• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    A method for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit (28) is disclosed. The method comprises the following steps: At least one predetermined section of the optical projection unit (28) is illuminated in such a way that an image is generated from at least two channels of the predetermined section of the multi-channel optical projection unit (28). At least one parameter is determined on the basis of the analysis of the image, a value of the parameter being assigned to a characteristic feature of the projection unit (28), a defect of the projection unit (28) and / or a defect class of the projection unit (28). The quality of the projection unit (28) is assessed on the basis of the at least one parameter. Furthermore, a test system (10) for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit (28) and a computer program are disclosed.
    公开号:BE1027491B1
    申请号:E20205601
    申请日:2020-09-01
    公开日:2021-10-01
    发明作者:Wilfried Noell；Sophiane Tournois；Isabel Agireen；Katrin Schindler；Susanne Westenhöfer
    申请人:Suss Microoptics；
    IPC主号:
    专利说明:

    -1- BE2020 / 5601 Method and test system for evaluating the quality of a micro-optical and / or sub-wavelength optical multi-channel projection unit The invention relates to a method for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit. The invention also relates to a test system for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit and a computer program for executing the method.
    Micro-optical projection units and sub-wavelength optical projection units typically comprise several micro-optical elements or
    sub-wavelength optical elements. The projection units typically include several hundred or several thousand of these optical elements, the diameter of the individual optical elements typically being less than 1 mm (micro-optical projection unit), in particular less than 1 μm (sub-wavelength optical projection unit) and of the order of several nanometers .
    Compared to classical optics, the manufacturing techniques of micro-optical and / or sub-wavelength optical devices can differ greatly, since the required packing density of optically active elements is considerably higher.
    When manufacturing the projection units, deviations can occur in one or more parameters of the manufacturing process. For example, the size, curvature and / or position of the optical elements can vary due to tolerances and process changes. In particular, the distance between individual optical elements can be different.
    In addition, pinholes can be present in the wafer, which lead to light being transmitted through the projection unit in areas outside the optical elements.
    Overall, there are high demands on the positioning accuracy of the individual optical elements of the projection units and also on other defects such as the above-mentioned pinholes.
    -2- BE2020 / 5601 Depending on the application of the projection unit, however, defects or errors can be tolerable to a certain extent. The quality of the projection units must therefore be assessed or evaluated in order to ensure that the respective quality criteria are met by the projection unit.
    The object of the invention is therefore to create a method and a system for evaluating the quality of micro- and / or sub-wavelength optical projection units.
    The object is achieved according to the invention by a method for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit. The method comprises the following steps: At least one predetermined section of the micro- and / or sub-wavelength optical projection unit is illuminated in such a way that an image is generated from at least two channels of the predetermined section of the multi-channel micro- and / or sub-wavelength optical projection unit. The image is captured and analyzed. At least one parameter is determined on the basis of the analysis of the image, a value of the parameter being assigned to at least one characteristic feature of the projection unit, at least one defect of the projection unit and / or at least one defect class of the projection unit. The quality of the projection unit is assessed on the basis of the at least one parameter.
    Sub-wavelength optical elements, in particular sub-wavelength lenses, can also be referred to as nano-optical elements, in particular nanolenses.
    The invention is based on the knowledge that defects in the projection unit are directly related to certain defects in the image generated by the projection unit. For example, deviations in the position of optically active areas of the projection unit and deviations from a desired curvature of the individual optically active areas and / or pinholes in the wafer each lead to correspondingly associated characteristic defects in the image.
    -3- BE2020 / 5601 Furthermore, unwanted pinholes in the projection unit cause bright circular spots in the image; unintentional misalignments during the manufacturing process lead to blurred contours; mechanical defects on lenses (e.g. scratches and / or cracks) lead to circular black rings and / or dark spots; Unwanted impurities in the manufacturing process result in dark speckles (dark spots), e.g. irregularities in the pattern.
    By analyzing the defects in the image generated by the projection unit, the quality of the projection unit can be assessed particularly quickly and conveniently by applying image processing techniques to the recorded image. Many different, well-elaborated image processing techniques are known in the art.
    The image that is generated, recorded and then analyzed by at least the predetermined section of the projection unit can be the same image that the projection unit generates during its intended use, or parts thereof.
    Alternatively or additionally, the image to be recorded and analyzed can be generated in that at least the predetermined section is illuminated in the same way as during the intended use of the projection unit.
