errors near the image edges. We must accept higher residuals to achieve better smoothness and fit througn the entire image. Note that extrapolation oeyonc :r.e end-most rows and columns of the gπd of calibrauon points is a prooiem for gπd-baseα interpo¬ lation methods as well. • Other fitting functions, for example, spheroidal harmonic func¬ tions. Study Figure 8 carefully and use it to help you remember a very important fact: Small calibrauon residuals do nor em che maD is globailv accu¬ rate! We can always find and adjust some function to achieve a good fit between a limited set of points. The proof of accuracy lies in using the calibrauon on points far away from the calibration set. Find a map 17C that is acceptably good everywhere. The simplest fined functions are linear ones. The unconected plot in Figure " ? is a graph of the residuals left over after a full linear correc - don. Thus we can see that linear maps are not good enough for our purposes- However. I did want to compare different types of linear maps. I did so for the data in Figure"!, which was taken from a Cos- tnicar 16mm lens. This comparison is in Table I, below. Table 1 Comoaπsoπ of Different Linear Fits The conclusion: accommodating pixel aspect ratio is important for the quality of fit. Skew angle (the difference between 5 and 6 parame¬ ters is not. 17D For the calibrauon map I chose a fitted- unction map using a polyno¬ mial foπn. Why? I believe that when a good ήt can be achieved, a fit¬ ted function is better because there is an underlying smooth model of the distortion. This model will probably work well to correct all the points in the image, not just the ones in the calibrauon set. A fined function is much less sensitive to a small number of outiier measure¬ ment errors. Furthermore, an interpolation scheme provides no inde¬ pendent measure of the quality of the map; all the calibration points can be perfectly fit. leaving no reasonable esumate for the residual errors. 17E The foπn of the fitting functions is as follows: u.iO 1*1 ≤Λ y = G y (u.v) = b-.u'vl ^j-) ι.j≥0 where n is the polynomial order. 17F A calibrauon can be no more accurate than the raw data used to com¬ pute the πmng map. It is essenuai to provide a high-quality unage and high-accuracy feature extracuoπ to get the best calibrauon. How do we know the accuracy of a given calibration'' small calibrauon residuals do not necessaπly indicate an accurate calibrauon. Misunderstanding of the term "accuracy" is so common that we'll staπ by defining terms. 17G Calibrauon residual. The error (Pythagorean distance) between a transformed point and its coπesponding known location. The known locations consist of the set of features provided during the computation of the mapping funcuon's parameters. Note that the residuals are different for each map: typically a caiibranon con¬ sists of a transformauon from image to physical space Map residual. The error (Pythagorean distance) between a trans¬ formed point and its coπesponding known locaαon. for any arbi¬ trary point within the domain of die calibrauon map. It's my opinion that when we say a calibration is accurate. v»e mean that us pnmar/ map ( i.e. the map that transform in the direction we care most about) has low map residuals. It's important that we have good methods to measure, to an accept¬ able uncertainty, the residuals that character.ze a calibrauon. The fol¬ lowing section describes some methods that [ have used as pan of th s project. 17H A rouune to compute map resiαuais was implemented. It is tenαed to support several kinds of characterizations, and so it nas some fea¬ tures that are not always needed. This test requires only a single map. but you must have as input a correspondence, that is. a set of match¬ ing image points and known corresponding physical locations. The aigoπthm performs the following steps: Use the map to transform the obser/ed locauons. For an i3nj2?:-.ys example: { , } = --g2?.-.ys - > - rir.s = rr.i { u,, v ( } ( q ) The points { ( , ( } are now in physical space. They may or may not be in locauons that closeiy matcn the given points { \ , y t ;- , however. Therefore, we usually perform tne optional step of per¬ forming a least-squares πgid-body fit between the transformed and model points. Then transform the points to fit the model. Fol¬ lowing our example: { ■ V- y. " } = T x ( \ O) where T is the best-ήt rigid-body transform between { ,'. y ( '} and I I • . , J What are we really doing here ? Simply removing the restriction that the calibration features be measured exactly the same loca¬ tions as they were when the calibration was computed. The ngid- bodv transform allows us to "move" the calibrauon plate to a 171 best- fit locauon over the known iate. so we can check for errors resulting from distoraon. not from locauon. Now we compute the error vectors. Far the above example: Each error vector is simply the coordinate -wise difference between the known point and the point predicted by the map. To produce the distortion maps in this document. I plotted the error vectors as a funcuσn of ideal physical locauon { , y } . To gener¬ ate a number indicauve of map accuracy, you can also compute stausucs of the errors. For example, the maximum map residual is: max-resid = max i e - - <_ - ι ( ι > I use tne acove rouune co compute calibration resiαuais This pro¬ vides at least a self-consistency cnecx on the quality of the calibra¬ uon. In other words, if