(19)
(11)EP 1 424 595 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
04.03.2009 Bulletin 2009/10

(21)Application number: 03024839.7

(22)Date of filing:  31.10.2003
(51)International Patent Classification (IPC): 
G03F 1/00(2006.01)
G03F 1/14(2006.01)

(54)

Automatic calibration of a masking process simulator

Automatische Kalibrierung eines Maskensimulators

Calibrage automatique d'un simulateur de masques


(84)Designated Contracting States:
DE GB NL

(30)Priority: 26.11.2002 US 305673

(43)Date of publication of application:
02.06.2004 Bulletin 2004/23

(73)Proprietor: LSI Logic Corporation
Milpitas, CA 95035 (US)

(72)Inventors:
  • Ivanovic, Lav D.
    Cupertino CA 95014 (US)
  • Filseth, Paul G.
    Los Gatos CA 95033 (US)
  • Garza, Mario
    Sunnyvale CA 94088 (US)

(74)Representative: Holmes, Miles Keeton et al
First Thought IP 35 New Broad Street House New Broad Street
London EC2M 1NH
London EC2M 1NH (GB)


(56)References cited: : 
EP-A- 1 329 771
US-A- 6 081 659
WO-A-01/51993
  
      
    Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


    Description

    FIELD OF THE INVENTION



    [0001] The present invention relates to the field of semiconductor processing and more particularly to an improved process for automatically calibrating a masking process simulator.

    BACKGROUND OF THE INVENTION



    [0002] An integrated circuit is fabricated by translating a circuit design or layout to a semiconductor substrate. In optical lithography, the layout is first transferred onto a physical template, which is in turn used to optically project the layout onto a silicon wafer. In transferring the layout to a physical template, a mask is generally created for each layer of the integrated circuit design. The patterned photomask includes transparent, attenuated phase shifted, phase shifted, and opaque areas for selectively exposing regions of the photoresist-coated wafer to an energy source. To fabricate a particular layer of the design, the corresponding mask is placed over the wafer and a stepper or scanner machine shines a light through the mask from the energy source. The end result is a semiconductor wafer coated with a photoresist layer having the desired pattern that defines the geometries, features, lines and shapes of that layer. The photolithography process is typically followed by an etch process during which the underlying substrate not covered or masked by the photoresist pattern is etched away, leaving the desired pattern in the substrate. This process is then repeated for each layer of the design.

    [0003] Ideally, the photoresist pattern produced by the photolithography process and the substrate pattern produced by the subsequent etch process would precisely duplicate the pattern on the photomask. For a variety of reasons, however, the photoresist pattern remaining after the resist develop step may vary from the pattern of the photomask significantly. Diffraction effects and variations in the photolithography process parameters typically result in critical dimension (CD) variation from line to line depending upon the line pitch of the surrounding environment (where pitch is defined for purposes of this disclosure as the displacement between an adjacent pair of interconnect lines). In addition to CD variation, fringing effects and other process variations can result in end-of-line effects (in which the terminal end of an interconnect line in the pattern is shortened or cut off by the photolithography process) and corner rounding (in which square angles in the photomask translate into rounded corners in the pattern). These three primary optical proximity effects, together with other photoresist phenomena such as notching, combine to undesirably produce a patterned photoresist layer that may vary significantly from the pattern of the photomask. In addition to variations introduced during the photolithography process, further variations and distortions are typically introduced during the subsequent etch process such that the pattern produced in the semiconductor substrate may vary from the photomask pattern even more than the photoresist pattern.

    [0004] Conventional semiconductor process engineering in the areas of photolithography and etch typically attempts to obtain a finished pattern that approximates the desired pattern as closely as possible by controllably altering the process parameters associated with the various masking steps. Among the parameters process engineers typically attempt to vary in an effort to produce a photoresist pattern substantially identical to the photomask pattern include the intensity, coherency and wave length of the energy source, the type of photoresist, the temperature at which the photoresist is heated prior to exposure (pre-bake), the dose (intensity x time) of the exposing energy, the numerical aperture of the lens used in the optical aligner, the use of antireflective coatings, the develop time, developer concentration, developer temperature, developer agitation method, post bake temperature, and a variety of other parameters associated with the photolithography process. Etch parameters subject to variation may include, for example, process pressure and temperature, concentration and composition of the etch species, and the application of a radio frequency energy field within the etch chamber.

    [0005] Despite their best efforts, however, semiconductor process engineers are typically unable to manipulate the photolithography and etch processes such that the photoresist and substrate patterns produced by the processes are substantially identical to the photomask pattern.

    [0006] To avoid the time and cost of producing actual test wafers for every desired permutation of process parameters, computerized simulation of masking processes is employed to facilitate the optimization of a particular masking sequence and the generation of an optical proximity correction (OPC) distorted photomask. Masking process simulators receive various inputs corresponding to the parameters of the photoresist and etch processes to be simulated and attempt to simulate the pattern that will be produced by the specified masking process given a particular photomask. Accordingly, computerization has significantly enhanced the process engineer's ability to characterize and optimize masking processes.

    [0007] Nevertheless, it is typically impossible to adequately account for the multitude of parameters associated with a masking process despite the effort devoted to masking process characterization, the introduction of optical proximity correction techniques, and the emergence of sophisticated process simulation software. In other words, simulation programs are ultimately unable to account for the various parametric dependencies in a manner sufficient to predict the exact pattern that will be produced by any particular masking process and mask.

    [0008] US-A-6081659 describes a method of simulating a masking process in which a process simulator is used to produce an aerial image. The simulator is configured to receive input information. The input information includes a digital representation of a patterned mask and a data set. Each element of the data set corresponds to one of a plurality of parameters associated with the masking process. The simulator is configured to produce an aerial image based upon the input information. The aerial image represents the simulator estimation of a pattern that would be produced by the masking process using the patterned mask under conditions specified by the data set. The method further includes the step of supplying the input information to the simulator to produce the aerial image. A first data base is then generated from the aerial image. The first data base is a digital representation of the aerial image. Thereafter, the pattern is produced on a semiconductor substrate using the masking process and the patterned mask. The pattern is produced under the conditions specified by the data set. A second data base is then generated wherein the second data base is a digital representation of the actual pattern. The first data base and the second data base are then compared to produce an error data base. The error data base is indicative of differences between the aerial image and the pattern. Thereafter, the process simulator is modified based upon the error data base to minimize the differences between a successive iteration of the aerial image and the pattern. This patent contemplates that a statistician, a mathematician, or other skilled person knowledgeable in the field of process simulators and familiar with the photoresist simulation routine employed by the simulator will be able to beneficially modify the simulator with the assistance of a skilled software engineer, based on the information contained in the error database, to produce a modified simulation routine that more accurately predicts and simulates the pattern such that a subsequently executed iteration of the simulator will produce a modified aerial image that more accurately approximates the physically produced wafer.