    The projection unit is set up, for example, in such a way that it creates a projection of a motif onto a surface, in particular a surface in a predetermined arrangement with respect to the projection unit.
    According to one aspect of the invention, the at least one characteristic variable comprises at least one of the following variables: sharpness, dark level, uniformity, fluctuations in brightness and local defects. It has been found that these variables are particularly suitable for evaluating the quality of the projection unit, since they are directly assigned to certain defects that can typically occur during the production of the projection unit.
    According to a further aspect of the invention, the micro-optical projection unit is divided into one of at least two quality classes on the basis of the at least one parameter. Thus, the projection unit in
    -4- BE2020 / 5601 one of exactly two classes can be divided, e.g. "quality test passed" or "quality test not passed". Alternatively, the projection unit can be divided into one of several classes, such as "Quality A", "Quality B" etc. Each of the classes is assigned to certain quality requirements that must be met, in particular certain threshold values for one or more the parameters that must be achieved.
    In a further embodiment of the invention, the projection unit comprises a plurality of micro- and / or sub-wavelength optical elements, in particular microlenses and / or sub-wavelength lenses, in particular wherein the optical elements have a size, a curvature, a masking and / or a position in the projection unit, wherein the size, the curvature and / or the position is at least one of the at least one characteristic feature of the projection unit. Deviations in the position of the optical elements and / or deviations from a desired curvature of the individual optical elements are assigned to certain defects in the recorded image. Consequently, defects in the recorded image can be associated with defects in the projection unit and / or the optical elements in such a way that the defects in the recorded image can be used to assess the quality of the projection unit.
    Generally speaking, each of the optical elements is set up to diffract, refract and / or guide light in a predetermined manner. Optical elements with diffractive, refractive and / or conductive properties can thus be implemented on the same projection unit, in particular on a common substrate.
    At least some of the optical elements, in particular all of the optical elements, can be designed to be identical to one another. Likewise, at least some of the optical elements, in particular all of the optical elements, can be designed differently from one another, i.e. the optical elements can differ, for example, in terms of size and / or curvature.
    The projection unit can comprise at least one additional mechanical feature, e.g. a spacer and / or at least one protrusion. The spacer and / or the at least one projection can serve to provide a spacing during use of the projection unit
    -5- BE2020 / 5601 to be set between the projection unit and a corresponding additional component.
    In particular, a geometric feature of the spacer and / or of the at least one projection can be a characteristic of the projection unit.
    For example, a height, width and / or depth of the spacer and / or of the at least one projection can be a parameter of the projection unit.
    In particular, the optical elements form an array, the array having a distance between two adjacent optical elements which is at least one of the at least one characteristic feature of the projection unit. The distance can be the minimum distance between the individual adjacent optical elements, the maximum distance between the individual adjacent optical elements and / or the average distance between the individual adjacent optical elements.
    In a further embodiment of the invention, the quality of the projection unit is assessed during the production of the projection unit, in particular at the end of the production line. The quality assessment is preferably repeated after several intermediate steps in the production of the projection unit. On the basis of the assessment of the quality during production, the projection unit may, if necessary, be sorted out in an earlier phase of production, as a result of which production time and production aids are saved.
    According to a further aspect of the invention, production parameters, in particular at least one of the characteristic features, are adapted on the basis of the quality assessment of the projection unit. The process parameters can thus be adapted in such a way that the quality of the projection unit is improved. In other words, any errors and / or deviations can be taken into account during production, thereby mitigating the effects of these production deviations.
    Manufacturing parameters that can be adapted are, for example, the base layer thickness of the projection unit, the lens shape and / or the lens curvature.
    -6- BE2020 / 5601 The image of the projection unit is preferably analyzed using a statistical method and / or a machine learning module, in particular the machine learning module comprising a pre-trained artificial neural network. In other words, the image can be analyzed with a purely classical, deterministic method based on classical algorithms, i.e. calculation rules. The image, on the other hand, can also be analyzed using a machine learning process alone or a mixture of both.
    The machine learning module can be pre-trained with marked training data, the marked training data including sample images generated by the specified section of an optical sample projection unit, and the marked training data including the at least one parameter that corresponds to the respective sample images and / or the quality class of the respective sample projection unit is equivalent to. The machine learning module is thus pre-trained in a monitored manner.