the calibration resiαuais are h gh, you know you have a bad calibrauon. The map residual test can also be used to compute an esumate of map residuals, not just caiibrauon residuals. Here are the steps- Place the calibrauon plate in the image, and generate a caiibrauon from it. Record the maximum and mean calibration residual. For each of a number of test cycles, do the following Move the caiibrauon plate to a new locauon within the image Acquire tne image, and generate a new correspondence rro it. • Compute the maximum and mean residual for this new corre¬ spondence. The overall stausucs on the residuals from all test cycles can provide you with a reasonable esumate of the map residual error: in other words, the largest error you are likely to encounter tn using the cali¬ brauon. You should also check to see that the residuals dunng the test cycles are not greatly higher than the calibrauon residuals. They will naturally be somewhat higher: the calibration was computed pre¬ cisely to make the calibrauon residuals as low as possible! But if the map residuals are SO or more rugher. this might indicate that there is something wrong- 17J * The acquired feature locauons might be in error, either at calibra¬ uon πme or during the map test. • The foπn of the caiibranon funcπon might be a poor match for the distortion of the lens. To ensure that the caiibranon map adequately represents the lens dis¬ tortion, the caiibrauon features should evenly cover the enure image area you intend to use for lnspecuon. 17 A teiecentπc lens can be used when scale-induced errors are unac- ceptably large. A telecentric lens is manufactured to have a single fieid-of-view size, that does not change with lens-object distance. A theoreucally perfect telecentric lens has no scale error at all. It should also almost comDletelv eliminate tilt errors. What have we learned from this? Measurement accuracy is extremely sensitive to small unmodeled deviations in die device presentation geometry for standard lens setups. For our 16mm lens we were able 17L to achieve a maximum residual of 0.038 pixels (2.6 microns) at cali¬ bration time, but an eirσr of only 100 microns in presentation dis¬ tance raises this residual to approximately 0.35 pixels (24 microns). We can see from (23) thai: • The sensitivity is proportional to O, the center-to-edge distance. Therefore the error is reduced when measuring small devices. • The sensitivity is inversely proporaonal to f, the focal length. We have already seen how longer focal lengths provide lower residu¬ als (flatter, more distortion- free images). A long focal length aiso reduces the distance sensitivity. • The sensitivity is proportional to the magnification. Therefore, a larger field of view reduces the distance sensitivity. Finally, if the required error is smaller than the sensiuvity due to unavoidable distance deviauons. the use of a telecentric lens should be considered. 17 descπbe the implementauon of a distortion-correct¬ ing mathemaαcal transfoπnauon of two-dimensional points. This transformauon. or map, is polynomial in form. We compute the map by linear least-squares fitting of two corresponding sets of points. Why is this useful? Pnncφally it is used to correct opucai distortion in machine vision systems. Figure7 illustrates the errors using a best- fit linear map versus me polynomial map. The largest error for this case (a 16mm lens) is 2.6 pixels versus 0.038 pixels. Why a polynomial? ouce how the error vectors m the linear case appear to radiate from a common center. Notice further that the length of the vectors is approximately a funcuon only of radial dis¬ tance from the center, and not of angle. Figure 2 shows polynomials fit thxoush one -dimensional cross-secuons of the above Linear error 17N map. The fit is rather good, suggesting that a two-dimensional poly¬ nomial will foπn a good map. Fining a funcuon to a set of data has the advantage of minimizing the sensitivity to measurement errors. Thus if calibrating using an image of a gπd of circles, two or three bad circle measurements will have only a small impact on the overall correction map. Note that in Figure 8 the use of a high-order polynomial function acmallv can increase errors bv fimns too exactlv to the observed data set. Therefore we have limited the polynomial order to five in this imDlementation. Using the convention that [x,y] ts a model coordinate, and [u.v] is the corresponding observed coordinate, we σansform using the following function: ι. j 20 y = G y ( ιι. v) = b , j U ' vJ i. j ≥ O » 170 where n is the polynomial order. We can see that the total number of coefficients required is: n. = (n- l) (n + 2) VO For this implementauon. we use a single array to hold die coeffi¬ cients. Odd members are coefficients for G and even members for G The full 5 th order form for G x and G y is as follows: G χ (u. v) = a, + a-u -r i.v ι- a-u — a„v- - a. ,av o a,.u- - a. .V 3 - ; .a-v a, 9 uv- -r a-,u T a-,v ι- a.-u-v- +• i_,_,u J v -- a, : n u l v J ■*■ a., ii- T ., v J - i "-.u- 1 v- ■>■ ? .π-v J 39" MΓ G (u.v) = a.*a,UTa j -t- 3 u- + ■ a 1Q v- ■ ► ii OΌ + a u 3 -»- l6 v3 ^ a 13 u-v -- a_. 