WO2019110948A1 - Procede et dispositif de determination automatique de valeurs d'ajustement de parametres de fonctionnement d'une ligne de depot - Google Patents
Procede et dispositif de determination automatique de valeurs d'ajustement de parametres de fonctionnement d'une ligne de depot Download PDFInfo
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- WO2019110948A1 WO2019110948A1 PCT/FR2018/053163 FR2018053163W WO2019110948A1 WO 2019110948 A1 WO2019110948 A1 WO 2019110948A1 FR 2018053163 W FR2018053163 W FR 2018053163W WO 2019110948 A1 WO2019110948 A1 WO 2019110948A1
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
- C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
- C23C14/54—Controlling or regulating the coating process
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
- C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
- C23C14/34—Sputtering
- C23C14/3492—Variation of parameters during sputtering
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
- C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
- C23C14/34—Sputtering
- C23C14/35—Sputtering by application of a magnetic field, e.g. magnetron sputtering
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
- C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
- C23C14/54—Controlling or regulating the coating process
- C23C14/542—Controlling the film thickness or evaporation rate
- C23C14/545—Controlling the film thickness or evaporation rate using measurement on deposited material
- C23C14/547—Controlling the film thickness or evaporation rate using measurement on deposited material using optical methods
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
- C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
- C23C16/52—Controlling or regulating the coating process
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
- G01B11/0616—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating
- G01B11/0625—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating with measurement of absorption or reflection
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
- H01J37/32—Gas-filled discharge tubes
- H01J37/34—Gas-filled discharge tubes operating with cathodic sputtering
- H01J37/3464—Operating strategies
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- H01J2237/00—Discharge tubes exposing object to beam, e.g. for analysis treatment, etching, imaging
- H01J2237/245—Detection characterised by the variable being measured
- H01J2237/24592—Inspection and quality control of devices
Definitions
- the invention relates to the manufacture of transparent substrates coated on at least one face of a stack of thin layers, in particular transparent substrates made of glass or polymeric organic material.
- coatings conferring on them particular properties, especially particular optical properties, for example reflection or absorption of radiation of a given wavelength range, electrical conduction properties, or properties related to ease of cleaning or the possibility for the substrate to self-clean.
- These coatings are generally stacks of thin layers based on inorganic compounds, especially metals, oxides, nitrides or carbides.
- the term "thin layer” refers to a layer whose thickness is less than one micrometer and generally ranges from a few tenths of nanometers to a few hundred nanometers (hence the term "thin").
- a stack of thin layers is generally manufactured via a succession of thin film deposits made in a plurality of compartments of a deposition line (typically 20 to 30 compartments), these deposits being made in the different compartments using one or more methods.
- deposition such as, in particular, magnetic field assisted sputtering (also known as magnetron sputtering), ion-assisted deposition (or IBAD for ion-assisted waste deposition), evaporation, chemical vapor deposition ( or CVD for Chemical Vapor Deposition), plasma enhanced chemical vapor deposition (PECVD for Plasma-Enhanced CVD), low pressure chemical vapor deposition (LPCVD for Low-Pressure CVD).
- the thicknesses of the thin layers deposited on the transparent substrate by the deposition line may be necessary to adjust the thicknesses of the thin layers deposited on the transparent substrate by the deposition line with a high accuracy, typically less than 1%, and this, on the entire width of the stack of thin layers deposited on the substrate.
- This can be achieved by acting on various parameters of the manufacturing process, and more particularly by adjusting the values of different operating parameters of the deposition line.
- Such operating parameters are, for example, for a magnetron sputtering deposition method, the powers applied to the cathodes of the different compartments of the deposition line, the speed of travel of the transparent substrate opposite each cathode, the pressure and / or the composition of the gases used in each compartment of the deposit line, etc. They can therefore be multiple and varied, and concern all or part of the compartments of the deposit line. It is understandable that in certain circumstances (for example when the deposit line is large and includes a large number of compartments), such an adjustment can be complex to implement and needs to be optimized.
- a first solution is to perform a layer-by-layer adjustment.
- the deposition line is parameterized and used to deposit at each passage of the substrate a single layer of material.
- Optical measurement devices placed at the output of the deposition line then measure different spectrometric values (eg in transmission and / or in reflection) at a plurality of points of the substrate on which the layer of material, distributed over the substrate, has been deposited. entire width of the board.
- the measured spectrometric values are compared to target values, determined over the entire width of the plate for the material layer considered by observing a stack of thin layers deemed satisfactory.
- the operating parameters of the deposition line are then adjusted by trial and error to attempt to obtain the target spectrometric values determined for the layer.
- This procedure (repository, measurements, comparison with target values and adjustment of the deposition line) is repeated for the same layer until target values are obtained for this layer (within a tolerance factor) and then repeated for each layer to be deposited on the substrate.
- This first solution can therefore be extremely time-consuming and time-consuming, even in the case where it is automated, since each layer is individually processed to adjust the operating parameters of the deposition line.
- a second solution is to use an optical simulation software that relies on physical modeling of the thin film stack deposited by the deposition line on the transparent substrate. This physical modeling takes into account, in particular, the thickness of the layers, possible gradients, the physical characteristics of the materials used for the layers (and in particular their refractive and absorption indices), etc.
- the optical simulation software is used to determine the "real" thicknesses of the thin film stack, and the operating parameters of the deposition line are then adjusted to make these thicknesses determined by means of the optical simulation software to desired target thickness values for thin film stacking.
- a third solution proposed in the prior art is to rely on in-situ instrumentation: more specifically, various optical measuring devices (eg ellipsometry, reflection spectrometry and / or transmission) are placed in various locations. the deposit line (in the actual deposit rack) so that it can perform optical measurements on each layer deposited on the substrate. Target values for these optical measurements are moreover determined from a stack of thin layers considered satisfactory. Adjustments of the operating parameters of the deposition line are identified to make the values measured by the different optical measuring devices positioned in the deposition line tend towards these target values.
- various optical measuring devices eg ellipsometry, reflection spectrometry and / or transmission
- the invention responds to this need by proposing a method for automatically determining an adjustment value of at least one operating parameter of a deposition line for depositing a stack of thin layers on a transparent substrate. this process comprising:
- a step of automatic determination by means of the mathematical model obtained of an adjustment value of the current value of said at least one operating parameter making it possible to reduce a difference existing between the value obtained from said at least one quality function and a target value chosen for said at least one quality function for thin film stacking.
- the invention also relates to a device for determining an adjustment value of at least one operating parameter of a deposition line for depositing a stack of thin layers on a transparent substrate, this device comprising :
- a first obtaining module configured to obtain a mathematical model connecting at least one operating parameter of the deposit line with at least one predetermined quality function defined from at least one quality measurement of a stack of thin layers deposited by the deposit line;
- a second obtaining module configured to obtain a value of said at least one quality function from a value of said at least one quality measurement measured at the output of the deposition line on a deposited thin-film stack; on a transparent substrate, said deposition line having been configured by means of a so-called current value of said at least one operating parameter;
- a determination module configured to automatically determine, by means of the mathematical model obtained, an adjustment value of the current value of said at least one operating parameter making it possible to reduce a difference existing between the value obtained from said at least one quality function; and a target value chosen for this at least one quality function for thin film stacking.
- the deposition line may implement a magnetic field assisted sputtering technique.
