Method, control device and storage medium

Abstract

Method (100) for operating a process group which performs at least a first process (P1) and a second process (P2), comprising the method (100): • Capture (101) a substrate (102) which has been processed by the first process (P1) and then by the second process (P2); and • Rules (103) of the process group by means of a first control variable of the first process (P1) and by means of a second control variable of the second process (P2) based on a result of the acquisition (101); • wherein the control (103) of the process group by means of the first manipulated variable is based on a first correction element, which is implemented by a first elementary controller, which takes into account a first time interval between the processing of the substrate (102) by means of the first process (P1) and the detection (101) of the substrate (102); and • wherein the control (103) of the process group by means of the second manipulated variable is based on a second correction element, which is implemented by a second elementary controller, which takes into account a second time interval between the processing of the substrate (102) by means of the second process (P2) and the detection (101) of the substrate (102); • where the first elementary controller and the second elementary controller operate independently of each other.

Description

[0001] Various embodiments relate to a method, a control device and a storage medium.

[0002] In general, a substrate, for example a glass substrate, a metal substrate and / or a polymer substrate, can be processed, e.g. coated, so that the chemical and / or physical properties of the substrate can be changed. By means of a coating system, a substrate can be processed in a chronological sequence of processes. The processes could, for example, include pretreatment, one or more coating processes and / or an annealing step. Various coating methods can be carried out to coat a substrate. For example, a vacuum coating system can be used to deposit one or more layers on one substrate or on several substrates by means of chemical and / or physical vapor deposition.

[0003] The processes are carried out one after the other in a specified time sequence. After processing is complete, the resulting product properties, such as color or sheet resistance, are measured (also known as ex-situ measurement). For further information, see DE 10 2011 008 047 A1.

[0004] According to various embodiments, it has been recognized that this results in a time delay (also referred to as substrate runtime) for each individual process between the processes or the completion of processing and the measurement. For example, in an architectural glass coating system, this time delay can range from 600 seconds (first process) to 60 seconds (last process). Within this range, for example, a number of 1 to 10 processed substrates can be processed, after which a process setting becomes visible for the first time in the measurement result.

[0005] In this context, it was recognized that deviations in product quality from the target state are difficult to compensate for, for example, when a human operator is required to evaluate the measurement result and make appropriate adjustments. This assessment is then carried out based on empirical values, pre-calculated tables, or even with special tools, such as software or professional configuration of the coating system.

[0006] According to various embodiments, a method, a control device, and a storage medium are provided that facilitate more precise modification of the operating points of individual processes. For this purpose, a closed loop (also referred to as a closed loop) can be provided, which performs the adjustment based on the measurement result, e.g., completely automatically.

[0007] To illustrate, the individual processes are deduced based on the measurement of the product properties (also referred to as the actual state of the substrate). The result is a list of process parameters, each of which is uniquely assigned to a process. The overall result is controlled by several independently operating elementary controllers. Each of these elementary controllers optimizes one or more process parameters per process. This ensures that the elementary controller only considers one delay time, which simplifies its implementation. The delay time can be specified for the elementary controller, for example, stored in its memory.

[0008] The method provided enables, for example, the cost-effective retrofitting of an existing coating system or, more generally, an existing processing system. For example, the existing infrastructure for measuring, such as sensors, data lines, and for controlling the process or its actuators, can be retained unchanged. This eliminates the need to perform measurements after each process, thus reducing the number of sensors required.

[0009] It shows Fig. 1 shows a method according to various embodiments in a schematic control diagram; Fig. 2 and Fig. 3 shows the method according to various embodiments in a schematic control diagram; Fig. 4, Fig. 5 and Fig. 6 each show the method according to various embodiments in different schematic diagrams; Fig. 7 and Fig. 8 shows the method according to various embodiments in a schematic process diagram; Fig. 9 shows a processing device according to various embodiments in a schematic construction diagram; and Fig. 10 and Fig. 11 each shows a vector space according to various embodiments in a schematic cross-sectional view.

[0010] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. In this regard, directional terminology such as "top," "bottom," "front," "back," "fore," "rear," etc., will be used with reference to the orientation of the described figure(s). Since components of embodiments can be positioned in a number of different orientations, the directional terminology is for purposes of illustration and is in no way limiting. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention.It is understood that the features of the various exemplary embodiments described herein may be combined with one another unless specifically stated otherwise. The following detailed description is therefore not to be construed in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0011] In this description, the terms "connected," "attached," and "coupled" are used to describe both a direct and an indirect connection (e.g., resistive and / or electrically conductive, e.g., an electrically conductive connection), a direct or indirect connection, and a direct or indirect coupling. In the figures, identical or similar elements are provided with identical reference numerals where appropriate.