    [0009] Accordingly, what is needed is a method and system for automatically improving the prediction accuracy of masking process simulator software. The present invention addresses such a need.

    SUMMARY OF THE INVENTION



    [0010] The present invention is defined in the claims. The present invention provides a method and system for improving the prediction accuracy of masking process simulators through automatic calibration of the simulators. The method and system include performing a masking process using a calibration mask and process parameters to produce a calibration pattern on a wafer A digital image is created of the calibration pattern, and the edges of the pattern are detected from the digital image using pattern recognition. Data defining the calibration mask and the process parameters are then input to a process simulator to produce an alim image estimating the calibration pattern that would be produced by the masking process. The method and system further include overlaying the alim image and the detected edges of the digital image, and measuring a distance between contours of the pattern in the alim image and the detected edges. Thereafter, one or more mathematical algorithms are used to iteratively change the values of the processing parameters input to the simulator until a set of processing parameter values are found that produces a minimum distance between the contours of the pattern in the alim image and the detected edges.

    [0011] According to the method and system disclosed herein, the calibration effectively calibrates the process simulator to compensate for process variations of the masking process. Once the calibration is performed and actual mask data and the modified process parameters are input to the process simulator, the process simulator will produce an image that varies minimally from the actual pattern produced by the masking process.

    BRIEF DESCRIPTION OF THE DRAWINGS



    [0012] 

    FIG. 1 is a diagram showing a portion of a desired semiconductor pattern and the patterned layer resulting from the masking process.

    FIG. 2 is a flow chart illustrating a process for calibrating a process simulator to compensate for process variations of the masking process in accordance with a preferred embodiment of the present invention.

    FIG. 3 is a block diagram of a web-enabled process simulation system in a preferred embodiment of the present invention.

    FIG. 4 is an illustration of an example calibration mask pattern.

    FIG. 5 is an illustration of an example SEM image produced by the masking process using the mask design shown in FIG. 4.

    FIG. 6 is a diagram showing an alim image superimposed with the detected SEM edges.

    FIG. 7 is a diagram illustrating a user interface screen produced by the calibration program in a preferred embodiment of the present invention.


    DETAILED DESCRIPTION OF THE INVENTION



    [0013] The present invention relates to simulating semiconductor fabrication processes and a method for improving process simulators through automatic calibration. The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications to the preferred embodiments and the generic principles and features described herein will be readily apparent to those skilled in the art. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein.

    [0014] Referring now to FIG. 1 a portion of a desired semiconductor pattern and the patterned layer resulting from the masking process is shown. The semiconductor pattern shown in dashed lines includes various pattern elements 102a, and 102b (collectively referred to as pattern elements 102). Using the pattern, a masking process is used to create the patterned layer 131, comprising the actual elements 132. The patterned layer 131 may comprise, in alternative embodiments, a photoresist pattern produced by a photolithography process or a substrate pattern produced by an etch process.

    [0015] As will be appreciated to those skilled in the art of semiconductor processing and design, elements 102 of semiconductor pattern includes various interconnect sections and pattern elements designed to achieve a desired function when the integrated circuit contemplated by the semiconductor fabrication process is completed. Typical elements 102 of a semiconductor pattern are substantially comprised of straight lines and square corners. For a variety of reasons, reproducing the exact image of semiconductor pattern in a production process is extremely complicated due to the large number of parameters associated with typical masking processes and further due to the unavoidable diffraction effects which inevitably result in some variations between the photomask used to produce a pattern and the pattern itself.

    [0016] It is seen in FIG. 1 that the actual pattern 131 produced by a masking process varies from the desired semiconductor pattern 102. This discrepancy is shown in FIG. 1 as the displacement between the dashed lines of pattern elements 102a and 102b and the actual pattern elements 132a and 132b. Typically, the variations from the idealized pattern 102 include rounding of the corners and a shrinking of the line widths. It will be appreciated to those skilled in the art of semiconductor processing that variations from the desired semiconductor pattern can contribute to lower processing yields, reduced reliability, reduced tolerance to subsequent misalignment, and other undesired effects.

    [0017] As is well-known in the art, commercial masking process simulation software is available that is capable of producing a simulated estimate of the pattern that would be produced by a specified masking process using a given photomask. Examples of process simulation software include TSUPREM-4™ and Taurus-LRC™ by Synopsys, Inc. of Mountain View, California. Masking process simulators are useful for generating a large quantity of information concerning the effects of modifying various parameters associated with the process. Simulation is necessary to avoid the time and expense associated with producing actual test wafers for each proposed parameter modification.

    [0018] Ultimately, the simulator will produce an estimate of the pattern, referred to as an aerial or latent image, that varies from the actual pattern produced by the masking process (due to diffraction effects and variations in the masking process) regardless of the number of parameters incorporated into the simulator.

    [0019] The Assignee of the present application has developed a process that improves the process simulator's prediction of the final pattern produced by a masking process by using the actual results obtained generated by the masking process. For example, U.S. Patent Nos. 6,078,738 and 6,081,659, discloses a process that introduces a feedback mechanism into the simulation process whereby the discrepancies observed between the actual pattern and the aerial image are analyzed to produce a modified simulator that results in less discrepancy, or error between the aerial image produced during a successive iteration of the simulator and the actual image produced by the pattern.

    [0020] Using the actual results obtained by the masking process to improve the prediction accuracy of the process simulator program can be referred to as a calibration process. However, how the calibration is implemented, including how the simulator is modified based on the calibration, can significantly affect the performance of the simulator.

    [0021] One approach for modifying the simulator during the calibration is a manual process whereby an operator iteratively changes the process parameter values input to the simulator by hand until the simulator achieves a desired level of performance. It is difficult, however, for the operator to change more than a couple of the processing parameter at a time, making the process tedious, error prone, and time-consuming.

    [0022] Another calibration approach uses critical dimension checking whereby a critical dimension of a particular feature of the actual pattern produced by the masking process is measured directly from a production wafer. The same critical distance is also measured across the feature in the aerial image produced by simulator. The processing parameters input to the simulator are then changed using an exhaustive search algorithm until the simulator produces an aerial image that has a critical distance equal to that of the actual pattern. One disadvantage of this method is that the critical dimension typically measures a feature in one dimension only, typically horizontally or vertically across the middle of the feature. The process, therefore, is incapable of analyzing the pattern in areas where most stepper errors occur, such as the ends of lines and the spaces between features.