- the deposition line may use an ion beam assisted deposition (IBAD) technique, an evaporation deposition technique, a chemical vapor deposition (CVD) technique, a plasma enhanced chemical vapor deposition (PECVD for Plasma-Enhanced CVD), a low-pressure chemical vapor deposition (LPCVD) technique, etc.
- IBAD ion beam assisted deposition
- CVD chemical vapor deposition
- PECVD plasma enhanced chemical vapor deposition
- LPCVD low-pressure chemical vapor deposition
- a value may consist of a single component or designate a vector or matrix comprising a plurality of components (for example when the values of several operating parameters have to be adjusted, typically one or several operating parameters for several compartments of the deposition line, or when several quality measurements are typically considered at different points of the plateau).
- one or more quality measures can be considered in the mathematical model envisaged. These measurements can be considered individually, in the state or in a modified form, or be linked together if necessary via one or more mathematical functions (for example via an average). These various possible forms are taken into account in the sense of the invention by the concept of quality function.
- the invention thus proposes a simple and innovative solution compared to the state of the art for automatically adjusting the operating parameters of a deposition line of a stack of thin layers on a transparent substrate, from measurements. of quality performed at the output of the deposition line on the complete stack of thin layers deposited by it. These quality measurements make it possible to determine to what extent the stack of thin layers deposited by the deposit line meets the target characteristics that it is desired for it to respect.
- the solution proposed by the invention is based on a simplified mathematical model connecting the operating parameters of the deposition line to the quality measurements made at the output thereof on the stack of thin layers deposited on the substrate.
- Such a mathematical model is in practice much more robust and easy to determine than a physical model of the stack as proposed in the state of the art, which, on the contrary, is a nonlinear model requiring many physical measurements to be established.
- the model considered is a mathematical model that does not seek to rely strictly on physical realities and is therefore more robust to errors (due to the very nature of the model).
- This mathematical model can also, according to the choice made, have much fewer coefficients to estimate than a physical model, and its nature (eg linear model or more generally polynomial, etc.) is a priori independent of the number of layers and / or materials used for stacking.
- Such a mathematical model can also be easily determined by learning, from a set of reference data established considering a reasonable number of previously deposited stacks (according to a dedicated experiment plan, or during previous productions); it can also be easily updated.
- An advantage of the invention is the adjustment of manufacturing parameters of several sensitive thin layers.
- a layer in particular its thickness or its stoichiometry, may vary due to the variation of non-adjustable parameters, for example due to the decrease in the thickness of the target.
- Thin films whose tolerances are less than the variability of the characteristics of the layer due to these non-adjustable parameters can in particular be considered as a sensitive layer.
- the invention can notably be implemented as follows:
- a first stack of thin layers is produced with parameters which are well known to lead to the obtaining of a stack whose properties are unsatisfactory;
- one or more measurements are taken, but only at the output of the deposition line, and the manufacturing parameters of several layers, in particular of sensitive layers, are adjusted according to the result of these measurements, to obtain, at the next iteration of the process, or even after several iterations, a stack whose properties are satisfactory.
- the invention therefore offers a considerable time saving compared to the state of the art for the adjustment of operating parameters of a deposition line. This is also permitted by the invention without requiring the realization of partial stacks, or resort to expensive in situ instrumentation and difficult to maintain. Nevertheless, the adjustment values of the operating parameters of the deposition line provided by the invention are precise and make it possible to quickly reach the desired specifications for the thin film stack to be produced.
- the adjustment method proposed by the invention is advantageously based on measurement devices already present at the output of a deposition line, and particularly robust.
- Such measuring devices are for example spectrometers and / or ellipsometers making it possible to obtain quality measurements of the complete thin-film stack, and more specifically measurements of optical spectra (eg in transmission and / or in reflection , at an angle of 0 ° or other angles (eg 60 °)) and / or ellipsometric values.
- said at least one quality measure taken into account by the mathematical model may also comprise at least one of:
- a measurement of an electrical property of the stack of thin layers (for example of its electrical resistance);
- a measurement of a mechanical property of the stack of thin layers (for example of its mechanical strength);
- a measurement of a chemical property of the stack of thin layers (for example of its chemical resistance);
- the operating parameters of the deposit line that can be adjusted by means of the invention can be of different natures.
- said at least one operating parameter can comprise:
- At least one pressure and / or an amount of a gas used in a said compartment of the deposit line At least one pressure and / or an amount of a gas used in a said compartment of the deposit line; and or
- At least one magnetic field or a magnetic field distribution applied in a said compartment of the deposition line At least one magnetic field or a magnetic field distribution applied in a said compartment of the deposition line;
- At least one adjustment of a mechanical element of a said compartment of the deposit line At least one adjustment of a mechanical element of a said compartment of the deposit line.
- a linear regression algorithm eg regression of Ridge, Lasso or carrier vector type (SVR)
- SVR carrier vector type
- an algorithm using a neural network or decision trees eg Random Forest algorithm
- an algorithm learning by reinforcement or interpolation eg Gaussian Process algorithm or nearest neighbors
- the mathematical model used by the invention relates at least one variation of said at least one operating parameter with at least one variation of said at least one quality function.
- Such a model which seeks to consider variations in the operating parameters and variations in the quality function or functions, makes it possible to overcome any noise or bias that may result from various "hidden parameters", such as for example acquisition conditions of the different quality measurements that may differ between the stack considered during the adjustment and the reference stacks considered in the learning game of the mathematical model, or errors or drifts presented by the measurement used to acquire the quality measurements at the output of the deposition line (for example by optical sensors), or slow drifts affecting the deposition line (related, for example, to the age of the cathodes used in the compartments, etc. .).
- hidden parameters such as for example acquisition conditions of the different quality measurements that may differ between the stack considered during the adjustment and the reference stacks considered in the learning game of the mathematical model, or errors or drifts presented by the measurement used to acquire the quality measurements at the output of the deposition line (for example by optical sensors), or slow drifts affecting the deposition line (related, for example, to the age of the cathodes used in the compartments, etc. .).
- the obtaining step (in other words the learning step of the mathematical model) may comprise, for example:
- an experiment plan specifically dedicated to the determination of the mathematical model.
- variations of the values of the operating parameters are chosen to guarantee the linear hypothesis envisaged for the mathematical model (for example + 10% or -10% with respect to the reference values), and the deposition line is parameterized as many times as necessary with operating parameters adjusted according to the selected variations.
- the stacks of training layers are limited to a reasonable and limited number of stacks of relevant learning layers (typically fifteen) to obtain a reliable model.
- the training stacks can be made following each other by the line of deposit, or at a more spaced way in time.
- Such an experimental design is particularly relevant when it is desired to determine the mathematical model via a deterministic calculus, the complexity of which is directly related to the number of learning stacks considered.
- Another approach may be to consider various learning stacks already made by the drop line in a "random" manner when using the drop line, and to exploit the information available for these stacks (measurements quality and operating parameters of the associated deposit line) to determine the mathematical model. There is therefore much more information to determine the mathematical model.
- the stacks in question have not been made specifically within the framework of a pre-established experiment plan and dedicated to the determination of the model, we preferentially filter to constitute the model among all available data, those which correspond to outliers (related to errors from sensors for example) or to stacks with anomalies (eg stacks that do not have the right composition).
- the stack of thin layers is intended to undergo another treatment, called "post-treatment" after the series of measurements carried out at the outlet of the deposition line (for example thermal quenching, laminating, or double glazing).
