WO2022111889A1 - Method and device for inspecting a weld seam in an assembly of an optical system for microlithography - Google Patents
Method and device for inspecting a weld seam in an assembly of an optical system for microlithography Download PDFInfo
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- WO2022111889A1 WO2022111889A1 PCT/EP2021/077095 EP2021077095W WO2022111889A1 WO 2022111889 A1 WO2022111889 A1 WO 2022111889A1 EP 2021077095 W EP2021077095 W EP 2021077095W WO 2022111889 A1 WO2022111889 A1 WO 2022111889A1
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- Prior art keywords
- weld seam
- optical system
- assembly
- measurement
- microlithography
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
- G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
- G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
- B23K31/006—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to using of neural networks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
- B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to investigating the properties, e.g. the weldability, of materials
- B23K31/125—Weld quality monitoring
-
- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/708—Construction of apparatus, e.g. environment aspects, hygiene aspects or materials
- G03F7/70808—Construction details, e.g. housing, load-lock, seals or windows for passing light in or out of apparatus
- G03F7/70825—Mounting of individual elements, e.g. mounts, holders or supports
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/708—Construction of apparatus, e.g. environment aspects, hygiene aspects or materials
- G03F7/70858—Environment aspects, e.g. pressure of beam-path gas, temperature
- G03F7/70883—Environment aspects, e.g. pressure of beam-path gas, temperature of optical system
- G03F7/70891—Temperature
-
- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/708—Construction of apparatus, e.g. environment aspects, hygiene aspects or materials
- G03F7/70975—Assembly, maintenance, transport or storage of apparatus
Definitions
- the invention relates to a method and an apparatus for inspecting a weld seam in an assembly of an optical system for microlithography.
- the invention further also relates to an assembly of an optical system for microlithography, which is produced using a method according to the invention.
- Microlithography is used to produce microstructured components, such as integrated circuits or LCDs, for example.
- the microlithographic process is carried out in what is known as a projection exposure system, which has an illumination device and a projection lens.
- mirrors are used as optical components for the imaging process due to the lack of availability of suitable transparent refractive materials.
- a problem that occurs in practice is that the EUV mirrors or mirror elements experience heating due to the absorption of the radiation emitted by the EUV light source, among other things, and an associated thermal expansion or deformation, which in turn impairs the imaging properties of the image optical system can result.
- Approaches to avoiding surface deformations caused by heat input into an EUV mirror and the associated optical aberrations include active direct cooling.
- heat can be dissipated via cooling channels through which a cooling fluid (e.g. cooling water) can flow.
- a cooling fluid e.g. cooling water
- Such cooling ducts can in particular be incorporated directly into the support frame or into the components forming this support frame (e.g. designed as discs) and can each be closed off from the outside by a welded cover.
- a problem that occurs in practice is the fundamental risk that over the lifetime of the optical system, corrosion-related leaks will occur in the area of said cooling ducts or the weld seams closing off these cooling ducts, provided that the effectively remaining wall thickness between the respective cooling duct and an outer surface of the relevant, the supporting frame forming component falls below critical values.
- the effectively remaining wall thickness is reduced by unavoidable defects (especially in the form of pores or cavities) within the weld seam.
- a constructive enlargement of the wall strengthens the design of the components or panes forming the supporting frame
- there are limits both because of the decreasing efficiency of heat dissipation with increasing wall thickness and because of existing space restrictions.
- NA image-side numerical aperture
- a method for inspecting a weld in an assembly of an optical system for microlithography comprises the following steps:
- the model is generated using an artificial intelligence method, wherein in a learning phase the model is trained using a large number of training data, the training data each comprising measurement data from previously carried out CT measurements and evaluations associated with this measurement data.
- the invention is based in particular on the concept of carrying out an automatic evaluation of a weld seam using a model and using an artificial intelligence method.
- the invention includes the Invention the concept of training the automatic assessment of the weld seam based on measurement data obtained in a CT measurement model in advance by using measurement data from previously performed CT measurements and these measurement data associated ratings as training data.
- the invention also makes use of the fact that suitable training data for training the model are typically available in very large numbers (eg several thousand) from evaluations carried out in the past.
- Weld also calculates a respective mean diameter of defects present in the weld on the basis of this three-dimensional data set.
- Weld also calculating a respective remaining wall thickness of defects present in the weld on the basis of this three-dimensional data set.
- the weld seam is evaluated with regard to the risk of a corrosion-related leak occurring.