    [0023] Accordingly, the present invention provides an improved process for analyzing the difference between the aerial image produced by a simulator and the actual pattern produced by the masking process in order to provide an improved method for calibrating the simulator. According to the present invention, calibration mask data and process parameters are input to a process simulator to produce an aerial image estimating the calibration pattern that would be produced by a masking process. The same calibration mask data and process parameters are used during the masking process to produce an actual calibration pattern on a wafer. A digital image of the actual pattern on the wafer is obtained, preferably using a scanning electron microscope (SEM). The edges of the pattern are automatically recognized from the SEM image using pattern recognition, and the recognized edges of the actual pattern are superimposed with the pattern in the aerial image. The distance between the contours of the pattern in the aerial image and the countours of the SEM edges is then measured, providing a distance value that is based on two dimensions, rather than one. One or more mathematical algorithms are then used to iteratively change the values of processing parameters input to the simulator until a set of processing parameter values are found that produces a minimum distance between the aerial image contours and SEM edges. This new set of parameters effectively calibrate the process simulator to compensate for process variations of the masking process.

    [0024] Once the calibration is performed and an operator inputs actual mask data and the modified process parameters into the process simulator, the process simulator will produce an aerial image that varies minimally from the actual pattern produced by the masking process. The calibrated process simulator may be used for a variety of tasks including predicting mask defects, as a model for OPC correction, for phase shifting mask correction, and so on.

    [0025] FIG. 2 is a flow chart illustrating a process for calibrating a process simulator to compensate for process variations of the masking process in accordance with a preferred embodiment of the present invention. The process begins in step 50 by providing a process simulation program that operates in accordance with the present invention on a server, and making the program available over a network, such as the Internet.

    [0026] FIG. 3 is a block diagram of a web-enabled process simulation system in a preferred embodiment of the present invention. The simulation system 140 includes a process simulator 142 and an automatic calibration program 143 for calibrating the process simulator 142. The process simulator 142 and the automatic calibration program 143 are executed on a server 144 as application programs and accessed over a network 146 by one or more operators using client computers 150. The automatic calibration program 143 may be included as part of, or separate from the process simulator 142.

    [0027] The process simulator 142 and calibration program 143 are capable of accessing one or more mask layout databases 152, each of which includes a set of mask designs that will be used to fabricate a particular semiconductor device. In particular, the calibration process 143 typically accesses a calibration mask design (not shown) when calibrating the process simulator 142. The process simulation system 140 also includes a data set 154 defining the input processing parameters, as described below. FIG. 4 is an illustration of an example calibration mask pattern from the mask layout database 152. In a preferred embodiment, mask data is stored in GDSII format.

    [0028] Referring again to FIG. 2, a calibration pattern is fabricated on a wafer by a masking process in step 52 whereby a physical calibration mask and a stepper machine are used to generate the calibration pattern under the conditions specified by the data set 154. The data set 154 includes global processing parameters that are associated with the masking process. In a preferred embodiment, the global processing parameters include both resist parameters for simulating the photoresist, and optical parameters for simulating the optics and characteristics of stepper machine.

    [0029] As is well known to those skilled in the field of photolithography engineering, examples of resist parameters include resist contrast (gamma), resist thickness, resist sensitivity, resist solids content, and resist viscosity. Examples of the optical parameters that may affect the resist image include the intensity of the stepper lamp, the duration of the exposure, the coherency of the optical energy, the aperture of lenses, and the wavelength of the lamp source. It will be further appreciated to those skilled in the art, that the develop process and the etch process both include a number of parameters that may also be input to the process simulator 142, such as develop time, developer concentration, developer temperature, developer agitation method, and any post bake time and temperature. Etch parameters may include, for example, etch temperature, etch pressure, and etchant composition and concentration. The process parameters described above are meant to be illustrative rather than exhaustive and additional parameters may be incorporated into the simulator 142.

    [0030] After the physical calibration pattern is fabricated by the masking process, a scanning electron microscope (SEM) is used to create a digital representation of the pattern, referred to herein as an SEM image in step 54. FIG. 5 is an illustration of an example SEM image produced by the masking process using the mask design shown in FIG. 4.

    [0031] Referring again to FIG. 2, according to one aspect of the present invention, in step 56, the edges of the mask pattern in the SEM image are automatically detected using pattern recognition. The detected edges may be stored in an edge database in a standard format, such as GDSII (a standard file format for transferring/archiving to the graphic design data). In one preferred embodiment, an algorithm, referred to as a Snake Algorithm, is used to automatically detect the mask edges from the SEM image, as it is disclosed in US 2004/057610 entitled "Mask Defect Analysis for Both Horizontal and Vertical Processing Effects". Alternatively, an "Adaptive SEM Edge Recognition Algorithm" may also be used to detect the edges, as disclosed in US 2004/0120578 entitled "Adaptive SEM Edge Recognition Algorithm."

    [0032] In step 58, the SEM image is correlated to the GDS mask design data layout database 152 in order to determine how many pixels in the SEM image are equal to one unit of measure of the mask design, which is typically nanometers.

    [0033] In step 60, an operator of a client computer 150 invokes the calibration program 143. In step 62, the operator selects the calibration mask design and the data set 154 representing global processing parameters of the masking process that were used to fabricate a calibration pattern.

    [0034] In step 64, the calibration mask data and process parameters are input to the process simulator 142 to produce an image estimating the calibration pattern that would be produced by a masking process. As is well-known in the art, an aerial or latent image may be produced by the simulator, which are collectively referred to herein as an "alim" image (aerial/latent image). The alim images generated by the process simulator 142 may be stored either on a server, or on the client computer 150.

    [0035] In step 66, the alim image, the calibration mask design, and the detected SEM edges are overlaid. In step 68, the alignment between the alim image, the calibration mask, and the detected SEM edges are refined. In a preferred embodiment, the alim image and the pattern in the SEM image may include corresponding alignment marks to facilitate a subsequent alignment and comparison. The overlaid images may optionally be displayed to the operator. FIG. 6 is a diagram showing an example alim image 164, shown with white lines, superimposed with the detected SEM edges, shown with dark lines.

    [0036] Referring again to FIG. 2, in step 70, the distance between the alim image contours and the detected SEM edges are determined. In a preferred embodiment, this distance is determined using a root mean square (RMS) algorithm. The RMS algorithm measures the distance between each pair of corresponding edges in the alim image 164 and the SEM image 166 (or a subset of the edges) and applies a weighted average to the measured distances to produce a single distance value. In another preferred embodiment, the weighted average is equal to an Nth root of an average Nth power of distance between the SEM edges and the alim image for some N not necessarily equal to 2. Calculating distances between the contours in this manner effectively provides a distance value that is based on two-dimensional measurements.

    [0037] In step 72, one or more mathematical algorithms are used to search for a set of processing parameter values for input to the simulator that will produce the minimum distance between the alim image contours and the SEM edges. The operator may also define a minimum distance threshold that will be used to terminate the search, and the minimum and maximum possible values for the processing parameters.

    [0038] In a preferred embodiment, a subset of the processing parameters used by the masking process are input to the mathematical algorithm. According to the present invention, the following 11 processing parameters are used to determine the minimum distance: focus, diffusion, sigma in, sigma out, angle of the pole location, numerical aperture, sigma of the pole, spherical, coma_x, coma_y, and intenstity contour.