    The machine learning module preferably comprises an artificial neural network, for example a convolutional neural network (CNN) and / or a recurrent neural network (RNN), which is pretrained to be analyzes the image, determines at least one parameter and / or evaluates the quality of the projection unit.
    Alternatively, the machine learning module can be pre-trained semi-monitored with partially marked data or unsupervised.
    According to a further aspect of the invention, the at least one parameter corresponding to the respective sample images is obtained by applying the statistical method to the sample images. In this way, the sample image is first analyzed using the classic statistical approach, the result of the statistical analysis yielding the marking for the respective image.
    In other words, the statistical approach is performed on a limited set of sample images. The sample images and the result of the statistical analysis form the training data for the machine learning module. The statistical method is therefore possibly only applied to this limited training set in order to train the machine learning module, while the quality assessment takes place during the production of the projection unit via the machine learning module.
    -7- BE2020 / 5601 According to the invention, the object is also achieved by a test system for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit, which comprises a lighting unit, an image recording device, an image analysis module and a control module, the control module being so set up is that it causes the test system to perform the procedure described above. With regard to the advantages and features of the test system, reference is made to the above explanations regarding the method, which also apply to the test system and vice versa.
    According to one aspect of the invention, the test system comprises a test object, in particular wherein the test object is a multi-channel micro- and / or sub-wavelength optical projection unit. As a rule, the test object is a substrate, such as a wafer, with one or more multi-channel micro- and / or sub-wavelength optical projection units. In particular, the test object can be designed as a single projection unit.
    According to the invention, the object is also achieved by a computer program which comprises commands which, when the program is executed by a processing unit of the control module of the test system described above, cause the test system to carry out the steps of the method described above. With regard to the advantages and features of the computer program, reference is made to the above explanations relating to the method, which also apply to the computer program and vice versa.
    In this and below, the term “commands” is understood to mean commands in the form of program code and / or program code modules in compiled and / or uncompiled form, with the commands being able to be created in any programming language and / or in machine language.
    The foregoing aspects and many of the attendant advantages of the claimed subject matter will become more readily apparent when better understood from the following detailed description in conjunction with the accompanying drawings; FIG. 1 shows schematically an inspection system according to the invention, FIG. 2 shows a detailed view of an image generated by a projection unit according to the invention,
    -8- BE2020 / 5601 -Figur3 a schematic flow diagram of a method for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit according to the invention, FIGS. 4 to 6 each illustrate individual steps of the method from FIG.
    In FIG. 1, a test system 10 with an illumination unit 12, a test object 14, a projection surface 16 and an image recording device 18 is shown schematically.
    The inspection system 10 further comprises a control and analysis unit 20 with a control module 22 and an image analysis module 24, the control and analysis unit 20 being connected in a signal-transmitting manner both to the lighting unit 12 and to the image recording device 18.
    The test object 14 is a substrate 26 which comprises a plurality of multi-channel micro- and / or sub-wavelength optical projection units 28. In the case shown in FIG. 1, the substrate 26 comprises four projection units 28. However, this number is only used for illustration. The substrate 26 can also comprise any other number of projection units 28.
    In particular, the test object 14 can be constructed as a single projection unit 28.
    The projection units 28 each include a plurality of micro- and / or sub-wavelength optical elements 30. The projection units 28 can each include between ten and 1000 optical elements 30, in particular between 30 and 500 optical elements 30, for example between 50 and 300 optical elements 30.
    A diameter of the individual optical elements 30 can be smaller than 1 mm in the case of micro-optical projection units 28 or smaller than 1 μm in the case of sub-wavelength optical projection units 28 and can be of the order of several nanometers.
    A region between the optical elements 30 is opaque so that light can only pass through the projection units through the optical elements 30.
    In general terms, each of the optical elements 30 is set up in such a way that it refracts, diffracts and / or guides light in a predefined manner. In other words, each of the optical elements 30 provides a channel of the multi-channel projection unit 28.
    More precisely, the optical elements 30 each have a predetermined size, a predetermined curvature and / or are provided with a mask in such a way that the desired optical properties of the individual optical elements 30 are achieved.
    The optical elements 30 can have a mask which is set up to create a projection of a motif onto a surface. The location of the surface with respect to the projection unit 28 is known. For example, the projection unit 28 is mounted in a door of a car, and the optical elements 30 are masked so that a projection of an emblem of the car manufacturer is generated on the road surface next to the car when the projection unit 28 is illuminated.