[0012] Control can be understood as the intentional influencing of a system. The current state of the system (also known as the actual state) can be changed according to a specification (also known as the target state). Regulation can be understood as control, whereby a change in the state of the system due to disturbances is also counteracted. Illustratively, the control system can have a forward-facing control path and thus implement a sequential control that converts an input variable (e.g. the specification) into an output variable. However, the control path can also be part of a control loop, thus implementing a closed-loop control. In contrast to pure forward-facing sequential control, the closed-loop control has a continuous influence of the output variable on the input variable, which is brought about by the control loop (also known as feedback).In other words, closed-loop control can be used alternatively or in addition to open-loop control, or closed-loop control can take place alternatively or in addition to open-loop control. The state of the system (also referred to as the operating point) can be represented by one or more controlled variables of the system, the actual value of which represents the actual state of the system and the setpoint value of which (also referred to as the reference value) represents the setpoint state of the system. In closed-loop control, the actual state of the system (e.g. determined based on a measurement) is compared with the setpoint state of the system, and the one or more controlled variables are influenced by means of a corresponding manipulated variable (using an actuator) in such a way that the deviation of the actual state from the setpoint state of the system is minimized. The manipulated variable (also referred to as the variable to be manipulated) can refer to the variable that is changed by controlling the actuator.

[0013] The system to be controlled can, for example, have one or more coating processes. The state of the system can thus represent the state of each coating process in the system, and vice versa. The actual state of each coating process can be detected by means of a measuring element (e.g., having one or more sensors). One or more sensors can be used for each controlled variable, each sensor configured to detect a variable (also referred to as a measured variable) that represents the controlled variable or that is the controlled variable.

[0014] A sensor (also called a detector) can be understood as a transducer that is designed to detect a property of its environment (e.g. qualitatively or quantitatively) that corresponds to the sensor type as a measurand, e.g. a physical property, a chemical property and / or a material condition. The measurand is the physical quantity (also called the controlled variable) to which the measurement is applied by the sensor. Depending on the complexity of the environment to be measured by the sensor, the sensor can be designed to distinguish between two or more discrete states of the measurand (also called a measuring switch), or to detect the measurand quantitatively. An example of a quantitatively detected measurand is a gas inflow rate (e.g. detected as a flow rate), the actual state of which can be converted into a measured value by the sensor.

[0015] Each sensor can be part of a measuring chain, which has a corresponding infrastructure (e.g., a processor, storage medium, and / or bus system, and the like). The measuring chain can be configured to control the corresponding sensor (e.g., a gas sensor, pressure sensor, and / or voltage sensor), process its detected measurement value as an input value, and, based on this, provide an electrical signal as an output value that represents the detected input value. For example, the output value can indicate the measured value. The measuring chain can be implemented, for example, by means of a so-called control device.

[0016] In the following, reference is made, among other things, to reactive coating processes in which a chemical reaction takes place. For example, one or more of the coating processes may comprise a reactive sputtering process which is provided with one or more reactive gases (e.g., m reactive gases) which form a chemical bond with the coating material (also referred to as target material) deposited on a substrate (where, for example, m=1, m>1 or m>2). It can be understood that the coating process does not necessarily have to be reactive. The quantities which represent materials involved in the chemical reaction in a reactive coating process may, in a non-reactive coating process, represent corresponding other materials which are not involved in a chemical reaction.Accordingly, what is described for the reactive coating process can apply analogously to a non-reactive coating process and vice versa.

[0017] According to various embodiments, reference is made to physical vapor deposition (PVD) as an exemplary coating process, e.g., comprising a sputtering process. It can be understood that what is described for PVD can apply analogously to chemical vapor deposition (CVD). In contrast to CVD, in PVD a solid material is first transferred into the gas phase (also referred to as the gaseous phase or vapor), and a layer is formed using this gas phase. In PVD, the gas phase of the target material can optionally be chemically reacted with a reactive gas to form a chemical compound, which is incorporated into the layer or forms it. During the chemical reaction in PVD, two or more materials are thus combined to form the chemical compound.

[0018] In chemical vapor deposition, a gaseous starting compound (also called precursor or reactant) is split into at least two reaction products, of which at least one reaction product is incorporated into the layer. Optionally, one reaction product is removed from the coating process as excess (e.g., using a pump). Optionally, CVD can be performed using a plasma, in which the splitting of the precursor occurs.