    [0039] In step 74, it is determined if the calculated distance between the alim image contours and the SEM edges meets the minimum distance threshold set by the operator. The minimum distance threshold is dependent upon the particular process technology being used. For a 130 nm process technology, for example, the minimum distance threshold may be set at 8-10 nm, which means that the process simulator must produce an alim image 164 that is within 10% of an SEM image 166. For critical applications, an error threshold of 5% or less may be necessary.

    [0040] If the calculated distance does not meet the minimum distance threshold, then the algorithm calculates new values for the processing parameters in step 76. The new processing parameter values are calculated during the process of minimizing the distance between the alim image contours and the SEM edge contours given a function (f) of the 11 variables (x):

            f(x1,...,x11)/R11-R



    [0041] In a preferred embodiment, two algorithms are employed to minimize this equation. First, a well-known stochastic algorithm is used to iteratively change the processing values until a global minimum for the function is found. This first set of calculated parameter values that produce the global minimum are then input to a second well-know algorithm, referred to as a simplex or Powell algorithm. This algorithm begins with the function defined by this set of parameter values and iteratively changes the values of the parameters until local minimums within the function are found, producing a second set of parameter values.

    [0042] In step 78, this second set of calculated parameter values are input to the process simulator 142 to generate a new alim image 164, and the process continues with steps 66-72. The alim image 164 is overlaid with the SEM edges and the distance between the two are calculated, etc. If the calculated distance between the alim image contours and the SEM edge contours does not meet the minimum distance threshold in step 74, then the process continues. If the calculated distance between the alim image contours and the SEM edge contours meets the minimum distance threshold in step 74, then in step 80 the current set of parameter values are the optimal set of parameters and are output by calibration program 143 for calibration of the process simulator 142.

    [0043] FIG. 7 is a diagram illustrating a user interface screen produced by the calibration program in a preferred embodiment of the present invention. In a further aspect of the present invention, the calibration program user interface screen 170 displays individual graphs 172 for each processing parameter that plot the parameter values for each iteration along the x-axis, and the resulting RMS distance value along the y-axis. In addition to the individual parameter graphs 172, the user interface screen also displays a global graph 174 plotting the global RMS distance result of each iteration.

    [0044] A method and system for calibrating a process simulator have been disclosed. The present invention has been described in accordance with the embodiments shown, and one of ordinary skill in the art will readily recognize that there could be variations to the embodiments, and any variations would be within the scope of the present invention as defined in the appended claims.


    Claims

    1. A computer-implemented method for automatically calibrating a masking process simulator, comprising the steps of:

    (a) performing (52) a masking process using a calibration mask and process parameters to produce a calibration pattern on a wafer;

    (b) creating (54) a digital image of the calibration pattern;

    (c) detecting (56) edges of the pattern from the digital image using pattern recognition;

    (d) inputting (64) data defining the calibration mask and the process parameters into a process simulator to produce an aerial or latent image estimating the calibration pattern that would be produced by the masking process;

    (e) overlaying (66) the aerial or latent image and the detected edges of the digital image;

    (f) measuring (70) a distance between contours of the pattern in the aerial or latent image and the detected edges; and

    (g) using (72) one or more mathematical algorithms to iteratively change at least a subset of the values of the processing parameters input to the simulator until a set of processing parameter values are found that produces a minimum distance between the contours of the pattern in the aerial or latent image and the detected edges, thereby effectively calibrating the process simulator to compensate for process variations of the masking process.


     
    2. The method of claim 1 wherein step (b) further includes the step of: using (54) a scanning electron microscope (SEM) to create an SEM image of the calibration pattern.
     
    3. The method of claim 1 or 2 wherein step (g) consists of: changing (72) the values of the subset of the processing parameters.
     
    4. The method of claim 3 wherein the subset of processing parameters includes focus, diffusion, sigma in, sigma out, angle of the pole location, numerical aperture, sigma of the pole, spherical, coma_x, coma_y, and intenstity contour.
     
    5. The method of any preceding claim wherein step (g) further includes the step of: receiving from an operator a minimum distance threshold that will be used to terminate the search, and the minimum and maximum possible values for the processing parameters.
     
    6. The method of claim 5 wherein step (g) further includes the steps of:

    (i) using a first algorithm to iteratively change the parameter values until a global minimum for a function of the processing parameters is found;

    (ii) inputting (76) a first set of calculated parameter values that produced the global minimum to a second algorithm, wherein the second algorithm begins with the function defined by the first set of parameter values and iteratively changes the values of the parameters until local minimums within the function are found, producing a second set of parameter values.


     
    7. The method of claim 6 wherein the first algorithm is a stochastic algorithm.
     
    8. The method of claim 6 or 7 wherein step (g) further includes the step of: iteratively inputting (78) the second set of calculated parameter values into the process simulator to generate new aerial or latent images for the distance measurement.
     
    9. The method of any preceding claim wherein step (c) further includes the step of: correlating (58) the SEM image to the mask design data in order to determine how many pixels in the SEM image are equal to one unit of measure of the mask design.
     
    10. The method of any preceding claim wherein step (f) further includes the step of: determining (70) the distance using a distance metric, including root mean square (RMS) algorithm.
     
    11. The method of claim 10 wherein the distance metric measures a distance between at least a subset of each pair of corresponding edges in the aerial or latent image and the SEM image and applies a weighted average to the measured distances to produce a single distance value.
     
    12. The method of claim 11 wherein the weighted average is equal to an Nth root of an average Nth power of distance between the SEM edges and the aerial or latent image.
     
    13. The method of any preceding claim wherein the aerial or latent image comprises an aerial image or a latent image.
     
    14. The method of any preceding claim further including the step of: displaying a user interface screen (172, 174) that displays individual graphs for each processing parameter that plot parameter values for each iteration along with resulting distance values, and displays a global graph plotting a global distance result of each iteration.
     
    15. The method of any preceding claim wherein step (a) further includes the step of: inputting (62) global processing parameters, which include both resist parameters for simulating photoresist and optical parameters for simulating optics and characteristics of a stepper machine.
     
    16. A process simulator system, comprising:

    a server (144) coupled to a network (146);

    a calibration program (143) executing on the server;

    a process simulator (142) executing on the server; and

    at least one client computer (150) coupled to the server over the network, such that an operator may access the calibration program, wherein once invoked, the calibration program:

    (a) receives a digital image of a calibration pattern on a wafer, the calibration pattern produced during a masking process using a calibration mask and process parameters

    (b) detects (56) edges of the pattern from the digital image using pattern recognition;

    (c) inputs (60-64) data defining the calibration mask and the process parameters into the process simulator, which then produces an aerial or latent image estimating the calibration pattern that would be produced by the masking process;

    (d) overlays (66) the aerial or latent image and the detected edges of the digital image;

    (e) measures (70) a distance between contours of the pattern in the aerial or latent image and the detected edges; and

    (f) uses (72) one or more mathematical algorithms to iteratively change at least a subset of the values of the processing parameters input to the simulator until a set of processing parameter values are found that produces a minimum distance between the contours of the pattern in the aerial or latent image and the detected edges, thereby effectively calibrating the process simulator to compensate for process variations of the masking process.