    At least some of the optical elements 30, in particular all of the optical elements 30, can be designed to be identical to one another. Likewise, at least some of the optical elements 30, in particular all of the optical elements 30, can be designed differently from one another, i.e. differ, for example, in terms of size, curvature and / or masking.
    The optical elements 30 are arranged in a predetermined manner in the respective projection unit 28, depending on the specific field of application of the projection unit 28.
    More precisely, the optical elements 30 are arranged in such a way that a desired image 32 is created behind the projection unit 28 when the projection unit 28 is illuminated.
    The optical elements 30 are distributed over the surface of the projection unit 28 in accordance with a predetermined pattern. This is shown in FIG. 1, in which the image 32 generated by illuminating one of the projection units 28 is recorded on the projection surface 16.
    In other words, the optical elements 30 form a multi-channel micro- and / or sub-wavelength optical array which is set up in such a way that it generates an image 32 with predefined properties when it is illuminated. The images generated by the individual optical elements 30 - which can also be referred to as “beamlets” - are superimposed, which leads to a statistical mixture of the images generated by the individual optical elements 30. As an alternative to this, the optical elements 30 can be distributed randomly over the area of the corresponding projection unit 28.
    The generated image 32 is shown in more detail in FIG. The image 32 contains dark areas 34 and illuminated areas 36. In the specific example from FIG. 2, the illuminated areas 36 are strips which each comprise two straight sections which are connected to one another via a curved section.
    During the production of the projection units 28, deviations in one or more process parameters can occur. For example, the size, curvature and / or position of the optical elements 30 can vary due to tolerances and process fluctuations. In particular, the distance between individual optical elements 30 can vary.
    In addition, there may be defects such as pinholes in the wafer 26, which lead to the transmission of light through the projection unit 28 in areas outside the optical elements 30.
    Depending on the application of the projection unit 28, such defects may possibly be tolerable to a certain extent. The quality of the projection units 28 must therefore be assessed in order to ensure that the corresponding quality criteria are met by the projection unit 28.
    The test system 10 is set up in such a way that it carries out a method for evaluating the quality of the projection units 28, which method is described below with reference to FIG.
    - 11 - BE2020 / 5601 More precisely, a computer program is executed on a central unit of the control and analysis unit 20, which causes the test system 10 to carry out the method described below.
    First of all, at least one predetermined section of at least one of the projection units 28 is illuminated by the lighting unit 12 (step S1). For example, one of the projection units 28 is completely illuminated, while the other projection units on the wafer 26 are not illuminated.
    As an alternative to this, only a specific section of one of the projection units 28 may be illuminated, for example a section with a specific structure of optical elements 30, the quality of which is to be assessed.
    As an alternative, several projection units 28 can also be illuminated at the same time.
    Without loss of generality, the following describes the case in which one of the projection units 28 is completely illuminated.
    Due to the illumination of the projection unit 28, the projection unit 28 generates an image and projects it onto the projection surface 16. In other words, the image 32 is generated by the projection unit 28 and projected onto the projection surface 16.
    The image that is generated by at least the predetermined section of the projection unit 28 can be the same image or parts thereof that the projection unit 28 generates during its intended use.
    Alternatively or additionally, the image can be generated in that at least the predefined section of the projection unit 28 is illuminated in the same way as when the projection unit 28 is used as intended.
    The image generated by the projection unit 28 is then recorded via the image recording device 18 (step S2). Generally speaking, the recorded image is a digital representation of the generated image 32, the recorded image comprising a plurality of pixels, each of which has a brightness value and / or a color value.
    -12- BE2020 / 5601 The image recording device 18 can be designed, for example, as a camera, in particular as a high-resolution camera.
    It is also conceivable that the image recording device 18 is integrated into the projection surface 16. The projection surface 16 can for example comprise light-sensitive elements which are set up to record the image generated by the projection unit 28.
    The recorded image is then forwarded to the control and analysis unit 20, more precisely to the image analysis module 24, and analyzed by the image analysis module 24 (step S3).
    The image analysis module 24 then determines at least one parameter of the recorded image on the basis of the analysis of the image (step S4).