0 uv- +■ - ^u 1 + a_, 1 ,"/- + τ . a α - s 6u u -v * - - ^r a*-:8 U U3 V -r a *-3 n 0.lV 3 4 32 ui " * " a 3-t vi * a 36 u3v2 + a 8 u2v3 * a -M. u4v " a -i2 UV We solve for G and G y using the Singular Value Decomposition (SVD) technique to solve least-squares problems. We solve the over- determined equation system: Ax = b O 17P that is constructed in the following form ( ' shown for G only: the form for G y is analogous): = b We soive this as follows: C2o> λ. b A " is the oseudo- inverse- of A. It has the rocertv that it minimizes: lA.c - cm ( 2 which is just the sum of squared errors in the x coordinates. Note that we can minimize separately in x and y. since each is a separate func¬ uon of u and v. The traditional method of comouuns A *- is: A = A T A A T (22.) This solves the system when A T A is invertibie: however. A " (as well as a least-squares soiuuon) e.xists even if not. For this reason we have used the method of SVD to solve for A " : the method also has an excellent rεputauon for numerical stability and robustness. We have used the implementation given in : Press, William P.. Fiannery. Teukoisky. & Vetterling, Numerical Recipes in C: The Art of Scientific Computing, Cambridge Uni¬ versity Press, Cambridae. UK. 1988. I. Also known as the Moore-Psnrosc inverse. An excellent treacment :s Jiven in Strang, Gilbert, Linear Algebra and Its Applications, Academic Press. New York, NY, 1980. 17Q For any m x α matrix A, where n. A can be written as the prod¬ uct: m x n m x n n x π π x π A U Q where the matrix U is column-orthogonal, i.e.. u τ u = : { and the matrix V is ortho g onal: v'v = v j r) and the maui.x Q is zero except for the non-negative values w ( on its diagonal. The w. are called the singular values of A. The SVD is a truly wonderful concoction, ana it tells us almost everything we want to know about A. First of all, the pseudo-inverse is obtained by trivially inverting each component: VQ- l U T ( <- ) The number of nonzero singular values is precisely the rank of A. Furthermore, the condition number of A. a measure of its "lll-condi- uoned-ness". is the rauo of the largest to the smallest singular value. 17R It is the ability to extract and manipulate the rank of A that makes SVD the proper way to solve our least-squares problem. If the condi¬ tion number approaches the inverse of the computer's numerical pre¬ cision, the matrix is numerically, if not theoretically, singular. So before computing set ail sufficiently small w { to zero. We used a condition number limit of iO 11 in the implementation of the Dolvnomial πt. Therefore, we set to zero all sinsuiar values < I IO" ' ! We found it necessary to scale the matrix A in (■ to reduce its condi¬ tion number. Columns of A contain successively higher powers of the coordinates, producing a very large dynamic range that could exceed the machine precision. We encountered this problem fitting from arbitrary model coordinates to i ase coordinates, the inverse of the typical direction. As we var- ied the ~ unit of measurement from 10 " A° to 10n , the numerical rank changed from 4 to 21 (full rank) to 15. leading to catastrophic loss of 17S accuracy. Naturally we would prefer the fitted solution to be insensi¬ tive to the particular unit of measurement chosen. The particular scal¬ ing scheme described below completely fixed the problem. In the same test, the scaled A has a condition number of roughly 45 over the full range of unit size, compared with the unsealed condition number that ranged from lθ " to lO- 6 . The scale factor is computed so that the largest element of A is at most unity. Thus we compute: k = tπaxiU j , v ( ) ( ^ O and then use in A the scaled elements: We can see from the form of gf) that mis scaling simply makes all terms in the polynomial roughiy the same order of magnitude. Thus we can add them without catastrophic loss of precision: G t (u. v) = J , , , U11'' vVJi J ~ ^ i. j ≥ 0 Once we solve fj , we scale the resulting polynomial coefneients α i( to reduce the computation required to transform coordinates, as fol¬ lows: a 'l fc. j 0 " > Let's illustrate this with an example. Suppose we ' re fitting the fol¬ lowing sets of corresponding points: 17T The x coordinate of the third point would be computed this way: 62 = 32000a. + 32000 3 2900.u - 3 x t0 a. + Lx.0 17 . /«t) We can see that if the linear term contributes 90% of the answer (as is normally the case), ai must be 10 times as large as a^. This is in fact typical, but computing d e coefficients with this much dynamic range is intractable. Why? Our A matrix iooks like this: rea y intended to add a minor "curvature tweak " ' to the dominant ear terms, yet their magnitude is so large ±at only two or three signif¬ icant digits in the linear terms will survive their addition or subtracuon. Yet to compute the SVD we must do exactly that: add or subtract linear combinations of columns with each other. Thus we have to scale A to compute A" We'll use the maximum coefficient value as a scale factor: = 32000 , j ) Our new A matrix becomes: We have reduced the dynamic range from 10 13 to 10 3 by scaling! But now, when we soive for the coefficients, we've really solved a differ¬ ent problem: e.g., (3 ^becomes: (JΪH)' 62 = 32000 3 2900 - i - (J*) 17U A We fix this up by changing the foπn of (βf-) as follows: λ 62 * 3200θ(~) This is how we arrived at the scaling in equations φt and 135".. 