[0019] A plasma can be formed using a so-called working gas (also referred to as a plasma-forming gas). According to various embodiments, the working gas can comprise a gaseous material that is inert, in other words, that participates in few or no chemical reactions. A working gas can, for example, be defined by the target material used and be adapted to it. For example, a working gas can comprise a gas or a gas mixture that does not react with the target material to form a solid. The working gas can, for example, comprise a noble gas (e.g., helium, neon, argon, krypton, xenon, radon) or several noble gases. The plasma can be formed from the working gas, which, for example, essentially causes the sputtering of the target material. If a reactive gas is used, this can have a higher chemical reactivity than the working gas, e.g.,with respect to the target material. In other words, the atomized target material can react more quickly with the reactive gas (if present) (i.e., form more reaction product per unit time) than with the working gas (e.g., if it reacts chemically with the working gas at all). The reactive gas and the working gas can be supplied together or separately as a process gas (e.g., as a gas mixture), for example, by means of the gas supply device.

[0020] The reactive gas can comprise a gaseous material that reacts with the target material (e.g. with the sputtered target material) and / or can be incorporated into the deposited layer by means of a chemical reaction. If, for example, a target material is used that can form a nitride (e.g. aluminum nitride), or if a nitride of the target material is to be deposited, the reactive gas can comprise nitrogen or be formed from it. If, for example, a target material is used that can form an oxide (e.g. aluminum oxide), or if an oxide of the target material is to be deposited, the reactive gas can comprise oxygen or be formed from it. The reactive gas can, for example, comprise a gas mixture (reactive gas mixture) of several gases or be formed from them, which react with the target material and / or the deposited layer, e.g. oxygen and nitrogen, e.g. when an oxynitride (e.g.Aluminum oxynitride) is to be deposited. The reactive gas mixture may, for example, comprise predominantly (i.e., more than 50%) oxygen, e.g., for depositing an oxide or oxynitride. Examples of the reactive gas include: molecular oxygen, molecular nitrogen, nitric oxide, carbon oxide, hydrogen sulfide, methane, gaseous hydrocarbon, molecular fluorine, molecular chlorine, ozone, or another gaseous material.

[0021] In the following, reference is made to a method which comprises controlling one or more than one actuator (also referred to as control intervention). In this context, reference is also made to a control device or code segments. The control device (also referred to as a regulating device) can be configured to implement one or more than one of the methods described herein. For this purpose, the control device can have a processor which is configured to implement the respective method. For example, the processor can be configured to output corresponding instructions for controlling. Alternatively or additionally, the processor can be configured to receive and process corresponding instructions and signals. The instructions received by the processor can, for example, be implemented by means of code segments which are stored in a non-volatile data memory.For example, the code segments may include at least instructions and / or one or more than one specification that, when executed by the processor, cause the processor to perform one of the methods.

[0022] The term "control device" can be understood as any type of logic-implementing entity, which can, for example, comprise a processor (and, for example, corresponding circuitry) that can, for example, execute software stored in a storage medium, in firmware, or in a combination thereof, and issue instructions based thereon. The control device can, for example, be configured using code segments (e.g., software). The control device can, for example, comprise or be formed from a programmable logic controller (PLC).

[0023] According to various embodiments, a data memory (more generally also referred to as a storage medium) may be a non-volatile data memory. The data memory may, for example, comprise or be formed from a hard disk and / or at least one semiconductor memory (such as read-only memory, random access memory and / or flash memory). The read-only memory may, for example, be an erasable programmable read-only memory (may also be referred to as EPROM). The random access memory may be a non-volatile random access memory (may also be referred to as NVRAM - "non-volatile random access memory"). For example, one or more of the following can be stored in the data memory: the code segments representing the method, one or more control elements, one or more tuning parameters, one or more model parameters, one or more calibration parameters.

[0024] The term "processor" can be understood as any type of entity that allows the processing of data or signals. The data or signals can, for example, be processed according to at least one (i.e., one or more) specific function performed by the processor. A processor can include or be formed from an analog circuit, a digital circuit, a mixed-signal circuit, a logic circuit, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an integrated circuit, or any combination thereof.Any other type of implementation of the respective functions, which are described in more detail below, can also be understood as a processor or logic circuit, for example virtual processors (or a virtual machine) or a plurality of decentralized processors, which are connected to one another, for example by means of a network, are spatially distributed in any way and / or have any share in the implementation of the respective functions (e.g. distribution of the computing load among the processors). The same generally applies to differently implemented logic for implementing the respective functions. It is understood that one or more of the method steps described in detail herein can be carried out (e.g. realized) by a processor, by one or more specific functions carried out by the processor.