     
    17. The system of claim 16 wherein a scanning electron microscope (SEM) is used to create an SEM image of the calibration pattern.
     
    18. The system of claim 16 or 17 wherein the mathematical algorithms iteratively change the values of the subset of the processing parameters.
     
    19. The system of claim 18 wherein the subset of processing parameters includes focus, diffusion, sigma in, sigma out, angle of the pole location, numerical aperture, sigma of the pole, spherical, coma_x, coma_y, and intenstity contour.
     
    20. The system of claim 16, 17, 18 or 19 wherein the calibration program receives from the operator a minimum distance threshold that will be used to terminate the search by the mathematical algorithms, and the minimum and maximum possible values for the processing parameters.
     
    21. The system of claim 20 wherein the mathematical algorithms include

    (i) a first algorithm for iteratively changing the parameter values until a global minimum for a function of the processing parameters is found; and

    (ii) a second algorithm that receives a first set of calculated parameter values that produced the global minimum and begins with a function defined by the first set of parameter values, and iteratively changes the values of the parameters until local minimums within the function are found, producing a second set of parameter values.


     
    22. The system of claim 21 wherein the first algorithm is a stochastic algorithm.
     
    23. The system of claim 21 or 22 wherein the calibration program iteratively inputs the second set of calculated parameter values into the process simulator to generate new aerial or latent images for the distance measurement.
     
    24. The system of claim 17 or any claim dependent thereon, wherein the SEM image is correlated to the mask design data in order to determine how many pixels in the SEM image are equal to one unit of measure of the mask design.
     
    25. The system of claim 16 or any claim dependent thereon, wherein the distance is determined using a distance metric, including a root mean square (RMS) algorithm.
     
    26. The system of claim 25 wherein the distance metric measures a distance between at least a subset of each pair of corresponding edges in the aerial or latent image and the SEM image, and applies a weighted average to the measured distances to produce a single distance value.
     
    27. The system of claim 26 wherein the weighted average is equal to an Nth root of an average Nth power of distance between the SEM edges and the aerial or latent image.
     
    28. The system of claim 16 or any claim dependent thereon, wherein the aerial or latent image comprises an aerial image or a latent image.
     
    29. The system of claim 16 or any claim dependent thereon, wherein the calibration program displays a user interface screen that displays individual graphs for each processing parameter that plot parameter values for each iteration along with resulting distance values, and displays a global graph plotting a global distance result of each iteration.
     
    30. The system of claim 16 or any claim dependent thereon wherein processing parameters input to the process simulator comprise global processing parameters, which include both resist parameters for simulating photoresist and optical parameters for simulating optics and characteristics of a stepper machine.
     


    Ansprüche

    1. Computer-implementiertes Verfahren zum automatischen Kalibieren eines Maskierungsprozesssimulators, mit den Schritten:

    (a) Durchführen (52) eines Maskierungsprozesses unter Verwendung einer Kalibrierungsmaske und Prozessparametern, um ein Kalibrierungsmuster auf einem Wafer zu erzeugen;

    (b) Erzeugen (54) eines digitalen Bildes des Kalibrierungsmusters;

    (c) Erfassen (56) von Kanten des Musters von dem digitalen Bild unter Verwendung einer Mustererkennung;

    (d) Eingeben (64) von Daten, die die Kalibrierungsmaske definieren, und der Prozessparameter in einen Prozesssimulator, um ein virtuelles oder latentes Bildes zu erzeugen, das das Kalibrierungsmuster, das durch den Maskierungsprozess erzeugt werden würde, schätzt;

    (e) Überlagern (66) des virtuellen oder latenten Bildes und der erfassten Kanten des digitalen Bildes;

    (f) Messen (70) einer Distanz zwischen Konturen des Musters in dem virtuellen oder latenten Bild und den erfassten Kanten; und

    (g) Verwenden (72) eines oder mehrerer mathematischer Algorithmen, um zumindest einen Teilsatz der Werte der Verarbeitungsparameter, die in den Simulator eingegeben werden, iterativ zu ändern, bis ein Satz von Verarbeitungsparameterwerten gefunden wird, der eine minimale Distanz zwischen den Konturen des Musters in dem virtuellen oder latenten Bild und den erfassten Kanten erzeugt, wodurch der Prozesssimulator effektiv kalibriert wird, um Prozessschwankungen des Maskierungsprozesses zu kompensieren.


     
    2. Verfahren gemäß Anspruch 1, wobei Schritt (b) weiterhin den Schritt umfasst:

    Verwenden (54) eines Rasterelektronenmikroskops (SEM), um ein SEM-Bild des Kalibrierungsmusters zu erzeugen.


     
    3. Verfahren gemäß Anspruch 1 oder 2, wobei Schritt (g) besteht aus: Ändern (72) der Werte des Teilsatzes der Verarbeitungsparameter.
     
    4. Verfahren gemäß Anspruch 3, wobei der Teilsatz von Verarbeitungsparametern Brennpunkt, Streuung, Eingangs-Sigma, Ausgangs-Sigma, Winkel des Polorts, numerische Apertur, Sigma des Pols, sphärisch, Koma_x, Koma_y und Intensitätskontur umfasst.
     
    5. Verfahren gemäß einem der vorstehenden Ansprüche, wobei Schritt (g) weiterhin den Schritt umfasst:

    Empfangen eines minimalen Distanzschwellenwerts, der verwendet wird, um die Suche zu beenden, und der minimal und der maximal möglichen Werte für die Verarbeitungsparameter von einem Bediener.


     
    6. Verfahren gemäß Anspruch 5, wobei Schritt (g) weiterhin die Schritte umfasst:

    (i) Verwenden eines ersten Algorithmus, um die Parameterwerte iterativ zu ändern, bis ein globales Minimum für eine Funktion der Verarbeitungsparameter gefunden ist;

    (ii) Eingeben (76) eines ersten Satzes von berechneten Parameterwerten, der das globale Minimum erzeugt hat, in einen zweiten Algorithmus, wobei der zweite Algorithmus mit der Funktion beginnt, die durch den ersten Satz von Parameterwerten definiert ist, und iterativ die Werte der Parameter ändert, bis lokale Minima innerhalb der Funktion gefunden sind, wobei ein zweiter Satz von Parameterwerten erzeugt wird.