- post-treatment after the series of measurements carried out at the outlet of the deposition line (for example thermal quenching, laminating, or double glazing).
- a goal sought by the invention can be to obtain a final product whose properties are satisfactory after this treatment and thus to adjust the operating parameters so that the final product (and not the thin film stack) at the output of the deposition line) has desired properties.
- This embodiment of the invention thus aims to minimize the difference between the value of the quality function measured at the output of the deposition line for a product whose properties have been judged satisfactory after further processing and the current value of the quality function.
- properties of the final product can be related to measurements made at the output of the deposition line, for example by an automatic statistical learning mechanism.
- the quality function is determined to account for this relationship.
- the quality function is chosen to represent certain characteristics of the stack of thin layers that are not easily measurable at the line output. This may be the case of thermal characteristics, the measurement of which takes a long time. Therefore, and more generally, in one embodiment of the invention, the vector or matrix representing the quality function may have one or more components that are not measurements but transformations of these measurements. Such a transformation can in particular be a transformation making it possible to predict a property that is difficult to measure according to an easily measurable property;
- a direct mathematical model requires an inversion of the model and can thus require, to converge towards the chosen target value for the quality function, several iterations in other words, several successive adjustments of the operating parameters. However, this convergence is quite fast, and only a few iterations are enough in practice.
- the use of such a direct model is more intuitive since it models the behavior of the deposited thin-film stack according to the setting of the deposition line. Its interpretation is therefore simpler from a physical point of view since it makes it possible to identify how the setting of the deposition line influences the properties of the thin film stack deposited.
- the mathematical model is a polynomial model ("direct") providing variations in the value of said at least one quality function as a function of variations in the value of said at least one operating parameter;
- the determination step comprises a step of inverting the polynomial model to determine said adjustment value.
- the inversion step may further comprise a regularization of the inversion of the polynomial model according to known techniques in order to improve the robustness of the model, such as for example using Tikhonov or Lasso regularization.
- regularization makes it possible to better distinguish the different layers of the stack of thin layers.
- a simple polynomial model such as a linear model is sufficient to lead to good results in terms of fit. This is the case, for example, when it comes to determining variations in optical spectra as a function of power variations to be applied to the cathodes of a deposition line using a magnetron sputtering technique.
- Such a polynomial model (and a / brf or / linear) has the advantage of being very simple to determine; it includes few coefficients to estimate and is particularly robust.
- the model chosen is a linear model, it can be represented in matrix form, easy to manipulate, which allows to derive the adjustment values by a deterministic calculation.
- var (n) denotes the difference between the values of the at least one quality function obtained for two stacks of thin learning layers deposited by the deposition line while the latter was parameterized with a value of the operating parameter P (n) having a variation +5 (n) with respect to the reference value of the operating parameter P (n) and with a value of the operating parameter P (n) having a variation - (n) with respect to the reference value of the operating parameter P (n).
- the adjustment value, denoted a, of said at least one operating parameter making it possible to reduce the difference existing between the value obtained from said at least one quality function and the target value chosen for it. is given for example by:
- v denotes the difference between the value obtained from the quality function and the target value chosen for the said quality function for the stack of thin layers and M T denotes the transposed matrix of the matrix M.
- the mathematical model used by the invention is obtained by using an automatic statistical learning algorithm.
- the advantage of using a statistical learning algorithm is, as mentioned above, that it offers the possibility of establishing the mathematical model from stacks of "random" thin layers made by the deposit line, and for which quality measures are available in association with the operating parameters with which the deposit line was configured to perform these stacks.
- the determination method further comprises a validation step of the mathematical model obtained.
- Such validation can be performed automatically or manually by an operator, for example by comparing, on a set of thin film stacks selected for validation, the layers that need to be adjusted by observing these stacks of validation against to the layers identified by the mathematical model (ie for which the mathematical model indicates non-zero adjustment values of the operating parameters associated with these layers).
- a new mathematical model is determined for example from another set of learning stacks, or from a filtered set of stacks in which the data relating to stacks with anomalies or outliers has been deleted.
- the various steps of the determination method are determined by computer program instructions.
- the invention also relates to a computer program on an information carrier, this program being able to be implemented in a determination device or more generally in a computer, this program comprising instructions adapted to the implementing the steps of a determination method as described above.
- This program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other form desirable shape.
- the invention also relates to a computer readable information or recording medium, and comprising instructions of a computer program as mentioned above.
- the information or recording medium may be any entity or device capable of storing the program.
- the medium may comprise storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording medium, for example a floppy disk or a disk. hard.
- the information or recording medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means.
- the program according to the invention can be downloaded in particular on an Internet type network.
- the information or recording medium may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question.
- the invention also relates to a method of parameterizing a deposition line capable of depositing a stack of thin layers on a substrate, this method comprising:
- the invention also provides a system for depositing a stack of thin layers on a transparent substrate comprising:
- a deposition line capable of depositing a stack of thin layers on a transparent substrate
- a deposition line configuration module configured to parameterize the deposition line with a current value of at least one operation parameter of the deposition line
- a determination device of an adjustment value of the current value of said at least one operating parameter configured to use a value of at least one quality measurement measured at the output of the deposit line on a stack of thin layers deposited on a transparent substrate by the deposition line;
- An estimation module configured to estimate an adjusted value of said at least one operating parameter from the current value and the adjustment value determined by the determination device;
- the parameterization module being configured to parameterize the deposition line with the adjusted value of said at least one operating parameter estimated by the estimation module.
- the parameterization method and the deposition system according to the invention have the same advantages as previously described, as the method and the determination device.
- the steps of deposition, determination, adjustment and the second parameterizing step of the parameterization process are implemented as long as:
- the difference between the value of said at least one quality function estimated from said measured value at the output of the deposition line and the target value chosen for said at least one quality function is greater than a predetermined threshold, or
- the adjustment value determined for said at least one operating parameter is less than a predetermined threshold.
- the value of a quality function can be represented by a vector or a matrix.
- the difference above is a distance (between two vectors or two matrices) and the threshold a real value.
- the difference between two vectors is a vector and the difference between two matrices is a matrix and the threshold is an element of the same nature.
- the difference between two vectors is lower than the threshold vector, if the absolute value of each component of the difference vector is less than the threshold vector component.
- the first condition makes it possible to ensure convergence towards the target value of said at least one quality function, while avoiding implementing unnecessary iterations.
- the second condition makes it possible to ensure that the determined adjustment value is not aberrant given expert criteria (according to which, in particular, the predetermined threshold is set). This second condition can also make it possible to force the adjustment value to meet certain criteria (eg to be for example in a certain range of values to guarantee certain properties to the substrate covered with the stack of thin layers).
- the determination method, the determination device, the parameterization method and the deposition system according to the invention present in combination all or some of the aforementioned characteristics.
- Figure 1 shows, schematically, in its environment, a deposition system according to the invention, in a particular embodiment
- Figure 2 shows a deposition line of the deposition system of Figure 1;
- FIG. 3 schematically represents the hardware architecture of an estimation device of the deposition system of FIG. 1, in accordance with the invention, and configured to determine operating parameter adjustment values of FIG. depot line of Figure 2;
- FIG. 4 shows in a particular embodiment, in the form of a flow chart, the main steps of a parameterization method according to the invention, as implemented by the deposition system of FIG. that the main steps of a determination method according to the invention as implemented by the estimation device of Figure 3;
- FIGS. 5A-5C illustrate the impact on different optical spectra measured on a thin film stack produced by a line of variation deposition applied on operating parameters of this deposition line.