17V For enhanced speed, the image correction calibration map is best implemented as a look¬ up table, where the surface represented by the polynomial function is approximated by a close- fitting tiled surface. Each of the tiles of the surface is a linearized version of the polynomial function. Increasing the number of the tiles, while decreasing the size of the tiles, provides higher accuracy, but requires more memory. Use of a look up table in this way provides the accuracy of the polynomial fit method, with the speed of the grid interpolation approach. Returning again to Fig. 6, to estimate a specific local-to-global coordinate transformation for each field of view (50), our knowledge of the relative distances between the landmarks of the calibration targets is now exploited. This knowledge can be obtained in a variety of ways. For example, the information can be obtained directly from the calibration plate, either by reading information that was printed on the plate in the form of one or more barcodes, as described above, or in some other machine readable form. Or. precise knowledge of the layout of the calibration plate, in conjunction with approximate knowledge of position of a calibration target on the calibration plate, can provide precise knowledge of the relative distance between calibration targets. Alternatively, the relative distance information can be provided by the manufacturer on paper or magnetic media. Alternatively, the relative distances between the landmarks of the calibration targets can be measured using a machine vision technique, for example. To estimate the transformation from distortion-corrected camera coordinates into global coordinates (50), for a given pixel in a given field of view, the following procedure is performed. The vector that represents the distortion-corrected distance of a pixel from a landmark in a field 18 of view, is added to the distance of the landmark from an arbitrarily selected origin that is common to all the fields of view The resulting distance is the distortion-corrected position of the pixel in the global coordinate system It should be noted that, theoretically, the order in which the distortion correction transformation and the local-to-giobal coordmate transformation are performed is significant This is because the transformations are vector maps, and the combination of the vector maps is not commutative In particular, the calibration map estimated for the field of view of each camera provides an error correction vector for each pixel in the field oi view Likewise, the local-to- global transformation map for each pixel in each field of view provides an local-to-global coordinate transformation vector The two transformations can be combined into a single transformation that performs both image distortion correction, and local-to-globai coordinate transformation, so that given the identity of any pixel, and the camera that acquired it, we can know the position in the global coordinate system. This position can be the actual physical position in the reference frame of a mechanical device, such as a wafer orientation stage For purposes of implementation, the order in which each transformation is performed determines their particular implementation, and is therefore important For example, it is relatively straightforward to generate a look-up table that implements a local image distortion correction transformation for each field of view that is to be applied first to an image, and to generate a look-up table that implements a subsequent local-to global coordmate transformation for each field of view In the alternative, although it is theoretically possible, a distorted local-to-distorted global 19 coordmate transform look-up table is relatively inefficient to generate, as it is also difficult to generate a look-up table that implements a global image distortion correction transformation that is to be applied last to each field of view of the distorted global image Referring to Fig 9, at run-time, an image of a different portion of an object is acquired from each of a plurality of cameras (52) The object can be πgidly mounted, such as by using a vacuum chuck, pneumatic toe clamp, or vice, or can be moving rapidly by the cameras, and instantaneously imaged using a strobe light Next, feature points of interest, such as points on the perimeter ol a semiconductor wafer. are found (54 ) using any technique for finding features in an image Next, all of the pixels in each field of view are corrected for camera distortion (56) using the distortion correction maps discussed above In a preferred embodiment, to improve computational efficiency, only pixels associated with feature points of interest are corrected Then, the corrected local coordinates of the feature points are transformed mto global coordinates (58) The global coordmates can be the real physical coordinates, or other coordinates adapted for further processing Other modifications and implementations will occur to those skilled in the art without departing from the spint and the scope of the invention as claimed Accordingly, the above descπption is not intended to limit the invention except as indicated in the following claims 20