     
    7. Verfahren gemäß Anspruch 6, wobei der erste Algorithmus ein stochastischer Algorithmus ist.
     
    8. Verfahren gemäß Anspruch 6 oder 7, wobei Schritt (g) weiterhin den Schritt umfasst: iteratives Eingeben (78) des zweiten Satzes von berechneten Parameterwerten in den Prozesssimulator, um neue virtuelle oder latente Bilder für die Distanzmessung zu erzeugen.
     
    9. Verfahren gemäß einen der vorstehenden Ansprüche, wobei Schritt (c) weiterhin den Schritt umfasst:

    Korrelieren (58) des SEM-Bildes zu den Maskenentwurfsdaten, um zu bestimmen, wie viele Bildelemente in dem SEM-Bild gleich einer Maßeinheit des Maskenentwurfs sind.


     
    10. Verfahren gemäß einem der vorstehenden Ansprüche, wobei Schritt (f) weiterhin den Schritt umfasst:

    Bestimmen (70) der Distanz unter Verwendung einer Distanzmetrik, die einen Effektivwert (RMS)-Algorithmus umfasst.


     
    11. Verfahren gemäß Anspruch 10, wobei die Distanzmetrik eine Distanz zwischen zumindest einem Teilsatz von jedem Paar von entsprechenden Kanten in dem virtuellen oder latenten Bild und dem SEM-Bild misst, und ein gewichtetes Mittel auf die gemessenen Distanzen anwendet, um einen einzelnen Distanzwert zu erzeugen.
     
    12. Verfahren gemäß Anspruch 11, wobei das gewichtete Mittel gleich einer N-ten Wurzel einer mittleren N-ten Potenz einer Distanz zwischen den SEM-Kanten und dem virtuellen oder latenten Bild ist.
     
    13. Verfahren gemäß einem der vorstehenden Ansprüche, wobei das virtuelle oder latente Bild ein virtuelles Bild oder latentes Bild umfasst.
     
    14. Verfahren gemäß einem der vorstehenden Ansprüche, weiterhin mit dem Schritt: Anzeigen eines Benutzerschnittstellenbildschirms (172, 174), der einzelne Graphen für jeden Verarbeitungsparameter anzeigt, die Parameterwerte für jede Iteration zusammen mit sich ergebenden Distanzwerten aufzeichnen und einen globalen Graph anzeigen, der ein globales Distanzergebnis jeder Iteration aufzeichnet.
     
    15. Verfahren gemäß einem der vorstehenden Ansprüche, wobei Schritt (a) weiterhin den Schritt umfasst: Eingeben (62) von globalen Verarbeitungsparametern, die sowohl Fotolackparameter zum Simulieren von Fotolack als auch optische Parameter zum Simulieren von Optiken und Charakteristika einer Schrittmaschine umfassen.
     
    16. Prozesssimulatorsystem, mit:

    einem Server (144), der mit einem Netzwerk (146) gekoppelt ist;

    einem Kalibrierungsprogramm (143), das auf dem Server ausgeführt wird;

    einen Prozesssimulator (142), der auf dem Server ausgeführt wird; und

    zumindest einem Client-Computer (150), der mit dem Server über das Netzwerk gekoppelt ist, so dass ein Bediener auf das Kalibrierungsprogramm zugreifen kann, wobei, sobald es einmal aufgerufen ist, das Kalibrierungsprogramm:

    (a) ein digitales Bild eines Kalibrierungsmusters auf einem Wafer empfängt, wobei das Kalibrierungsmuster während eines Maskierungsprozesses unter Verwendung einer Kalibrierungsmaske und Prozessparametern erzeugt wird;

    (b) Kanten des Musters von dem digitalen Bild unter Verwendung einer Mustererkennung erfasst (56);

    (c) Daten, die die Kalibrierungsmaske definieren, und die Prozessparameter in den Prozesssimulator eingibt (60 bis 64), welche dann ein virtuelles oder latentes Bild erzeugen, das das Kalibrierungsmuster schätzt, das durch den Maskierungsprozess erzeugt werden würde;

    (d) das virtuelle oder latente Bild und die erfassten Kanten des digitalen Bildes überlagert (66);

    (e) eine Distanz zwischen Konturen des Musters in dem virtuellen oder latenten Bild und den erfassten Kanten misst (70); und

    (f) einen oder mehrere mathematische Algorithmen verwendet (72), um zumindest einen Teilsatz der Werte der Verarbeitungsparameter, die in dem Simulator eingegeben werden, iterativ zu ändern, bis ein Satz von Verarbeitungsparameterwerten gefunden ist, der eine minimale Distanz zwischen den Konturen des Musters in dem virtuellen oder latenten Bild und der erfassten Kanten erzeugt, wodurch der Prozesssimulator effektiv kalibriert wird, um Prozessschwankungen des Markierungsprozesses zu kompensieren.


     
    17. System gemäß Anspruch 16, wobei ein Rasterelektronenmikrosop (SEM) verwendet wird, um ein SEM-Bild des Kalibrierungsmusters zu erzeugen.
     
    18. System gemäß Anspruch 16 oder 17, wobei die mathematischen Algorithmen iterativ die Werte des Teilsatzes der Verarbeitungsparameter ändern.
     
    19. System gemäß Anspruch 18, wobei der Teilsatz von Verarbeitungsparametern Brennpunkt, Streuung, Eingangs-Sigma, Ausgangs-Sigma, Winkel des Polorts, numerische Apertur, Sigma des Poles, sphärisch, Koma_x; Koma_y, und Intensitätskontur umfassen.
     
    20. System gemäß Anspruch 16, 17, 18 oder 19, wobei das Kalibrierungsprogramm von dem Bediener einen minimalen Distanzschwellenwert, der verwendet wird, um die Suche durch die mathematischen Algorithmen zu beenden, und die minimal und maximal möglichen Werte für die Verarbeitungsparameter empfängt.
     
    21. System gemäß Anspruch 20, wobei die mathematischen Algorithmen umfassen:

    (i) einen ersten Algorithmus zum iterativen Ändern der Parameterwerte, bis ein globales Minimum für eine Funktion der Verarbeitungsparameter gefunden wird; und

    (ii) einen zweiten Algorithmus, der einen ersten Satz von berechneten Parameterwerten empfängt, die das globale Minimum erzeugten, und mit einer Funktion beginnt, die durch den ersten Satz von Parameterwerten definiert ist, und iterativ die Werte der Parameter ändert, bis lokale Minima innerhalb der Funktion gefunden werden, wobei ein zweiter Satz von Parameterwerten erzeugt wird.


     
    22. System gemäß Anspruch 21, wobei der erste Algorithmus ein stochastischer Algorithmus ist.
     
    23. System gemäß Anspruch 21 oder 22, wobei das Kalibrierungsprogramm iterativ den zweiten Satz von berechneten Parameterwerten in den Prozesssimulator eingibt, um neue virtuelle oder latente Bilder für die Distanzmessung zu erzeugen.
     