- FIG. 1 represents, in its environment and in a particular embodiment, a deposition system 1 according to the invention, intended to be used for carrying out the depositing a stack of thin layers on a transparent substrate 2 by means of a deposition line 3.
- the deposition system 1 advantageously allows the deposition line 3 to be properly parameterized in order to ensure that the stack of layers thin meets predetermined target characteristics.
- the transparent substrate 2 is a 6 mm thick glass substrate on which is deposited a stack of thin layers comprising a plurality of thin functional layers that can act on the solar radiation (namely here, two silver layers denoted by Agi and Ag2), and coatings formed of one or more thin layers located on either side of each functional layer.
- the term “module” (M1, M2, M3) denotes each of the coatings which surround the functional silver layers, it being understood that a module may consist of a single thin layer or a thin layer. plurality of thin layers.
- the term "thin layer” refers to a layer whose thickness is less than one micrometer and generally ranges from a few tenths of nanometers to a few hundred nanometers (hence the term "thin").
- the stack of thin layers consists, successively from substrate 2:
- a module Ml consisting of a thin layer of silicon nitride (Si3N4) with a thickness of 14.5 nm and a thin layer of zinc oxide (ZnO) with a thickness of 6 nm;
- a module M2 consisting of a thin layer of blocking alloy of nickel and chromium (NiCr) with a thickness of 0.6 nm, a thin layer of zinc oxide (ZnO) with a thickness of 6 nm, a thin layer of silicon nitride (Si3N4) with a thickness of 34 nm, a thin layer of niobium nitride (NbN) of thickness lnm, a thin layer of silicon nitride (Si3N4) with a thickness of 34 nm and a thin layer of zinc oxide (ZnO) with a thickness of 5 nm;
- a module M3 consisting of a nickel-chromium alloy thin-walling layer (NiCr) of thickness lnm, a thin layer of zinc oxide (ZnO) with a thickness of 5 nm, a thin layer of silicon nitride (Si3N4) 23nm thick and a thin layer of zinc oxide and tin (SnZnO) 5nm thick.
- NiCr nickel-chromium alloy thin-walling layer
- ZnO zinc oxide
- Si3N4 silicon nitride
- this EX1 example is in no way limiting and other stacks of thin layers with other thicknesses as well as other transparent substrates (eg substrates of polymeric organic material, flexible or rigid) can be envisaged. .
- the deposition line 3 used to deposit the stack of thin layers on the transparent substrate 2 implements a magnetic field assisted sputtering technique also known as magnetron sputtering.
- the glass substrate coated with the stack of thin layers deposited by the filing line 3 (also sometimes referred to as a tray in this description) is hereinafter referred to as 4.
- a sputtering technique relies on the condensation within a rarefied atmosphere of a vapor of a target material from a source of spray on a substrate. More precisely, the atoms of the source (also referred to as a target) are ejected into an ionized gas, such as, for example, argon, in a vacuum chamber maintained at a certain pressure. An electric field is created leading to the ionization of the gas thus forming a plasma. The target is brought to a negative potential (cathode) so that the ions present in the plasma are attracted to the target and eject atoms from it.
- an ionized gas such as, for example, argon
- the particles thus sprayed are diffused in the chamber and some of them are collected in particular on the substrate on which they form a thin layer.
- a magnetic field oriented perpendicular to the electric field is also created by magnets placed near the cathode so as to confine the electrons in the vicinity of the cathode. This makes it possible to increase the ionization rate of the gas, and thus to significantly improve the deposition efficiency compared to a conventional sputtering technique. Since sputtering techniques are known to those skilled in the art, they are not described in more detail here.
- FIG. 2 diagrammatically represents the deposition line 3 used for depositing thin layers by sputtering on the glass substrate 2. It comprises here an inlet chamber 5, a first buffer chamber 6, a magnetron sputtering chamber 7 comprising a first transfer section 8 and a second transfer section 9, a second buffer chamber 10 and an exit chamber 11.
- Each element Ei comprises a compartment or deposition chamber 12-i containing a cathode used as a target during magnetron sputtering, and optionally one or two compartment (s) or pump chamber (s) equipped with a pump, and located (e) (s) where appropriate on both sides of the deposit chamber to create a vacuum therein.
- the glass substrate 2 circulates in the different successive compartments of the sputtering chamber 7, driven by a conveyor or a conveyor belt 13.
- the parameterization of the various compartments 12-i in which the thin-film depositions take place can be carried out by regulating various parameters of the magnetron cathode sputtering, and in particular, the pressure of the gas, its quantity and its composition, the power applied on the cathode of the compartment in question, the angle of incidence of the bombardment particles and the magnetic field applied in the compartment, the speed of travel of the glass substrate with respect to the cathode, etc. These adjustments are made so as to obtain at the output of the deposition line 3 a stack of thin layers respecting one or more target properties (for example mechanical and / or optical), desired for this stack. The respect of these properties is ensured as soon as the thin films deposited respect a certain thickness gauge with a predetermined precision.
- target properties for example mechanical and / or optical
- the various aforementioned parameters constitute operating parameters of the deposition line 3 within the meaning of the invention, which can be adjusted in whole or in part by means of the invention, by means of a parameterization module 14 of the deposition system 1 , provided for this purpose and using adjustment values of the operating parameters determined by the invention.
- the term "currents" denotes the operating parameters with which the deposition line 3 is configured by the parameterization module 14 and which it applies to deposit, during the passage of a transparent substrate through its compartments, a stack of thin layers on this transparent substrate.
- the deposition line 3 may be adjusted when the deposition line 3 is used, such as, for example, mechanical element settings of the compartments 12.
- the deposition line 3 such as the position of metal masks provided in the compartments for limiting the deposition of thin layers made on the substrate, the position of actuators for moving the magnets placed near the cathode, etc.
- the invention can also apply to this type of operating parameters of the deposit line.
- the deposition system 1 here comprises one or more systems 15 for controlling the quality of the stacks of thin layers deposited by the deposition line 3. This or these control systems 15 are placed at the output of the line of deposition. deposit 3 so as to provide quality measurements of the stacks of complete thin layers deposited by the deposit line 3.
- each optical spectrum SPj, j 1,..., J (where J denotes an integer greater than 1), measured by the optical control system 15 at a given measurement point, provides several measured spectral values for several lengths of time.
- wave for example for K wavelengths, K denoting an integer greater than 1: each optical spectrum SPj can thus be represented in the form of a vector of spectral values, each spectral value corresponding to a given wavelength .
- the optical control system 15 can measure one or more optical spectra typically at various points of the thin-film stack deposited by the line deposition 3 (ie over its entire width and over its entire length), in transmission and / or in reflection, on the side of the stack and / or on the side of the substrate, at different angles (eg angle of 0 ° and / or or 60 °), etc.
- Such an optical control system is known per se and conventionally used at the output of deposit lines. It is not described in more detail here.
- the integer J here takes into account, where appropriate, the different types of measured optical spectra and / or the different measurement point (s) at which these optical spectra are measured.