    Generally speaking, the at least one parameter is assigned to the quality of the projection unit 28. This is due to the fact that defects in the wafer 26 and / or defects in the optical elements 30 show up in corresponding defects in the image, i.e. defects in the generated image 32.
    For example, deviations in the position of the individual optical elements 30, deviations from a desired curvature of the individual optical elements 30 and / or pinholes in the wafer 26 each lead to correspondingly assigned characteristic defects in the image.
    It has been found that there are several main image features which are well suited for evaluating the quality of the projection unit 28, namely sharpness, dark level and uniformity, the uniformity in turn including brightness fluctuations, background fluctuations and local defects. A more detailed definition of these image features follows below.
    Accordingly, the at least one parameter can include one or more of the following variables: sharpness, dark level, uniformity, brightness fluctuations, background fluctuations, local defects, curvature of the individual optical elements 30, minimum distance between the individual optical elements 30, maximum distance between the individual optical elements 30 and / or average distance between the individual optical elements 30.
    -13- BE2020 / 5601 The image analysis module 24 determines the at least one parameter by analyzing the image, which is based on a statistical method and / or a machine learning method.
    In other words, the image can be analyzed with a purely classical, deterministic method based on classical algorithms, i.e. calculation rules. On the other hand, the image can be analyzed using a machine learning method alone or a mixture of both.
    The case of the statistical approach is explained in more detail below with reference to FIGS.
    FIG. 4 shows schematically the steps which are taken to determine the sharpness of the image.
    In the first column of Figure 4, two images with the same pattern are shown. However, the image in the first line is in focus while the image in the second line is out of focus.
    A measure of the sharpness of the image is obtained by the following procedure: First, a fast Fourier transform of the image is determined (second column in FIG. 4). The Fourier transformed image is then autocorrelated (third column) and the inverse fast Fourier transform of the autocorrelated image is determined (fourth column).
    A mean value of the intensity, a normalized intensity and / or a mean value of the normalized intensity of the result of the inverse Fourier transformation is or are determined, which represents a measure of the sharpness of the original image. A higher mean value of the intensity is associated with a sharper image, while a lower mean value of the intensity is associated with a less sharp image.
    For the normalization, for example, the highest value of the image is normalized with respect to the image height, the image width, the image mean value and / or the image standard deviation.
    FIG. 5 shows the main steps that are undertaken in order to obtain the parameter “dark level”. Generally speaking, it is the dark level
    "14 - BE2020 / 5601 a measure for the brightness of those areas of the projection surface 16 that should not be illuminated, i.e. for the brightness of the dark areas 34.
    The image on the left-hand side of FIG. 5 shows the original image which was recorded by the image recording device 18.
    To determine the dark level, a contrast threshold with respect to the background and / or a brightness threshold is set in order to distinguish the illuminated areas 36 from the dark areas 34 (background), as is illustrated in the image on the right-hand side of FIG. In other words, each pixel of the captured image is categorized as either “lit” (dotted area) or “not lit” (hatched area).
    Thereafter, an average brightness level of the pixels categorized as “not illuminated”, i.e. an average brightness level and / or an average contrast of the dark areas 34 is determined.
    The dark level is possibly only determined for a specific region of the dark areas 34, i.e. for a so-called region of interest.
    FIG. 6 shows how the parameter “uniformity” is determined.
    First, the edges of the illuminated areas 36 are determined such that the individual illuminated area 36 can be analyzed individually. The result is shown on the left-hand side of FIG. 6, where the uppermost strip-shaped illuminated area 36 has been isolated.
    A mask filter 38 is then applied to the isolated illuminated area 36, the mask filter having a predetermined size and a predetermined brightness threshold. In general terms, the mask filter 38 determines the degree of fluctuations in the brightness level within the predetermined size of the mask filter 38. The mask filter 38 is applied to the entire illuminated area 36 one after the other.
    In other words, the parameter “uniformity” represents a measure of local fluctuations in brightness and background fluctuations within the illuminated areas 36 and thus a measure of local defects in the respective projection unit 28 and / or the respective optical elements 30.
    -15- BE2020 / 5601 On the basis of the at least one determined parameter, the projection unit 28 is divided into one of at least two quality class categories (step S5). For this purpose, at least one predetermined quality criterion is established for at least one of the parameters, in particular for several or all parameters. The at least one predetermined criterion includes, for example, a threshold value for the sharpness of the image and / or a corresponding threshold value for the other parameters. The projection unit 28 can be divided into one of two classes, namely “quality test passed” and “quality test failed”.