[0025] A “controller” can be understood here as a processor that implements the control functions, e.g. by converting information about the actual state of a process into signals for controlling the process. The signals can be used, for example, to control one or more actuators that supply the process. The processor can, for example, be set up as a controller using one or more control elements (e.g. via software) or can provide the control functions at least partially in a hard-wired manner. For example, a control device can have the processor set up as a controller. It can therefore be understood that what has been explained with regard to the controller can apply analogously to the one or more control elements and vice versa.

[0026] At least one control element (also referred to as a correction element) can be configured to take into account a time period (also referred to as the substrate runtime) between the processing of the substrate by means of a process and the detection of the substrate. The correction element can, for example, be configured to take into account a duration of the time period or control interventions occurring within the time period.

[0027] Here, several control elements are explained in a more easily understandable parallel structure. For example, the control elements can operate independently of each other. However, it should be understood that the control elements explained can also be implemented together (e.g., in the so-called series structure), e.g., using a common transfer function.

[0028] The term "actuator" (e.g., having an actuator) can be understood as a transducer configured to influence a state, a process (e.g., a coating process), or a device in response to the actuation of the actuator. The actuator can convert a control signal supplied to it (by means of which the actuation occurs) into mechanical movements or changes in physical quantities such as pressure or temperature. An electromechanical actuator, for example, can be configured to convert electrical energy into mechanical energy (e.g., through movement) in response to the actuation.

[0029] A control element can be configured to influence the actual state (also referred to as the operating point) of the process (e.g., its manipulated variable), which is supplied by the control element. This influence can be direct or indirect. For example, the manipulated variable and the controlled variable can differ from each other. The controlled variable (e.g., pressure) can then be a function of one or more manipulated variables (e.g., gas flow).

[0030] For example, the actuator can change an electrical voltage as a manipulated variable by means of which a plasma is supplied, so that as a result an atomization rate or a layer thickness is changed as a controlled variable. For example, the actuator can change an inflow rate of a gas as a manipulated variable, so that as a result a pressure is changed as a controlled variable. For example, the actuator can change an inflow rate of a working gas as a manipulated variable, so that as a result an atomization rate or a layer thickness is changed as a controlled variable. For example, the actuator can change an inflow rate of a reactive gas as a manipulated variable, so that as a result a chemical composition of the plasma or a coating is changed as a controlled variable.

[0031] Examples of components of an actuator include: a voltage source (if present), a valve (e.g. a pump arrangement and / or gas supply device), a motor (e.g. a valve or a pump), or the like. The voltage source can, for example, be configured to generate one or more voltages according to the control and supply them to the coating process. The pump arrangement can, for example, be configured to pump out one or more gases according to the control and thus remove them from the coating process. The gas supply device can, for example, be configured to supply one or more gases to the coating process according to the control (also referred to as gas supply). The supplied gas can have a gas inflow rate (i.e. gas inflow per time). The gas inflow can, for example, be a standard volume flow supplied to the coating process.

[0032] With regard to the control of an actuator, reference is made, among other things, to the more easily understood manipulated variable or its control value, which is influenced by the actuator. What has been described here can apply analogously to the controlled variable or its control value, which are fed to the actuator for control, and vice versa. The actuator acts as a converter, converting a control signal into the manipulated variable or its control value, so that the control value is a function of the control value. Modern actuators, for example, are provided as a complex assembly comprising an actuator and its own control device (also referred to as an actuator control device). The actuator control device can be configured to receive the control value as input using the control signal and to control the actuator according to the control value.The generation and transmission of the control variable then takes place within the actuator, so that the manipulated variable is fed to the actuator. Less complex actuators can only process the control variable as a control signal, so the control variable is fed to them for control.

[0033] More generally, an actuator can be controlled using a control signal, where the control signal can represent the manipulated variable or its control value and / or the controlled variable or its control value. Alternatively or additionally, the control signal can contain instructions that specify how the manipulated variable or its control value is to be changed (e.g., its relative change).

[0034] The term “process parameter” (also referred to as operating point parameter) refers to a physical parameter of a process, e.g. representing its operating point and / or a controlled variable of the process. The process parameter can be a detectable parameter, but does not necessarily have to be detected. For example, the process parameter can be a parameter that is difficult to detect, e.g. not without impairing the processing of a substrate by means of the process. According to various embodiments, the process parameter can be a mathematically determined parameter, e.g. determined indirectly based on a result of the process. When determining the process parameter, for example, it can be based on at least one additional piece of information about the process, e.g. its duration. The process parameter can, for example, be a coating rate that is determined based on the measured layer thickness and the known coating duration.