- control systems 15 may be placed at the output of the deposition line 3 to acquire measurements of the quality of the stack of thin layers deposited by the deposition line 3 on the glass substrate 2
- These may be other optical control systems such as ellipsometers, or control systems making it possible to acquire quality measurements of other types, such as, for example, measurements of an electrical property of the stack. thin layers (eg of its electrical resistance), measurements of a mechanical property of the stack of thin layers (eg of its mechanical strength), measurements of a chemical property of the thin film stack ( (eg chemical resistance), or measurements of a mass property of the thin film stack.
- the quality measurements made by the control system 15 on the complete stack deposited on the glass substrate 2 are supplied to a system determination device 16 according to the invention and configured to determine adjustment values of the common operating parameters with which the deposition line 3 is configured in view of the quality of the thin-film stack having been the subject of the quality measurements. These adjustment values are then supplied to the parameterization module 14 to modify, if necessary, all or part of the operating parameters of the deposit line 3 in order to achieve or any at least as close as possible to the desired target characteristics for the stacks of thin layers made by the deposition line.
- the determination device 16 is a computer whose hardware architecture is shown schematically in FIG. 3. It comprises a processor 17, a random access memory 18, a read-only memory 19, a non-volatile memory 20 , a communication module 21.
- the communication module 21 enables the determination device 16 to obtain the quality measurements made by the control system 15 (measurements of optical spectra in the example envisaged here) on a stack of thin layers deposited by the deposition line 3. and to communicate with the parameterization module 14 of the deposition line 3 to provide it with the adjustment values to be applied to the current operating parameters with which the deposition line 3 is configured.
- This communication module 21 may comprise in particular a digital data bus, and / or communication means on a network (local or remote) such as, for example, a network card, etc., depending on the way in which the optical control system 15 , the parameterization module 14 and the determination device 16 are interconnected. It is assumed, in the embodiment described here, that the parameterization module 14 also integrates an estimation module 22, configured to estimate adjusted values of the current operating parameters of the deposition line 3 from the current values with which the deposition line 3 is configured and adjustment values determined and provided by the determination device 16.
- the read-only memory 19 of the determination device 16 constitutes a recording medium in accordance with the invention, readable by the processor 17 and on which is recorded a computer program PROG according to the invention, comprising instructions for execution. steps of a localization method according to the invention.
- This computer program PROG equivalently defines functional and software modules here configured to implement the steps of the determination method according to the invention. These functional modules support or control the hardware elements 17 to 21 mentioned above. They include in particular here:
- a first obtaining module 16A configured to obtain a mathematical model, denoted MOD, connecting at least one operating parameter of the deposition line 3 with at least one predetermined quality function defined from at least one measurement of quality provided by the control system 15 placed at the output of the deposit line.
- quality function we mean here any mathematical function of one or more quality measures;
- a second obtaining module 16B configured to obtain a value of said at least one quality function from a value of said at least one quality measurement measured at the output of the deposition line 3 on a stack of layers thin deposited on a transparent substrate, while the deposition line 3 is configured by means of a current value of said at least one operating parameter; and
- An automatic determination module 16C configured to determine, by means of the mathematical model MOD, an adjustment value of the current value of said at least one operating parameter making it possible to reduce a gap existing between the value obtained from said at least one function of quality and a target value chosen for this at least one quality function for thin film stacking.
- a value can refer to either a single value or a vector of values.
- the value of said at least an operating parameter denotes the vector whose components are the values of the various operating parameters considered. The same is true when considering several quality measures or several quality functions.
- modules 16A-16C of the determination device 16 are described in more detail now with reference to FIG. 4.
- FIG. 4 represents, in the form of a flow chart, the main steps of a method of parameterizing the deposition line 3 according to the invention as implemented by the deposition system 1, in a particular embodiment of FIG. production.
- This parameterization method is based on the steps of a determination method according to the invention implemented by the determination device 16 of the system 1 to determine operating parameter adjustment values of the deposition line 3.
- These adjustment values are intended to make it possible to obtain, by means of the deposition line 3, a stack of thin layers satisfying predetermined characteristics (eg layers of predefined thicknesses with a given precision, etc.).
- the invention is particularly advantageous in that it relies for determining the adjustment values, on a simplified MOD mathematical model.
- each optical spectrum SPj comprising K measurements taken at K different wavelengths: SP1 is an optical spectrum in transmission, SP2 is a optical spectrum in reflection measured on the stack side, and SP3 is an optical spectrum in measured reflection transparent substrate side.
- Each optical spectrum SPj can be represented by a vector of K components.
- the spectrum SP corresponds to a quality function according to the invention defined from the optical spectra SP1, SP2,..., SPJ, which constitute quality measurements within the meaning of the invention.
- This quality function considers the quality measurements SP1, SP2, ..., SPJ as they are (ie they are just concatenated without being modified).
- the SP optical spectrum can be obtained by concatenating the measured optical spectral LxJ SP1 (1), ..., SP1 (L), SP2 (1), ..., SP2 (L), SPJ (1), ..., SPJ (L).
- the mathematical model MOD can be obtained in various ways by the determination device 16, and more particularly by its obtaining module 16A (step E10). It can be derived, as is the case in the embodiment described here, upstream of any adjustment of the parameters of the deposition line 3, by the obtaining module 16A of the determination device 16 or by another device and stored in a memory accessible by the obtaining module 16A (for example in the non-volatile memory 20 of the determining device 16) for future use. The obtaining module 16A can then obtain the MOD model when it needs it by accessing the non-volatile memory 20.
- the mathematical model MOD may be updated at each required adjustment.
- the mathematical model MOD can be a linear, polynomial, statistical model, etc.
- the relation established by the mathematical model MOD between the operating parameters and the quality measurements can be of different types: the mathematical model MOD can actually be a so-called "direct” model, providing values of the quality functions according to the values of the values. considered operating parameters, or a so-called "indirect” model providing values of the operating parameters as a function of the values of the quality functions.
- the obtaining module 16A establishes a linear and direct MOD1 mathematical model.
- Other types of mathematical models are described later in alternative embodiments.
- the deposition line 3 is used to deposit a stack of thin learning layers corresponding to this setting of the ith cathode; and the optical control system 15 is used to measure at the output of the deposition line 3 the SPj optical spectra (Pi_ref-x%) of the learning thin film stacks made;
- each training stack corresponds to the variation of a single operating parameter at a time, the same for each compartment (ie, it is considered to apply the variations of +/- x % the operating parameters individually and successively so as to individually identify the contribution of each power variation to the optical spectra measured by the optical control system 15).
- it is possible alternatively, to apply a different quantity upward and downward, and to vary this quantity from one cathode to another (x is in this case dependent on n, n 1, ...,NOT).
- the variations of +/- x% considered around each reference value Pi_ref of the operating parameters are chosen so as to maintain the hypothesis of linearity which is made, in the first embodiment, of the variations of the measurements. of quality depending on the variations of the operating parameters (ie x is chosen sufficiently important to be distinguished from noise while retaining the hypothesis of linearity made for the mathematical model). This is illustrated in Figures 5A-5C.
- VARi (x%) [SP (Pi ref + x%) - SP (Pi ref - x%) ⁇
- the matrix M thus obtained is of dimensions (KxJ) ⁇ N.
- This matrix in relation to the reference powers and the power variations applied to the reference powers, defines the linear mathematical model MOD1. It should be noted that the person skilled in the art would have no difficulty in deriving such a matrix in the case where different variations +/- x (i)% are applied to the reference powers Pi_ref of each cathode, but also if a different amount is applied upwards and downwards relative to the reference power on each cathode.