    24. System gemäß Anspruch 17 oder einem davon abhängigen Anspruch, wobei das SEM-Bild mit den Maskenentwurfsdaten korreliert ist, um zu bestimmen, wie viele Bildelemente in dem SEM-Bild gleich einer Maßeinheit des Maskenentwurfs sind.
     
    25. System gemäß Anspruch 16 oder einem davon abhängigen Anspruch, wobei die Distanz unter Verwendung einer Distanzmetrik gemessen wird, die einen Effektivwert (RMS)-Algorithmus umfasst.
     
    26. System gemäß Anspruch 25, wobei die Distanzmetrik eine Distanz zwischen zumindest einem Teilsatz von jedem Paar von entsprechenden Kanten in dem virtuellen oder latenten Bild und dem SEM-Bild misst und ein gewichtetes Mittel auf die gemessenen Distanzen anwendet, um einen einzelnen Distanzwert zu erzeugen.
     
    27. System gemäß Anspruch 26, wobei das gewichtete Mittel gleich einer N-ten Wurzel einer mittleren N-ten Potenz einer Distanz zwischen den SEM-Kanten und dem virtuellen oder latenten Bild ist.
     
    28. System gemäß Anspruch 16 oder einem davon abhängigen Anspruch, wobei das virtuelle oder latente Bild ein virtuelles Bild oder ein latentes Bild aufweist.
     
    29. System gemäß Anspruch 16 oder einem davon abhängigen Anspruch, wobei das Kalibrierungsprogramm einen Benutzerschnittstellenbildschirm anzeigt, der einzelne Graphen für jeden Verarbeitungsparameter anzeigt, die Parameterwerte für jede Iteration zusammen mit sich ergebenen Distanzwerten aufzeichnen, und einen globalen Graphen anzeigt, der ein globales Distanzergebnis jeder Iteration aufzeichnet.
     
    30. System gemäß Anspruch 16 oder einem davon abhängigen Anspruch, wobei Verarbeitungsparameter, die in den Prozesssimulator eingegeben werden, globale Verarbeitungsparameter umfassen, welche sowohl Fotolackparameter zum Simulieren von Fotolack als auch optische Parameter zum Simulieren von Optiken und Charakteristika einer Schrittmaschine umfassen.
     


    Revendications

    1. Procédé implémenté par un ordinateur pour calibrer automatiquement un simulateur de traitement de masquage, comprenant les étapes de :

    (a) réalisation (52) d'un traitement de masquage en utilisant un masque de calibrage et des paramètres de traitement pour produire un motif de calibration sur une tranche ;

    (b) création (54) d'une image numérique du motif de calibrage ;

    (c) détection (56) des bords du motif à partir de l'image numérique en utilisant la reconnaissance de motifs ;

    (d) entrée (64) de données définissant le masque de calibrage et les paramètres de traitement dans un simulateur de traitement pour produire une image aérienne ou latente évaluant le motif de calibrage qui sera produit par le traitement de masquage ;

    (e) superposition (66) de l'image aérienne ou latente et les bords détectés de l'image numérique ;

    (f) mesure (70) d'une distance entre les contours du motif dans l'image aérienne ou latente et les bords détectés ; et

    (g) utilisation (72) d'un ou plusieurs algorithmes mathématiques pour changer de façon itérative au moins un sous-ensemble des valeurs des paramètres de traitement entrés dans le simulateur jusqu'à ce qu'un ensemble de valeurs de paramètres de traitement soit trouvé pour produire une distance minimum entre les contours du motif dans l'image aérienne ou latente et les bords détectés, calibrant efficacement de ce fait le simulateur de traitement pour compenser les variations de traitement du traitement de masquage.


     
    2. Procédé selon la revendication 1 dans lequel l'étape (b) comprend de plus l'étape due : utilisation (54) d'un microscope électronique à balayage (MEB) pour créer une image de MEB du motif de calibrage.
     
    3. Procédé selon la revendication 1 ou 2 dans lequel l'étape (g) consiste dans : le changement (72) des valeurs du sous-ensemble des paramètres de traitement.
     
    4. Procédé selon la revendication 3 dans lequel le sous-ensemble de paramètres de traitement comprend la mise au point, la diffusion, l'entrée sigma, la sortie sigma, l'angle de l'emplacement du pôle, l'ouverture numérique, le sigma du pôle, le sphérique, le coma_x, le coma_y, et le contour d'intensité.
     
    5. Procédé selon l'une quelconque des revendications précédentes dans lequel l'étape (g) comprend de plus l'étape de : réception depuis un opérateur d'un seuil de distance minimum qui est utilisé pour terminer la recherche, et les valeurs possibles minimum et maximum pour les paramètres de traitement.
     
    6. Procédé selon la revendication 5 dans lequel l'étape (g) comprend de plus les étapes de :

    (i) utilisation d'un premier algorithme pour changer de façon itérative les valeurs des paramètres jusqu'à un minimum commun pour qu'une fonction des paramètres de traitement soit trouvée ;

    (ii)entrée (76) d'un premier ensemble de valeurs de paramètres calculées qui a produit le minimum commun à un deuxième algorithme, dans lequel le deuxième algorithme commence avec la fonction définie au moyen du premier ensemble de valeurs de paramètres et change de façon itérative les valeurs des paramètres jusqu'à ce que des minimum locaux dans la fonction soient trouvés, produisant un deuxième ensemble de valeurs de paramètres.


     
    7. Procédé selon la revendication 6 dans lequel le premier algorithme est un algorithme stochastique.
     
    8. Procédé selon la revendication 6 ou 7 dans lequel l'étape (g) comprend de plus l'étape de : entrée de façon itérative (78) du deuxième ensemble de valeurs de paramètres calculées dans le simulateur de traitement pour générer de nouvelles images aériennes ou latentes pour la mesure de la distance.
     
    9. Procédé selon l'une quelconque des revendications précédentes dans lequel l'étape (c) comprend de plus l'étape de : corrélation (58) de l'image de MEB avec les données de conception du masque de façon à déterminer combien de pixels dans l'image de MEB sont égaux à une unité de mesure de conception du masque.
     
    10. Procédé selon l'une quelconque des revendications précédentes dans lequel l'étape (f) comprend l'étape de :

    détermination (70) de la distance en utilisant un dispositif de mesure de distance, comprenant un algorithme de moyenne quadratique (RMS).


     
    11. Procédé selon la revendication 10 dans lequel le dispositif de mesure de distance mesure une distance entre au moins un sous-ensemble de chaque paire de bords correspondants dans l'image aérienne ou latente et l'image de MEB et applique une moyenne pondérée aux distances mesurées pour produire une seule valeur de distance.
     