[0035] Fig. 1 illustrates a method 100 according to various embodiments in a schematic control diagram. The method 100 can be configured to operate a process group that performs two or more processes (also referred to as coating processes), e.g., at least a first process and a second process. For example, the process group can perform several (e.g., N) processes (where, for example, N>1, N>5, or N>10). In the following, with regard to the method 100, reference is made, among other things, to two coating processes as exemplary processes for the sake of easier understanding. It can be understood that what is described for the two coating processes can apply analogously to more than two processes or can equally apply to processes of a different type.

[0036] The process group can have one or more than one (e.g., N or more) processing devices, each of which is configured to perform one or more processes. For example, the process group can be configured to process (also referred to as "process") multiple substrates (e.g., N or more) in parallel, e.g., one substrate per processing device. If the processing device is configured to perform a coating process, it is also referred to as a coating device. The actual state of the substrate can be used as the controlled variable(s) of the process group.

[0037] The multiple coating processes may, for example, be identical in their coating material, their reactive gas, and / or the type of coating device (e.g., thermal or sputtering). The or each coating process may, according to various embodiments, be configured to coat at least one substrate (ie, one substrate or multiple substrates).

[0038] The method 100 may include, in 101, detecting (also referred to simply as measuring 101) a substrate 102 that was processed by the first process 152a (also referred to simply as process step A or P1) and subsequently by the second process 152b (also referred to simply as process step B or P2). By detecting the substrate 102, the actual state of the substrate is determined (e.g., as a controlled variable of the process group).

[0039] Measuring 101 may include detecting the substrate using a measuring element (e.g., comprising one or more than one sensor). The or each sensor may be part of the measuring chain 112, which provides an electrical signal as output variable 112o (herein also referred to as result 112o of the detection) based on one or more than one measured variable as input variable 112a. The measuring chain may be implemented, for example, by means of a control device 224.

[0040] Examples of the measured variable can include: a sheet resistance of the substrate 102, a layer thickness of the substrate 102, an optical spectrum of the substrate (e.g. in transmission and / or reflection) 102, a transmittance of the substrate 102, a reflectance of the substrate 102. The measured variable can, for example, be related to a coating of the substrate. The reflectance or transmittance can be related to a reference wavelength, e.g. 400 nm or 500 nm. Examples of corresponding measuring elements can include: a sheet resistance measuring element (e.g. having one or more than one voltage sensor or induction sensor) for measuring a sheet resistance, a layer thickness measuring element (e.g. having one or more than one interferometer) for measuring a layer thickness, an optoelectronic measuring element (e.g. having one or more than one CCD sensor) for detecting electromagnetic radiation.

[0041] The method 100 may include, in 103, controlling the process group using a first manipulated variable 114a of the first process and using a second manipulated variable 114b of the second process based on a result of the detection. Control 103 may include controlling a first actuator configured to influence the first manipulated variable and controlling a second actuator configured to influence the second manipulated variable.

[0042] The first process can, for example, be controlled (e.g., supplied) by the first actuator. More generally, the first actuator can be configured to influence the operating point of the first process, e.g., its first manipulated variable. The second process can, for example, be controlled (e.g., supplied) by the second actuator. More generally, the second actuator can be configured to influence the operating point of the second process, e.g., its second manipulated variable.

[0043] Examples of the first manipulated variable 114a include: an electrical power (e.g., by means of which the first process is supplied), a fluid volume flow (e.g., by means of which the first process is supplied), a pressure (e.g., to which the first process is exposed), a chemical composition that is supplied to the first process or to which the first process is exposed, a magnetic flux density to which the first process is exposed. The fluid volume flow can, for example, relate to a fluid (e.g., a gas) that is supplied to and / or removed from the first process.

[0044] Examples of the second manipulated variable 114b include: an electrical power (e.g., by means of which the second process is supplied), a fluid volume flow (e.g., by means of which the second process is supplied), a pressure (e.g., to which the second process is exposed), a chemical composition that is supplied to the first process or to which the first process is exposed, a magnetic flux density to which the first process is exposed. The fluid volume flow can, for example, relate to a fluid (e.g., a gas) that is supplied to and / or removed from the second process.

[0045] The control of the process group by means of the first manipulated variable 114a can be based on a first correction element that takes into account a first time period 111a (also referred to as the first substrate runtime, first time delay 111a, or time delay 111a of the first process) between the processing 152a of the substrate by means of the first process and the measurement 102. The first time period 111a can have a duration that elapses from the end of the processing 152a of the substrate by means of the first process to the end of the measurement 102.