Claims: CLAIMS What is claimed is: 1. An article of manufacture both for coordinating multiple fields of view of a plurality of cameras so as to facilitate determining the distance between features of a semiconductor wafer, each feature disposed within a different field of view, and for correcting image distortion in each field of view, the article comprising: a substantially rigid dimensionally-stable substrate having a plurality of image distortion calibration targets, each of said image distortion calibration targets for correcting image distortion within a field of view, and being disposed at a known relative position, so as to facilitate determining the distance between features of a semiconductor wafer each disposed within a different field of view. 2. The article of claim 1 , wherein each calibration target includes a feature that indicates an origin of the calibration target. 3. The article of claim 1, wherein said substantially rigid dimensionally-stable substrate includes at least one planar surface, each planar surface including at least one calibration target. 21 4 The article of claim 1 , wherein each calibration target is disposed at a position on said surface so as to facilitate direct viewing by a camera. 5 The article of claim 1 , wherein said substantially rigid dimensionally-stable substrate mcludes a substantially planar surface having a plurality of calibration targets. 6 The article of claim 1, wherein said substrate includes a calibration target for each possible camera location 7 The article of claim 1 wherein said substrate includes markings that communicate relative displacement information regarding at least the linear displacement of each said calibration target with respect to each other said calibration target. 8 The article of claim 1 wherein said substrate includes markings that communicate relative displacement information regarding both the linear displacement and relative angular displacement of each said calibration target with respect to each other said calibration target 9 The article of claim 1, wherein each calibration target is disposed on said substrate so said calibration target is in the field of view of a camera positioned to include a portion of the edge of the semiconductor wafer at run-time 10. The article of claim 1, wherem each calibration target is disposed at an oπentation angle such that a first pπnciple axis of said calibration target is perpendicular to a tangent to the peπmeter of said semiconductor wafer 1 1 The article of claim 1 , wherein a surface of the substrate is of a size and shape such that each calibration target disposed on the substrate can be in the field of view of a camera positioned to include a portion of the edge of said semiconductor wafer at run-time 12 The article of claim 1 , wherein the surtace of the substrate is ot a size that is larger than the major surface of the semiconductor wafer such that each calibration target disposed on the substrate can extend across a portion of the edge of the semiconductor wafer when it is in position at run-time 13 The article of claim 1. wherein the surface of the substrate is of a size that is larger than the major surface of the semiconductor wafer such that each calibration target disposed on the substrate can extend across a portion of the edge of the semiconductor wafer in position at run¬ time, and the surface of the substrate is sufficiently large that misalignment of the wafer with respect to the position of the plate at train-time can be accommodated 14. The article of claim 1 , wherein the substrate includes physical alignment features that facilitate coordination of the multi-camera coordinate system of the article of manufacture of the invention with the coordinate system of a semiconductor manufacturing machine. 15. The article of claim 14, wherein said physical alignment features include one of a notch and a flat formed anywhere on the perimeter of a surface of the substrate. 16. The article of claim 14, wherein said physical alignment features include one of a pair of holes sized to receive a pair of pegs that project from the stage of a semiconductor processing machine, and a pair of pegs sized to be received by a pair of holes formed in the stage of a semiconductor processing machine. 17. An article of manufacture both for coordinating multiple fields of view of a plurality of cameras so as to facilitate determining the distance between features of a semiconductor wafer each disposed within a different field of view, and for correcting image distortion in each field of view, the apparatus comprising: a substantially rigid dimensionally-stable substrate having a regular array of features, said features being sized such that a plurality of said features can fit within a field of view, said array being of a spatial extent such that a plurality of fields of view are substantially filled with said features, and said regular array of features being characterized by a distribution density suitable for correcting image distortion within a field of view; and 24 a plurality of landmarks disposed at a plurality of possible camera positions, said possible camera positions being of known relative position, so as to facilitate determining the distance between features on a semiconductor wafer, each feature disposed within a different field of view. 18. The article of claim 17 wherein said substrate includes markings that communicate relative displacement information regarding at least the linear displacement of each said landmark with respect to each other said landmark. 19. The article of claim 17 wherein said substrate includes markings that communicate relative displacement information regarding both the linear displacement and relative angular displacement of each said landmark with respect to each other said landmark.

Inventors: MICHAEL DAVID, WALLACK AARON, MICHAEL, DAVID, WALLACK, AARON
Applicants: COGNEX CORP, COGNEX CORPORATION
Application year: 1996
Main classification: G01J1
IPC codes: G01B11, G01N21, G03F7, G03F9, H01L21