- One aim of the method according to the invention is to unequivocally assign each voxel of a volume of the weld seam to be evaluated to one of several mutually exclusive classes.
- the method of artificial intelligence can use machine learning, in particular “deep learning”, more particularly “convolutional neural networks” (CNNs).
- CNNs In contrast to other methods of automated volume segmentation, such as a voxei-wise extraction of information-carrying features (e.g. SIFT or HOG) with subsequent transformation of these features (e.g. PCA or Fisher encoding) and subsequent application of classic machine learning models for voxei-wise classification (e.g. SupportVectorMachine, Random Forest, nearest-neighbor classifier or similar), the use of CNNs has the advantage that a step of feature extraction and subsequent transformation does not have to be implemented at great expense and with the use of assumptions to be justified before the actual one classifier can be applied. Rather, CNNs allow the simultaneous learning of information-carrying features, transformation and classifier by optimizing a single, common optimization criterion (e.g. classification accuracy). Especially for the context of the present application, this represents a substantial advantage, since due to the low signal-to-noise ratio of the data, it is usually not readily possible to find robust feature extractions for the defect segmentation.
- a concrete implementation can include "Convolutional Neural Networks" (CNNs), possibly pre-learned on other datasets, optimized using variants of gradient descent (e.g. Adam).
- CNN can be configured to output a single value (e.g., the probability of a voxel belonging to a defect) for each voxel of a volume.
- a CNN can be designed to output two (“binary”) or more (“multi-class”) values for each voxel of a volume, which indicate class memberships in which the classes mutually exclusive (e.g. the probabilities that a voxel belongs to mutually exclusive classes “defect” and “defect-free”).
- a CNN can also be designed to output two or more values that indicate class memberships in which the classes can also occur independently of one another ("multi label", e.g. the probabilities for defect types "pore” and "” that occur independently of one another. Material”).
- multi label e.g. the probabilities for defect types "pore” and "” that occur independently of one another. Material
- Such configurations would, for example, correspondingly minimize the quadratic error, the softmax cross-entropy, or the sigmoid cross-entropy as an optimization criterion in the learning step.
- Suitable CNN structures consist of an encoding module and an optional decoding module.
- the encoding engine creates a lower resolution feature volume from the input volume. The specific form of the features is determined by the free parameters of the coding module.
- the coding module usually consists of several stages. Each stage consists of several layers of convolutions, non-linear activation functions (e.g. hyperbolic tangent or piecewise linear functions "ReLU") and normalizations (e.g. "batch normalization"). The outputs of each layer are in turn input to one or more subsequent layers.
- the decoding module creates the prediction volume on the basis of the feature volume, intermediate results from the coding stages and, if applicable, the input volume.
- a well-known example of such CNNs is U-Net.
- the data is repeatedly changed slightly in the learning step (“augmentation”), for example by mirroring on the x and y axes (“data flipping”), cutting out randomly selected image sections (“data cropping”) or random variations the image contrast.
- augmentation for example by mirroring on the x and y axes (“data flipping”), cutting out randomly selected image sections (“data cropping”) or random variations the image contrast.
- suitable regularization methods can be used in the learning step in order to prevent the data from simply being memorized (e.g. "DropOut").
- the method of artificial intelligence includes supervised learning.
- the invention is not limited to this.
- the assembly has at least one cooling channel through which a cooling fluid can flow.
- this cooling channel is closed by a cover welded over the weld seam.
- the optical system is a microlithographic projection exposure tool.
- the optical system is designed for a working wavelength of less than 30 nm, in particular less than 15 nm.
- the invention is not restricted to this, in which case the optical system can also be designed for a different working wavelength in other applications.
- the invention further relates to an assembly of an optical system for microlithography, which is manufactured using a method according to the invention.
- the invention further relates to a device for inspecting a weld seam in an assembly of an optical system for microlithography, the device being configured to carry out a method having the features described above.
- FIG. 1 shows a schematic representation of a detail from an assembly with a weld seam, which can be inspected using a method according to the invention
- FIG. 2a-2b flow charts to explain the possible sequence of a method according to the invention
- FIG. 3 shows a schematic representation of a section from an assembly with a weld seam to be evaluated and with pores detected using a method according to the invention
- FIG 4 is a schematic representation to explain what is possible
- FIG. 5 shows a schematic illustration to explain the possible structure of a microlithographic projection exposure system designed for operation in the EUV.