    12. Procédé selon la revendication 11 dans lequel la moyenne pondérée est égale à la racine nième d'une puissance nième moyenne de distance entre les bords MEB et l'image aérienne ou latente.
     
    13. Procédé selon l'une quelconque des revendications précédentes dans lequel l'image aérienne ou latente comprend une image aérienne ou une image latente.
     
    14. Procédé selon l'une quelconque des revendications précédentes comprenant l'étape de : affichage d'un écran d'interface utilisateur (172, 174) qui affiche les graphiques individuels pour chaque paramètre de traitement qui trace les valeurs de paramètres pour chaque itération en même temps que les valeurs des distances résultantes, et affiche un graphique commun traçant une distance commune résultat de chaque itération.
     
    15. Procédé selon l'une quelconque des revendications précédentes dans lequel l'étape (a) comprend de plus l'étape due : entrée (62) de paramètres de traitement globaux, qui comprennent à la fois des paramètres de réserve pour simuler une résine photosensible et des paramètres optiques pour simuler l'optique et les caractéristiques d'une machine pas à pas.
     
    16. Système simulateur de traitement, comprenant :

    un serveur (144) couplé à un réseau (146) ;

    un programme de calibrage (143) s'exécutant sur le serveur ; et

    au moins un ordinateur client (150) couplé au serveur sur le réseau, de telle façon qu'un opérateur puisse accéder au programme de calibrage, dans lequel une fois qu'il est invoqué, le programme de calibrage :

    (a) reçoit une image numérique d'un motif de calibrage sur une tranche, le motif de calibrage produit pendant un traitement de masquage utilisant un
    masque de calibrage et des paramètres de traitement

    (b) détecte (56) les bords du motif à partir de l'image numérique en utilisant une reconnaissance de motif ;

    (c) entre (60 à 64) des données définissant le masque de calibrage et les paramètres de traitement dans le simulateur de traitement, qui alors produit une image aérienne ou latente en évaluant le motif de calibrage qui serait produit par le traitement de masquage ;

    (d) recouvre (66) l'image aérienne ou latent et les bords détectés de l'image numérique ;

    (e) mesure (70) une distance entre les contours du motif dans l'image aérienne ou latente et les bords détectés ; et

    (f) utilise (72) un ou plusieurs algorithmes mathématiques pour changer de façon itérative au moins un sous-ensemble des valeurs des paramètres de traitement entrés dans le simulateur jusqu'à ce qu'un ensemble de valeurs de paramètres de traitement soit trouvé qui produise une distance minimum entre les contours du motif dans l'image aérienne ou latente et les bords détectés, calibrant de ce fait efficacement le simulateur de traitement pour compenser les variations de traitement du traitement de masquage.


     
    17. Système selon la revendication 16 dans lequel un microscope électronique à balayage (MEB) est utilisé pour créer une image de MEB du motif de calibrage.
     
    18. Système selon la revendication 16 ou 17 dans lequel les algorithmes mathématiques changent de façon itérative les valeurs du sous-ensemble des paramètres de traitement.
     
    19. Système selon la revendication 18 dans lequel le sous-ensemble comprend la mise au point, la diffusion, l'entrée sigma, la sortie sigma, l'angle de l'emplacement du pôle, l'ouverture numérique, le sigma du pôle, le sphérique, le coma_x, le coma_y, et le contour d'intensité.
     
    20. Système selon la revendication 16, 17, 18 ou 19 dans lequel le programme de calibrage reçoit de l'opérateur un seuil de distance minimum qui est utilisé pour terminer la recherche au moyen d'algorithmes mathématiques, et les valeurs possibles minimum et maximum pour les paramètres de traitement.
     
    21. Système selon la revendication 20 dans lequel les algorithmes mathématiques comprennent

    (i) un premier algorithme pour changer de façon itérative les valeurs de paramètres jusqu'à un minimum commun pour qu'une fonction des paramètres de traitement soit trouvée ; et

    (ii) un deuxième algorithme qui reçoit un premier ensemble de valeurs de paramètres calculées qui produisent le minimum commun et commencent une fonction définie par le premier ensemble de valeurs de paramètres, et change de façon itérative les valeurs des paramètres jusqu'à ce que des minimum locaux dans la fonction soient trouvés, produisant un deuxième ensemble de valeurs de paramètres.


     
    22. Système selon la revendication 21 dans lequel le premier algorithme est un algorithme stochastique.
     
    23. Système selon la revendication 21 ou 22 dans lequel le programme de calibrage entre de façon itérative le deuxième ensemble de valeurs de paramètres calculées dans le simulateur de traitement pour générer de nouvelles images aériennes ou latentes pour la mesure de distance.
     
    24. Système selon la revendication 17 ou de n'importe quelle revendication dépendante ici, dans lequel l'image de MEB est corrélée aux données de conception de masque de façon à déterminer combien de pixels dans l'image de MEB sont égaux à une unité de mesure de la conception du masque.
     
    25. Système selon la revendication 16 ou de n'importe quelle revendication dépendante ici, dans lequel la distance est déterminée en utilisant un dispositif de mesure de distance, comprenant un algorithme de moyenne quadratique (RMS).
     
    26. Système selon la revendication 25 dans lequel le dispositif de mesure de distance mesure une distance entre au moins un sous-ensemble de chaque paire de bords correspondants dans l'image aérienne ou latente et l'image de MEB, et applique une moyenne pondérée à la distance mesurée pour produire une seule valeur de distance.
     
    27. Système selon la revendication 26 dans lequel la moyenne pondérée est égale à une racine nième d'une puissance nième moyenne de distance entre les bords MEB et l'image aérienne ou latente.
     
    28. Système selon la revendication 16 ou de n'importe quelle revendication dépendante ici, dans lequel l'image aérienne ou latente comprend une image aérienne ou une image latente.
     
    29. Système selon la revendication 16 ou de n'importe quelle revendication dépendante ici, dans lequel le programme de calibrage affiche un écran d'interface utilisateur qui affiche les graphiques individuels pour chaque paramètre de traitement qui trace les valeurs de paramètres pour chaque itération en même temps que les valeurs des distances résultantes, et affiche un graphique commun traçant une distance commune résultat de chaque itération.
     
    30. Système selon la revendication 16 ou de n'importe quelle revendication dépendante ici, dans lequel les paramètres de traitement entrés dans le simulateur de traitement comprennent des paramètres de traitement communs, qui comprennent à la fois les paramètres de réserve pour simuler une résine photosensible et des paramètres optiques pour simuler l'optique et les caractéristiques d'une machine pas à pas.
     




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    Cited references

    REFERENCES CITED IN THE DESCRIPTION



    This list of references cited by the applicant is for the reader's convenience only. It does not form part of the European patent document. Even though great care has been taken in compiling the references, errors or omissions cannot be excluded and the EPO disclaims all liability in this regard.

    Patent documents cited in the description