- the obtaining module 16A stores the matrix M in the non-volatile memory 20 as a mathematical model MOD1.
- the mathematical model MOD1 can be updated, for example from a new set of training stacks, to take account of the evolution of various factors that can affect the quality of thin film stacks made by the deposit line.
- the deposition line 3 is used to deposit a thin film stack of EX1 type on the glass substrate 2.
- the current values Pi_curr can be chosen by an expert or correspond for example to the values used during the last production carried out by the deposit line 3.
- the deposition line 3 is activated to deposit a stack of thin layers on the glass substrate 2 (step E30).
- the optical control system 15 measures at the output of the deposition line 3, various quality measurements on the complete thin film stack deposited (step E40).
- the quality measurements made are J optical spectra (so-called "currents”) SP1_curr, ..., SPJ_curr, as already detailed previously (including spectrum measurements for K distinct wavelengths). These optical spectra are provided by the optical control system 15 to the determination device 16 (and more precisely to its second obtaining module 16B) (step E50).
- the quality function considered corresponds to the concatenation of the optical spectra SP1_curr,..., SPJ_curr (quality measurements within the meaning of the invention) in the state.
- the concatenated quality measurements SPl_curr, ..., SPJ_curr to form the current optical spectrum SP_curr thus constitutes a value of a quality function obtained from a current value (corresponding to J spectra SPl_curr, ..., SPJ_curr) quality measurements within the meaning of the invention.
- the determination device 16 via its automatic determination module 16C, then evaluates the difference between the value obtained from the quality function and a target value chosen for this quality function for the thin-film stack produced by the line of measurement. 3.
- this difference noted v is a column vector evaluated as follows:
- target optical spectra SPj_targ can be determined experimentally from a stack of reference thin films having expected characteristics (eg in terms of mechanical and / or optical properties, or thicknesses of the layers, etc.) for this stack. .
- the determination device 16 seeks to minimize or at least reduce the difference v found between the current spectrum and the target spectrum to obtain a product at the output of the deposition line according to certain predetermined properties.
- the automatic determination module 16C determines, using the mathematical numerical model MOD stored in the non-volatile memory 20, the variations of the operating parameters to be applied to the current values of the operating parameters with which the deposit line 3 is set to minimize (if not cancel) the deviation v evaluated (step E60). This determination requires, when the mathematical model MOD is a direct model, an inversion of the mathematical model MOD (this one giving variations of the quality function according to the values of the operating parameters).
- AAJ argmin ⁇ P (
- DR denotes the N-dimensional column vector of the power variations to be applied to the N cathodes (M denoting a matrix having LxJ lines and N columns, DR is therefore an N-dimensional column vector).
- to minimize for determining the vector AAJ of the power adjustment values of the cathodes constitutes a cost function that must be minimized in order to try to correct the residual difference v between the current optical spectrum SP_curr measured on the stack at the output of the deposition line and the target optical spectrum SPtarg desired for this stack.
- the automatic determination module 16C here determines the vector AAJ by performing the following deterministic calculation (step E70):
- the automatic determination module 16C can implement a regularization of the inversion.
- DA] argmin AP (
- the vector DA] verifying this relation can be deterministically calculated by the following automatic determination module 16C:
- a denotes a regulation parameter and DR ⁇ the adjustment value applied to the current power of the cathode i.
- the regularization parameter applied by the automatic determination module 16C may be a constant or vary over time.
- the vector of the adjustment values AAJ determined by the determination device 16 is then supplied by the latter to the module parameterization 14 of the deposition line, and more particularly to its estimation module 22.
- the estimation module 22 estimates, from the adjustment values supplied to it, adjusted values of the operating parameters of the line deposition 3 (step E80). In the example envisaged here, it adds for each operating parameter concerned (ie for which a non-zero adjustment value has been determined by the determining device 16), the adjustment value supplied to the current value of the parameter of operation.
- the deposition line 3 is parameterized by the parameterization module 14, with the adjusted values of the operating parameters estimated by the estimation module 22 (step E90).
- the deposition line 3 is activated to re-deposit a stack of thin layers on the glass substrate 2 (step E100).
- the deposition system 1 can be activated to repeat the steps E40 to E100 until a difference is reached between the value of the quality function estimated from the quality measurement values obtained by the control system. optical 15 output of the deposition line 3 and the target value chosen for it less than a predetermined threshold.
- the steps of deposition, determination, adjustment and parameterization of the deposition line 3 can be implemented as long as the adjustment values determined for the operating parameters are below a predetermined threshold (that is, as long as the adjustment values are sufficiently small, the values of the operating parameters are adjusted, but if they reach incoherent values greater than the threshold considered, we stop adjustment of operating parameters of the deposit line).
- a predetermined threshold that is, as long as the adjustment values are sufficiently small, the values of the operating parameters are adjusted, but if they reach incoherent values greater than the threshold considered, we stop adjustment of operating parameters of the deposit line.
- the setting of the deposition line 3 is carried out in steps E20 and E90 by means of the parameterization module 14.
- this assumption is not limiting in itself and the The invention also applies when this parameterization is performed for example manually by an operator.
- the mathematical model MOD1 it is possible, in a particular embodiment, to validate the mathematical model MOD1 obtained by the obtaining module 16A, using dedicated tools.
- a tool can for example proceed in a manner similar or identical to that performed by the determination device 16 to determine the adjustment values of the operating parameters from a set of observed test stacks for which we know the actual adjustment values to be applied, and compare the adjustment values obtained using the MOD1 model with these actual values. If the values coincide with a certain reliability, the model MOD1 is validated.
- the obtaining module 16A is configured to determine a new mathematical model, for example from a new set of training stacks: this new set of learning stacks can correspond to in particular to take into account new values of variations x to be applied to the operating parameters around the reference values.
- the mathematical model MOD used by the determination device 16 to determine the adjustments to be made to the operating parameters of the deposition line 3 is a direct linear mathematical model MOD1, connecting variations of the spectra. optics measured at the output of the deposition line on the stack of thin layers to the power variations of the cathodes of the deposition line.
- mathematical models can be envisaged during the implementation of the invention.
- other types of models than a linear model can be considered, such as for example a polynomial model of degree greater than 1, or models obtained by using an automatic statistical learning algorithm.
- the mathematical model considered can also be direct like the MOD1 or indirect model, that is to say, provide values of the operating parameters (eg variations with respect to a reference value) as a function of the values of the quality measurements. (eg variations in quality measures).
- a second exemplary embodiment is envisaged in which the mathematical model MOD used by the determination device 16 is a direct mathematical model MOD2 obtained by using an automatic statistical learning algorithm.
- an algorithm in a manner known per se, makes it possible to determine the best a function that makes it possible to predict a variable Y from an X variable.
- the focus here is more particularly on a mathematical model MOD2 providing variations of the quality measures or quality functions considered ( optical spectral variations provided by the optical control system with respect to reference spectra as a function of variations of the operating parameters considered (eg power variations of the cathodes of the deposition line 3 with respect to power reference of these cathodes).
- Determining the direct mathematical model MOD2 thus consists in determining here via the automatic learning algorithm considered, the function (noted for example f) which makes it possible to predict the variations of optical spectra from variations in the power of the cathodes.