- FIGS. 1 and 3 An exemplary sequence of a method according to the invention is described below with reference to the schematic representations of FIGS. 1 and 3 and the flowcharts of FIGS. 2a and 2b.
- the fiction, contemporary inspection of a weld relates to this Exemplary embodiment of an assembly of an optical system (in particular a projection lens) for microlithography, for example the projection exposure system of FIG. 4 or the projection exposure system FIG. 5, which will be described below.
- the electromagnetic radiation incident on the effective optical surface of the optical elements or mirrors leads via absorption to heating and an associated thermal deformation, which in turn can impair the imaging properties.
- heat can be dissipated via cooling channels through which a cooling fluid (e.g. cooling water) can flow, which can be incorporated in particular into the components or panes forming a support frame of the projection exposure system and can each be closed by a welded cover.
- a cooling fluid e.g. cooling water
- the base part 11 can be one of the components or panes forming the support frame.
- FIG. 3 also shows in section and in a highly simplified representation a partial view of an assembly according to the invention, with components that are analogous or essentially functionally the same as in FIG. 1 being denoted by reference numbers increased by “20”.
- the weld seam as such (as the area which merges the cover 32 and the base part 31 with one another) is not emphasized.
- Such pores 35 can (without the invention being based thereon would be limited) have typical diameters from approx. 0.1mm up to 1mm and more and result in a reduction of the effectively remaining (residual) wall thickness between the inside of the cooling channel and the surface (designated with “36” in Fig. 3).
- cover 32 or the weld seam means that the risk of leaks occurring as a result of unavoidable corrosion during the term of the assembly or the optical system is increased.
- An inspection of the weld seam typically carried out using CT measurements, serves both to prepare for a corresponding repair (by re-welding) and to optimize the respective welding parameters with regard to existing pores.
- An essential feature of the present invention is that the corresponding assessment of the weld seam with regard to such pores 35 or remaining wall thicknesses is not carried out solely on the basis of a visual inspection by service personnel, but is based on a model generated using an artificial intelligence method.
- FIG. 2a The flow chart of FIG. 2a is initially used for a brief explanation of the basic manufacturing process and the integration of the above-mentioned CT analysis in this manufacturing process.
- the components mechanically prefabricated in a first step S205 are first cleaned (step S210), then welded (step S220) and mechanically rough-processed (step S230), with the latter rough-processed in particular includes machining to reduce existing wall thicknesses before a subsequent CT measurement (step S240).
- a final cleaning (step S260) follows the mechanical finishing (step S250).
- step S241 the CT measurement is first carried out and the measurement data obtained is stored in step S242. 3D segmentation then takes place in step S243, with each individual voxel of the weld seam being classified as belonging to a defect, in particular a pore 35 becomes. Each individual voxel within the weld seam is classified based on the CT measurement and on the basis of the artificial intelligence model used according to the invention in the sense of a binary assignment as to whether the respective voxel is part of a pore 35 or not.
- a defect list is generated in step S244, which lists all the pores 35 present within the weld seam and their size or average diameter.
- a residual wall thickness is calculated for each pore 35 (as a distance to the next pore 35 or to the interface between the weld seam or cover 32 and the area outside the cover 32 or the relevant weld seam).
- step S247 After a corresponding CT protocol has been created on the basis of the defect list and the residual wall thickness calculation in step S246, the measurement job ends (step S247).
- Fig. 4 shows a schematic representation of a possible structure of a microlithographic projection exposure system 400, which is designed for operation at wavelengths in the DUV range (i.e. for a working wavelength of less than 250 nm, in particular less than 200 nm, e.g. approx. 193 nm). and has an illumination device 401 and a projection lens 408 .
- the illumination device 401 comprises a light source 402 and an illumination optics symbolized in a greatly simplified manner by lenses 403 , 404 and a diaphragm 405 .
- the working wavelength of the projection exposure system 400 is 193 nm when using an ArF Excimer laser as light source 402.
- the working wavelength can also be 248 nm when using a KrF excimer laser or 157 nm when using an F2 laser as light source 402, for example.
- a mask 407 is arranged between the illumination device 401 and the projection objective 408 in the object plane OP of the projection objective 408 and is held in the beam path by means of a mask holder 406 .
- the mask 407 has a structure in the micrometer to nanometer range, which is reduced by a factor of 4 or 5, for example, to an image plane IP of the projection lens 408 by means of the projection lens 408 .