    For example, the relevant projection unit 28 is classified in the “quality test failed” class if the at least one quality criterion is not met. Correspondingly, the relevant projection unit 28 is classified in the “quality test passed” class if the at least one quality criterion is met.
    Of course there can be more than two grades. The projection unit in question is classified, for example, in one of the classes “Grade A”, “Grade B” etc., depending on whether one or more of the quality criteria is or are met and depending on which of the quality criteria is met.
    In different applications of the projection unit 28, different requirements can be placed on the quality of the projection unit 28. For example, a projection unit 28 classified in the "quality level C" class may not be suitable for a particular application in which a high quality of the projection unit 28 is required, but there may be an application in which the "quality level C" class is sufficient is. The projection unit 28 may not have to be discarded, but can simply be assigned to another application.
    The quality of the projection unit 28 is preferably assessed during the manufacture of the projection units 28. In particular, the quality of the projection unit 28 is assessed at the end of the production of the projection unit 28, i.e. at the end of the production line.
    - 16 - BE2020 / 5601 The process parameters for the production line can be adjusted based on the result of the evaluation. As an alternative to this, the quality of the projection unit 28 is repeatedly assessed after intermediate steps in the production of the projection unit 28. On the basis of the quality assessment, the relevant projection unit 28 may possibly be sorted out in an earlier production stage, as a result of which production time and production aids are saved. If the projection unit 28 is not sorted out due to the interim quality assessment, manufacturing process parameters can be adapted for further manufacture.
    The above explanations relate to the fact that steps S3 to S5 are carried out using a classic statistical approach.
    As already mentioned above, however, these steps can also be carried out using a machine learning method.
    More precisely, the control and analysis unit 20 or the image analysis module 24 can comprise a machine learning module which is set up to carry out the steps S3 to S5 described above.
    The machine learning module comprises an artificial neural network, for example a convolutional neural network (CNN) and / or a recurrent neural network (RNN), which is pre-trained so that it can Analyzes image, which determines at least one parameter, evaluates the quality of the respective projection unit 28 and / or assigns the test object 14 to a class.
    Of course, any other suitable type of neural network can also be used.
    Furthermore, the statistical approach and the machine learning approach can be combined.
    In particular, the machine learning module can be pre-trained with data that are obtained by evaluating the quality of the projection unit 28 using the statistical approach.
    "17 - BE2020 / 5601 More precisely, several sample images are analyzed in the manner described above using the statistical approach.
    As a result, marked training data are obtained which, on the one hand, comprise the sample images and, on the other hand, the at least one parameter corresponding to the respective sample images and / or the quality class of the respective sample projection unit 28.
    The marked training data are fed to the machine learning module.
    The machine learning module determines the at least one parameter and / or categorizes the relevant image into one of the quality classes.
    Then weighting factors of the machine learning module are adapted based on a deviation of the determined at least one parameter and / or the determined quality class from an actual parameter and / or an actual quality class of the projection unit 28, which was determined using the statistical approach.
    In summary, it should be said that the test system 10 is set up in such a way that it evaluates the quality of one or more projection units 28 during the production of the projection units 28.
    On the basis of the quality assessment, the individual projection units 28 are divided into quality classes using a statistical approach, a machine learning method, or a combination of the two.
    The test system 10 accordingly offers the possibility of determining the suitability of individual projection units 28 for applications that require certain quality features.
    权利要求:
    Claims (13)
    [1]
    1. A method for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit (28), with the following steps: - At least one predetermined section of the micro- and / or sub-wavelength optical projection unit (28) is illuminated so that at least two channels of the predetermined section of the multi-channel micro- and / or sub-wavelength optical projection unit (28) an image is generated, - the image generated by the predetermined section of the projection unit (28) is recorded, - the image is analyzed, - based on the analysis of the image at least one parameter is determined, a value of the parameter being assigned to at least one characteristic feature of the projection unit (28), at least one defect of the projection unit (28) and / or at least one defect class of the projection unit (28), and - the quality of the projection unit ( 28) is evaluated on the basis of the at least one parameter.
    [2]
    2. The method according to claim 1, characterized in that the at least one parameter comprises at least one of the following: sharpness, dark level, uniformity, brightness fluctuations and local defects.