[0046] The control of the process group by means of the second manipulated variable 114b can be based on a second correction element that takes into account a second time period 111b (also referred to as the second substrate runtime, second time delay 111b, or time delay 111a of the second process) between the processing 152b of the substrate by means of the first process and the measuring 102. The second time period 111b can have a duration that elapses from the end of the processing 152b of the substrate by means of the second process to the end of the measuring 102.

[0047] The first time period 111a and the second time period 111b may differ from each other, e.g., in their duration and / or their start time. The first time period 111a and the second time period 111b may coincide in their end time (the time of measurement 101).

[0048] Optionally, the measurement 102 may include generating a timestamp representing the time of the measurement 102 (also referred to as the measurement time). Optionally, the measurement 102 may include generating a substrate identifier representing the substrate that was detected at the measurement time.

[0049] The control device 224 can, for example, be configured to carry out the control 103 (then also referred to simply as controller 224).

[0050] In principle, the controller 224 can (but does not necessarily have to) be configured such that the control 103 using the first control variable and the control 103 using the second control variable are carried out separately and / or independently of one another, e.g., using so-called elementary controllers 224a, 224b (illustratively providing an individual control loop). For example, several elementary controllers 224a, 224b can operate independently of one another. For example, the two elementary controllers can each be dedicated components of the controller 224.

[0051] For example, each elementary controller can be configured to control exactly one process of the process group, and / or each process can be assigned to exactly one elementary controller that is configured to control the process.

[0052] A first elementary controller 224a can be configured to perform the control 103 using the first control variable, e.g., by controlling the first actuator. A second elementary controller 224b can be configured to perform the control 103 using the second control variable, e.g., by controlling the second actuator.

[0053] Such an elementary controller can be implemented using a programmable logic controller (PLC), which can potentially even implement multiple such elementary controllers. A so-called PID controller (proportional-integral-derivative) used for this purpose feeds back the control deviation using a P control element (proportional control element), an I control element (integral control element), and / or a D control element (derivative control element). More generally, the elementary controller can have one or more control elements.

[0054] An exemplary implementation according to embodiments 100 is explained below. A dimensionless deviation from the target state is determined as an input variable for the control 103, which is referred to herein as the target state deviation x (e.g., a layer defect). If the target state deviation x = 0, the deviation from the target state is equal to zero, or the actual state of the substrate is equal to the target state of the substrate (e.g., equal to the state of a reference product). If x ≠ 0, there is a deviation from the target state, e.g., a quantized deviation from the target state. What has been described for the target state deviation can also apply analogously to a differently defined input variable of the control 103.

[0055] The target state deviation can be determined, for example, on the basis of the recorded actual state of the substrate and / or a process characteristic value derived therefrom, as will be described in more detail later.

[0056] Based on a coating rate as a process parameter, the target state deviation can be determined, for example, according to the following relationship: x:=(RRRef−1), where x is the target deviation, R is the determined actual coating rate, and R ref the target coating rate (e.g., which leads to the reference product). In this case, the target state deviation corresponds to a relative target value deviation, e.g., the percentage deviation from the target state.

[0057] Based on a color index of the substrate as a recorded quantity (also called a measured quantity), the target state deviation can be determined, for example, according to the following relation: x:=c⋅(a*−aref*), where c is a constant, a* is the determined actual color index according to the Lab color space (also known as CIELAB) and a* refthe target color index according to the Lab color space. In this case, the target state deviation corresponds to an absolute target value deviation, e.g., the distance from the target state. The constant c can be freely chosen. It is advantageous to choose c such that x again corresponds to a percentage change in layer thickness. The same can apply analogously for another point on the a*b* coordinate plane or for another color space. This can be advantageous, for example, if the corresponding color index is only influenced by a single process in the process group. By controlling this process, the desired value of the color index can be stabilized. This enables a uniform interpretation of values ​​obtained by different methods, the target state deviation.

[0058] Based on a refractive index of the substrate as a measured quantity, the target state deviation can represent an absolute refractive index deviation in analogy to the color index.

[0059] Just like the setpoint deviation, the control output Δy can also be noted as a dimensionless quantity, which is then converted into a physical control variable.

[0060] The control output Δy is represented as a relative scaling or shift of a control value. For example, a relative change in process performance could be calculated from this: Pneu=Pist⋅expΔy Thus, the method 100 can also be applied, for example, to several related processes with different actual states.

[0061] In the case of the control output Δy, the control can be based on an instantaneous change Δy. In the following, reference is made, among other things, to the more descriptive process setting y. The description for the process setting y (also referred to as the operating point) can be used analogously for the control output Δy = f(y).