- this type of algorithm is based on two phases: a learning phase of the mathematical model MOD2 followed by a test phase which aims to validate the model learned during the learning phase.
- These two phases are implemented from a set of thin film stacks observed (stacks of thin learning layers in the sense of the invention) which are divided into two subsets (for example in a proportion 70% / 30%), one for use during the learning phase, when intended to be used during the test phase.
- the observed thin film stacks used to learn the mathematical model MOD2 have not necessarily been produced for this purpose, that is to say according to a predetermined experimental design as for the model mathematical MOD1.
- the use of the direct mathematical model MOD2 by the determination device 16 to determine the adjustment values of the operating parameters of the deposit line requires, as for the direct mathematical model MOD1, a model inversion. It is indeed during steps E60 and E70 to determine, thanks to the mathematical model MOD2, the variations of the operating parameters to be applied to the current values of the latter which minimize the difference v.
- the determination module 16C by means of the statistical learning algorithm from the MOD2 model previously learned, in a manner known per se (in the example previously considered of N operating parameters to be adjusted, by reusing the Notations previously introduced for the mathematical model MOD1, the vector DA] of the adjustment values of these N operating parameters is the vector DR minimizing the cost function vf (AP), where f denotes the function modeled by the model MOD2.
- the determination device 16 uses an indirect mathematical model providing values of the operating parameters (eg variations with respect to a reference value) as a function of the values of the quality measurements (e.g. variations in quality measures).
- a third exemplary embodiment is envisaged in which the mathematical model MOD used by the determination device 16 is an indirect mathematical model MOD3 obtained by using a statistical learning algorithm. automatic.
- the mathematical model MOD used by the determination device 16 is an indirect mathematical model MOD3 obtained by using a statistical learning algorithm. automatic.
- a MOD3 mathematical model providing variations of the considered operating parameters (eg powers of the cathodes of the deposition line 3) as a function of the variations of the quality measurements or of the quality functions considered (ex. optical spectra provided by the optical control system 15).
- the indirect mathematical model MOD3 can be used by the determination device 16 to determine the adjustment values of the operating parameters of the deposit line without requiring inversion.
- the knowledge of the difference v is sufficient to determine directly, from the mathematical model MOD3, the adjustment values of the operating parameters with respect to their current values.
- - q (l) denotes the considered quality measurement measured at the position indexed by I (q (1) can be a vector typically in the case of an optical spectrum measurement, having as many components as measured wavelengths ;
- - dist (a, b) is a selected distance between two quality measures a and b;
- - reg (P1, ..., PN) is a regularization of the operating parameters possibly considered
- the determination of the adjustment values of the operating parameters of the deposition line 3 by the determination device 16 is based on the following steps : Obtaining the values of the quality function or functions from the quality measurements made at the output of the deposition line 3 on a stack of thin layers deposited by the deposition line while the latter is parameterized with a current value of operating parameters;
- this determination may or may not include an inversion of the mathematical model MOD considered.
- the MOD2 or MOD3 model will first be realized with the given production history.
- the new x variation values to be applied can be determined by an expert or an algorithm. Stacks of thin layers generated by these variations can also be produced in one or more times.
- the deposit line can be configured or reconfigured either by a human or automatically by software.
- the function which connects said at least one operating parameter of the deposition line and said at least one quality function defined from said at least one quality measure, can be composed of a plurality of functions or combine a plurality of functions.
- a numerical model of the function f it is possible to determine separately for each of the plurality of functions of which it is composed a numerical model, for example by statistical learning, then to determine by means of an ad hoc algorithm the model digital function f.
- a value of a thin-layer operating parameter in other words by compartment of the deposit line, a compartment single being responsible for the deposition of a given thin layer of the stack
- the power of the cathode used by this compartment to proceed to the deposition of a thin layer on the glass substrate 2.
- n ' is not limiting in itself.
- the invention applies to other operating parameters of the deposition line 3, as already mentioned above (eg composition of gases, gas pressures, etc.).
- the layers of the stack deposited on the transparent substrate 2 by the deposition line 3 may alternatively be considered through the mathematical model MOD (the other layers being ignored by the model so that the device of FIG. determination does not provide adjustment values of the operating parameters for these layers, such "ignored” layers are for example very thin layers or not having a measurable impact on the stack made, for example layers of type "blockers", etc.), or some layers can be grouped via the MOD mathematical model.
- all the compartments of the deposition line are in the same surface treatment machine known under the name of "coater”.
- the compartments of the deposit line may be distributed in several surface treatment machines, the method thus comprising a passage to the air (or return to the atmosphere) between two machines.
- the measurement or measurements made are all performed at the output of the deposition line, that is to say, in the case where several surface treatment machines are used, after the exit of the last machine.
- At least one measurement can be performed, in situ, in other words, in the deposition line, before the last thin layer is produced by the last machine.
- additional measurement can be performed, in situ, in other words, in the deposition line, before the last thin layer is produced by the last machine.
- no further measurement is taken after each thin film production, it being said that a thin film may be obtained in several times, in other words by several compartments depositing the same material.
- the value of the quality function is obtained from quality measurements taken at different points over the entire width or surface of the substrate.
- This embodiment makes it possible to know the parameter or parameters to be modified (for example to adjust the thickness of at least one layer), for all or some of the thin layers, over the entire width or surface of these layers so as to respect the desired properties at any point of the plateau.
- valves In the case where it is desired to obtain a homogeneous thickness for a given layer, it is possible to control the valves to produce a different gas flow over localized areas of the substrate.