- the projection lens 408 includes a lens arrangement, also symbolized in a highly simplified manner by lenses 409 to 412, which defines an optical axis OA.
- the image plane IP of the projection lens 408 is positioned by a substrate holder 418 and provided with a light-sensitive layer 415 nes substrate 416, or a wafer, held. Between the last optical element 420 of the projection lens 408 on the image plane side and the light-sensitive layer 415 is an immersion medium 450, which can be deionized water, for example.
- FIG. 5 shows a schematic meridional section of the possible structure of a microlithographic projection exposure system designed for operation in EUV.
- the projection exposure system 501 has an illumination device 502 and a projection lens 510.
- the illumination device 502 serves to illuminate an object field 505 in an object plane 506 with radiation from a radiation source 503 via an illumination optics 504 .
- a reticle 507 arranged in the object field 505 is exposed.
- the reticle 507 is held by a reticle holder 508 .
- the reticle holder 508 can be displaced in particular in a scanning direction via a reticle displacement drive 509 .
- a Cartesian xyz coordinate system is shown in FIG. 5 for explanation.
- the x-direction runs perpendicular to the plane of the drawing.
- the y-direction is horizontal and z-direction is vertical.
- the scanning direction runs along the y-direction.
- the z-direction runs perpendicular to the object plane 506.
- the projection lens 510 is used to image the object field 505 in an image field 511 in an image plane 512.
- a structure on the reticle 507 is imaged on a light-sensitive layer of a wafer 513 arranged in the area of the image field 511 in the image plane 512.
- the wafer 513 is held by a wafer holder 514.
- the wafer holder 514 can be displaced via a wafer displacement drive 515, in particular along the y-direction.
- the relocation of the reticle 507 on the one hand via the reticle displacement drive 509 and on the other hand the wafer 513 via the wafer displacement drive 515 can take place synchronously with one another.
- the radiation source 503 is an EUV radiation source.
- the radiation source 503 emits in particular EUV radiation, which is also referred to below as useful radiation or illumination radiation.
- the useful radiation has a wavelength in the range between 5 nm and 30 nm.
- the radiation source 503 can be, for example, a plasma source, a synchrotron-based radiation source or a free-electron laser (“free-electron laser”, FEL). act.
- the illumination radiation 516 which emanates from the radiation source 503, is bundled by a collector 517 and propagates through an intermediate focus in an intermediate focal plane 518 into the illumination optics 504.
- the illumination optics 504 has a deflection mirror 519 and a first facet mirror 520 (with schematically indicated facets 521) and a second facet mirror 522 (with schematically indicated facets th 523).
- the projection objective 510 has six mirrors M1 to M6. Alternatives with four, eight, ten, twelve or another number of mirrors Mi are also possible.
- the penultimate mirror M5 and the last mirror M6 each have a passage opening for the illumination radiation 516 .
- the projection objective 510 is a doubly obscured optic.
- the projection objective 510 has a numerical aperture on the image side which is greater than 0.5 and which can also be greater than 0.6 and which can be 0.7 or 0.75, for example.
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CN202180079364.1A CN116802561A (en) | 2020-11-26 | 2021-10-01 | Method and device for inspecting welds in components of an optical system for microlithography |
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DE102020131383.7 | 2020-11-26 | ||
DE102020131383.7A DE102020131383A1 (en) | 2020-11-26 | 2020-11-26 | Method and device for inspecting a weld seam in an assembly of an optical system for microlithography |
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DE (1) | DE102020131383A1 (en) |
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CN109285139A (en) * | 2018-07-23 | 2019-01-29 | 同济大学 | A kind of x-ray imaging weld inspection method based on deep learning |
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2020
- 2020-11-26 DE DE102020131383.7A patent/DE102020131383A1/en not_active Ceased
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- 2021-10-01 CN CN202180079364.1A patent/CN116802561A/en active Pending
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DE102009039400A1 (en) * | 2009-08-31 | 2011-03-03 | Carl Zeiss Laser Optics Gmbh | Reflective optical element for use in an EUV system |
CN109285139A (en) * | 2018-07-23 | 2019-01-29 | 同济大学 | A kind of x-ray imaging weld inspection method based on deep learning |
DE102018213189A1 (en) * | 2018-08-07 | 2020-02-13 | Carl Zeiss Smt Gmbh | Process for bending hydroformed cooling devices and curved, hydroformed cooling devices |
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