[0062] Likewise, reference is made below to a vectorial notation of the quantities used, where the vector components each denote measured variables, parameters or manipulated variables used in parallel. This facilitates understanding of the case where the process group (e.g. its operating point) cannot be easily described by a single number, e.g. when the control 103 is applied to several (different) target values ​​(also referred to as setpoints). If, for example, the measurement 101 has to record the substrate in parallel at several points on the substrate, then instead of a one-dimensional (e.g. scalar) actual state of the substrate, a tuple of measured values ​​is obtained as the actual state (also referred to as a multi-component actual state) of the substrate, which can be notated as components of a vector. As a result, the target state deviation can also be represented as a vector x→ (also called deviation vector), which contains individual deviation values ​​x as components i , i = 1 ... N i (also called error values).

[0063] The following section explains how to control a scalar setpoint deviation using a single elementary controller. A generalization of the scalar setpoint deviation to a vector setpoint deviation will be explained later. The description for the individual elementary controller can apply analogously to multiple elementary controllers.

[0064] Fig. 2 illustrates the method 100 according to various embodiments 200 in a schematic control diagram, wherein an exemplary implementation (also referred to as feedback correction) of the correction element 202 is explained using the first elementary controller 224a. It can be understood that what is described for the first elementary controller 224a can apply analogously to the second elementary controller 224b or one or more additional elementary controllers 224b, to a differently configured control device 224 by means of which the method is carried out, or to a differently implemented method 100.

[0065] The first elementary controller 224a can be configured to process the measurement result 112o (also referred to as measurement result 112o). The measurement result 112o can, for example, comprise a first process characteristic value 212a, which is fed to the first elementary controller 224a. The process characteristic value can clearly represent the virtual (e.g., difficult or impossible to detect) controlled variable of the corresponding process, which is not measured but determined based on the actual state of the substrate, as will be described in more detail later.

[0066] The elementary controller 124 can implement the correction element 202. Each elementary controller 124 can implement its own correction element 202. For example, two elementary controllers 124 or their correction elements 202 can differ from one another. Each correction element 202 can, for example, be assigned (e.g., only) to one process of the process group and be configured to take into account the time span of the process to which the correction element 202 is assigned. The correction element 202 can accordingly take into account the time delay of the process (also referred to as the substrate runtime) to which the correction element 202 is assigned.

[0067] The elementary controller 124 may further implement one or more control elements 210, which are connected in parallel with the correction element 202 according to the feedback correction. The one or more control elements may include or be formed from a P-control element, an I-control element, and / or a D-control element. For example, the one or more control elements may include two or more different control elements.

[0068] The output 222o of the correction element 202 (also referred to as correction or in formulas as Δx̂) can be superimposed on the measurement result 112o, which is supplied to the or each control element 210. This ensures that the measurement result 112o supplied to the or each control element 210, e.g., the first process characteristic value 212a, is corrected based on the time delay of the process. As a result of the superimposition, for example, a corrected measurement result 212o is provided, which is based on the measurement result 112o and the correction 222o. The corrected measurement result 212o can, for example, comprise a corrected process characteristic value that is supplied to the or each control element 210.

[0069] The correction element 202 may be configured to provide the correction 222o based on the time delay of the process and on the basis of an output of the or each control element 210. For example, the correction element 202 may feed back the output of the or each control element 210 taking into account the time delay of the process.

[0070] As an output of the one or more than one control element 210 (which provides the control output of the feedback correction), a control value can be obtained, according to which the first control element, which is configured to influence the first control variable 114a of the first process, is controlled.

[0071] An exemplary implementation according to embodiments 200 is explained below.

[0072] The elementary controller 224a, for example, has only the integral element and then operates as a pure integral controller. This ensures that a single detected deviation leads to a permanent correction of the control output. The elementary controller 224a stores output corrections 222o and the output time in order to take into account the runtime effects between the control intervention 103 (also referred to as control intervention) and the measurement 101 using a predictor (e.g., a Smith predictor).

[0073] The control 103 comprises, for example: input of the target state deviation (from previous calculation); a prediction (also referred to as output of the predictor), which comprises taking into account the sum of the corrections already output but not yet effective for the measurement in order to calculate a virtual target state deviation x' from the target state deviation x; determination of an unlimited control output Δy uby multiplying the deviation vector by a proportional factor; checking the unlimited control output for violation of one or more limit values ​​(e.g., control limits and / or rate limit) to obtain the limited control output Δy; optional application of a watchdog to detect instability using Δy and the configured rate of change limit; converting the dimensionless value Δy into a change to be made to the operating point (e.g., into a control variable). The virtual target state deviation x' clearly represents the expected measured value using the corrections already made.