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BR112020011322-0A BR112020011322A2 (pt) | 2017-12-07 | 2018-12-07 | método e dispositivo de determinação automática de valores de ajuste dos parâmetros de funcionamento de uma linha de deposição |
RU2020121298A RU2020121298A (ru) | 2017-12-07 | 2018-12-07 | Способ и устройство автоматического определения величин регулирования параметров функционирования линии осаждения |
EP18833275.3A EP3720985A1 (fr) | 2017-12-07 | 2018-12-07 | Procede et dispositif de determination automatique de valeurs d'ajustement de parametres de fonctionnement d'une ligne de depot |
US16/770,818 US11739417B2 (en) | 2017-12-07 | 2018-12-07 | Method and a device for automatically determining adjustment values for operating parameters of a deposition line |
CN201880088871.XA CN111670264A (zh) | 2017-12-07 | 2018-12-07 | 用于自动确定沉积线的操作参数的调整值的方法和设备 |
MX2020005919A MX2020005919A (es) | 2017-12-07 | 2018-12-07 | Un método y un dispositivo para determinar automáticamente valores de ajuste para parámetros de funcionamiento de una línea de deposición. |
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FR1761786A FR3074906B1 (fr) | 2017-12-07 | 2017-12-07 | Procede et dispositif de determination automatique de valeurs d'ajustement de parametres de fonctionnement d'une ligne de depot |
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PCT/FR2018/053163 WO2019110948A1 (fr) | 2017-12-07 | 2018-12-07 | Procede et dispositif de determination automatique de valeurs d'ajustement de parametres de fonctionnement d'une ligne de depot |
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US (1) | US11739417B2 (fr) |
EP (1) | EP3720985A1 (fr) |
CN (1) | CN111670264A (fr) |
BR (1) | BR112020011322A2 (fr) |
FR (1) | FR3074906B1 (fr) |
MX (1) | MX2020005919A (fr) |
RU (1) | RU2020121298A (fr) |
TW (1) | TW201936958A (fr) |
WO (1) | WO2019110948A1 (fr) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2021239516A1 (fr) | 2020-05-26 | 2021-12-02 | Saint-Gobain Glass France | Procédé d'estimation d'une fonction de qualité d'un substrat transparent revêtu en monocouche ou en multicouche |
WO2022023029A1 (fr) | 2020-07-21 | 2022-02-03 | Saint-Gobain Glass France | Procédé de configuration d'un processus de revêtement |
FR3120125A1 (fr) * | 2021-02-25 | 2022-08-26 | Saint-Gobain Glass France | Dispositif de mesure de pression vide secondaire et système embarqué pour mesure de pression de vide résiduel |
EP4105746A1 (fr) | 2021-06-17 | 2022-12-21 | Saint-Gobain Glass France | Procédé de réglage des paramètres d'un processus de revêtement pour fabriquer un substrat transparent revêtu |
Families Citing this family (3)
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CN116685709A (zh) * | 2021-06-01 | 2023-09-01 | 株式会社爱发科 | 溅射装置、溅射装置的控制方法和溅射装置用控制装置 |
JP2023163848A (ja) * | 2022-04-28 | 2023-11-10 | エピクルー株式会社 | エピタキシャル成長装置のためのパラメータ決定装置、パラメータ決定方法、及びパラメータ決定プログラム |
CN115976480B (zh) * | 2022-12-21 | 2024-05-28 | 江苏盆晶科技有限公司 | 一种金属双极板镀层智能加工方法和系统 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001090434A2 (fr) * | 2000-05-24 | 2001-11-29 | Semitool, Inc. | Reglage d'electrodes utilisees dans un reacteur pour le traitement electrochimique d'une piece micro-electronique |
US7324865B1 (en) * | 2001-05-09 | 2008-01-29 | Advanced Micro Devices, Inc. | Run-to-run control method for automated control of metal deposition processes |
WO2016110407A1 (fr) * | 2015-01-11 | 2016-07-14 | Soleras Advanced Coatings Bvba | Couvercle doté d'un système de capteur pour un système de mesure configurable pour un système de pulvérisation configurable |
WO2017061333A1 (fr) * | 2015-10-08 | 2017-04-13 | 株式会社ニューフレアテクノロジー | Dispositif de mesure de vitesse de croissance en phase vapeur, dispositif de croissance en phase vapeur, et procédé de détection de vitesse de croissance |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6524449B1 (en) * | 1999-12-03 | 2003-02-25 | James A. Folta | Method and system for producing sputtered thin films with sub-angstrom thickness uniformity or custom thickness gradients |
US20050185174A1 (en) * | 2004-02-23 | 2005-08-25 | Asml Netherlands B.V. | Method to determine the value of process parameters based on scatterometry data |
DE102007030052B4 (de) * | 2007-06-29 | 2015-10-01 | Advanced Micro Devices, Inc. | Automatische Abscheideprofilzielsteuerung |
KR101668984B1 (ko) * | 2013-09-14 | 2016-10-24 | 칼 짜이스 에스엠티 게엠베하 | 마이크로리소그래피 투영 장치의 동작 방법 |
WO2015171149A1 (fr) * | 2014-05-08 | 2015-11-12 | Halliburton Energy Services, Inc. | Régulation par transmission/réflexion optique de la vitesse de dépôt in situ pour la fabrication de glace |
CN106521459B (zh) * | 2016-08-17 | 2018-04-17 | 中山大学 | 一种mocvd设备生长均匀性工艺参数的优化方法 |
JP2018160290A (ja) * | 2017-03-22 | 2018-10-11 | 株式会社東芝 | 磁気記録媒体の製造方法、多層膜の成膜システム、及び成膜調整方法 |
-
2017
- 2017-12-07 FR FR1761786A patent/FR3074906B1/fr active Active
-
2018
- 2018-12-07 RU RU2020121298A patent/RU2020121298A/ru unknown
- 2018-12-07 EP EP18833275.3A patent/EP3720985A1/fr active Pending
- 2018-12-07 CN CN201880088871.XA patent/CN111670264A/zh active Pending
- 2018-12-07 WO PCT/FR2018/053163 patent/WO2019110948A1/fr unknown
- 2018-12-07 TW TW107144220A patent/TW201936958A/zh unknown
- 2018-12-07 MX MX2020005919A patent/MX2020005919A/es unknown
- 2018-12-07 US US16/770,818 patent/US11739417B2/en active Active
- 2018-12-07 BR BR112020011322-0A patent/BR112020011322A2/pt unknown
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001090434A2 (fr) * | 2000-05-24 | 2001-11-29 | Semitool, Inc. | Reglage d'electrodes utilisees dans un reacteur pour le traitement electrochimique d'une piece micro-electronique |
US7324865B1 (en) * | 2001-05-09 | 2008-01-29 | Advanced Micro Devices, Inc. | Run-to-run control method for automated control of metal deposition processes |
WO2016110407A1 (fr) * | 2015-01-11 | 2016-07-14 | Soleras Advanced Coatings Bvba | Couvercle doté d'un système de capteur pour un système de mesure configurable pour un système de pulvérisation configurable |
WO2017061333A1 (fr) * | 2015-10-08 | 2017-04-13 | 株式会社ニューフレアテクノロジー | Dispositif de mesure de vitesse de croissance en phase vapeur, dispositif de croissance en phase vapeur, et procédé de détection de vitesse de croissance |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2021239516A1 (fr) | 2020-05-26 | 2021-12-02 | Saint-Gobain Glass France | Procédé d'estimation d'une fonction de qualité d'un substrat transparent revêtu en monocouche ou en multicouche |
WO2022023029A1 (fr) | 2020-07-21 | 2022-02-03 | Saint-Gobain Glass France | Procédé de configuration d'un processus de revêtement |
FR3120125A1 (fr) * | 2021-02-25 | 2022-08-26 | Saint-Gobain Glass France | Dispositif de mesure de pression vide secondaire et système embarqué pour mesure de pression de vide résiduel |
WO2022180085A1 (fr) * | 2021-02-25 | 2022-09-01 | Saint-Gobain Glass France | Dispositif de mesure de pression vide secondaire et système embarqué pour mesure de pression de vide résiduel |
EP4105746A1 (fr) | 2021-06-17 | 2022-12-21 | Saint-Gobain Glass France | Procédé de réglage des paramètres d'un processus de revêtement pour fabriquer un substrat transparent revêtu |
WO2022263586A1 (fr) | 2021-06-17 | 2022-12-22 | Saint-Gobain Glass France | Procédé de réglage de paramètres d'un procédé de revêtement pour la fabrication d'un substrat transparent revêtu |
Also Published As
Publication number | Publication date |
---|---|
FR3074906B1 (fr) | 2024-01-19 |
RU2020121298A3 (fr) | 2022-01-14 |
CN111670264A (zh) | 2020-09-15 |
EP3720985A1 (fr) | 2020-10-14 |
RU2020121298A (ru) | 2022-01-14 |
BR112020011322A2 (pt) | 2020-11-17 |
MX2020005919A (es) | 2020-10-28 |
US11739417B2 (en) | 2023-08-29 |
TW201936958A (zh) | 2019-09-16 |
US20200392617A1 (en) | 2020-12-17 |
FR3074906A1 (fr) | 2019-06-14 |
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