    [3]
    3. The method according to any one of the preceding claims, characterized in that the micro-optical projection unit (28) is divided into one of at least two quality classes on the basis of the at least one parameter.
    [4]
    4. The method according to any one of the preceding claims, characterized in that the projection unit (28) comprises a plurality of micro- and / or sub-wavelength optical elements (30), in particular microlenses and / or sub-wavelength lenses, in particular wherein the optical elements (30) have a size a curvature, a masking and / or a position in the
    -19- BE2020 / 5601 projection unit (28), the size, the curvature and / or the position being at least one of the at least one characteristic feature of the projection unit (28).
    [5]
    5. The method according to claim 4, characterized in that the optical elements (30) form an array, the array having a distance between two adjacent optical elements (30) which is at least one of the at least one characteristic feature of the projection unit (28) .
    [6]
    6. The method according to any one of the preceding claims, characterized in that the quality of the projection unit (28) is assessed during the production of the projection unit (28), in particular at the end of the production line.
    [7]
    7. The method according to claim 6, characterized in that production parameters, in particular at least one of the characteristic features, are adapted on the basis of the quality assessment of the projection unit (28).
    [8]
    8. The method according to any one of the preceding claims, characterized in that the image of the projection unit (28) is analyzed using a statistical method and / or a machine learning module, in particular wherein the machine learning module comprises a pre-trained artificial neural network.
    [9]
    9. The method according to claim 8, characterized in that the machine learning module has been pre-trained with marked training data, wherein the marked training data include sample images that are generated by the predetermined section of an optical sample projection unit, and wherein the marked training data include the at least one parameter that corresponds to the respective sample images and / or the quality class of the respective sample projection unit.
    [10]
    10. The method according to claim 9, characterized in that the at least one parameter corresponding to the respective sample images is obtained by applying the statistical method to the sample images.
    -20- BE2020 / 5601
    [11]
    11. Test system for evaluating the quality of a multi-channel micro- and / or sub-wavelength optical projection unit (28), with a lighting unit (12), an image recording device (18), an image analysis module (24) and a control module (22), the control module (22) is set up in such a way that it causes the test system (10) to carry out the method according to one of the preceding claims.
    [12]
    12. Test system according to claim 11, characterized in that the test system (10) comprises a test object (14), in particular wherein the test object (14) is a multi-channel micro- and / or sub-wavelength optical projection unit (28).
    [13]
    13. Computer program with instructions which, when the program is executed by a processing unit of the control module (22) of the test system (10) according to claim 11 or 12, cause the test system (10) to carry out the steps of the method according to one of claims 1 to 10.
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    CN112525490A|2021-03-19|
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    FR3100331A1|2021-03-05|
    AT522945A3|2021-06-15|
    KR20210028119A|2021-03-11|
    SG10202008528YA|2021-04-29|
    US20210063863A1|2021-03-04|
    JP2021047179A|2021-03-25|
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    BE1027491A1|2021-03-05|
    AT522945A2|2021-03-15|
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    TW202122773A|2021-06-16|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20030063789A1|2001-08-29|2003-04-03|Seiko Epson Corporation|Device for inspecting illumination optical device and method for inspecting illumination optical device|
    DE10348509A1|2003-10-18|2005-05-19|Carl Zeiss Jena Gmbh|Determining image errors by computing test object Zernike coefficients involves detecting pupil edge according to position, size in computer system in addition to wavefront measurement by evaluating light from object recorded by CCD camera|
    TWI361269B|2008-04-02|2012-04-01|Univ Nat Taiwan|Lens measurement device and method for measuring lens|
    DE102017118355A1|2017-08-11|2019-02-14|Gom Gmbh|Device and method for the planar 3D optical metrology|
    法律状态:
    2021-10-06| FG| Patent granted|Effective date: 20211001 |
    2021-10-06| PD| Change of ownership|Owner name: SUSS MICROOPTICS; CH Free format text: DETAILS ASSIGNMENT: CHANGE OF OWNER(S), ASSIGNMENT; FORMER OWNER NAME: SUSS MICROTEC LITHOGRAPHY GMBH Effective date: 20210830 |
    优先权:
    申请号 | 申请日 | 专利标题
    NL2023747A|NL2023747B1|2019-09-02|2019-09-02|Method and test system for assessing the quality of a multi-channel micro- and/or subwavelength-optical projection unit|
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