[0074] Exemplary implementations of models for determining the target state deviation according to embodiments 200 are explained below.

[0075] Determining the target state deviation may, for example, involve using a spectral model (also known as singular value decomposition or SVD). The output of the singular value decomposition then directly corresponds to the target state deviation.

[0076] Determining the target state deviation may, for example, involve using so-called thin-film simulation software (e.g., having a neurocognitive model of number processing). The thin-film simulation software may implement a fit parameter f with a reference value f ref to determine the target state deviation, for example according to the following relation: x:=ffref−1.

[0077] Determining the target state deviation may, for example, involve using a scalar model to determine the target state deviation, for example according to the following relation: x:=∑ci(ai−ai,ref), where the index i refers to any scalar characteristic values, such as a*, b*. For example, the target state deviation can be derived from a single characteristic value or from a linear combination of characteristic values.

[0078] Determining the target state deviation may, for example, involve using a resistance model (also referred to as resistance model) to determine the target state deviation, for example according to the following relation: x:=ρRsq1dref−1, where ρ is the measured resistivity of the layer, d ref the target thickness of the layer and R sq the target resistance of the layer.

[0079] Optionally, the target state deviation (e.g. deviation of the layer thickness) can be scaled in such a way that it corresponds to the relative deviation of the actual coating rate from the target state of the coating rate (also referred to as the target coating rate).

[0080] Fig. 3 illustrates the method 100 according to various embodiments 300 in a schematic control diagram, wherein an exemplary implementation of the correction element (also referred to as parallel correction) is explained using the first elementary controller 224a.

[0081] According to the parallel correction, the output 222o of the correction element 202 (also referred to as correction) can be superimposed on the output of the or each control element 210 to obtain the control output. This achieves that the output provided by the or each control element 210 is corrected based on the time delay of the process. As a result 312o of the superposition, for example, a corrected output 312o is provided, which is based on the measurement result 112o and the correction 222o.

[0082] The result 312o of the superposition (which provides the control output of the parallel correction) can be used to obtain a control value according to which the first actuator, which is configured to influence the first control variable 114a of the first process, is controlled.

[0083] The correction element 202 may be configured to provide the correction 222o based on the time delay of the process and on the output of the or each control element 210. For example, the correction element 202 may correct the output of the or each control element 210 taking into account the time delay of the process.

[0084] Fig. 4 illustrates the method 100 according to various embodiments 400 in a schematic diagram when the time delay is not taken into account; and Fig. 5 illustrates the method 100 according to various embodiments 500 in a schematic diagram when the time delay is taken into account.

[0085] In the time period between processing the substrate by the process and detecting 101 the substrate 102 (also referred to as a time delay), multiple control interventions can occur, each control intervention comprising the regulation 101 of the process group (e.g., one or more processes of the process group). After processing the substrate, one or more control interventions can occur until the detection 101 of the substrate 102 occurs.

[0086] For example, a frequency a / t=1 / ΔT of the control interventions (in number “a” per time “t”) can be greater than the reciprocal of the time delay T n of the n-th process (e.g., the first and / or the second process). Alternatively or additionally, a frequency a / t=1 / ΔT with which a substrate is detected 101 may be greater than the reciprocal of the time delay T n of the nth process (e.g. the first and / or the second process).

[0087] The time interval ΔT here clearly describes the time interval between immediately consecutive events in which a measurement or a control takes place (then also referred to as a measurement event or control intervention).

[0088] The processing of the substrate by means of the processes takes place in a fixed time sequence, which results from the position s n of the n-th process along the path (also referred to as transport path) along which the substrate 102 is transported, and the speed v (also referred to as transport speed) at which the substrate 102 is transported. By way of example, the first process (i.e., n=1) is completed at t1=v·s1 and the second process (i.e., n=2) is completed at t2=v·s2. The time interval ΔT can, for example, satisfy the relation ΔT≥t1-t2. For example, the control 103 and / or the measurement 101 can be carried out at the clock rate 1 / ΔT, according to which the substrates are transported.

[0089] The duration ΔT and / or the duration t1-t2 may, for example, be in a range from about 5 seconds to about 30 seconds.

[0090] For example, 1 / ΔT > p / T n , where the scalar p can be, for example, greater than 2, e.g. than 3, e.g. than 4, e.g. than 5, e.g. than 6, e.g. than 7, e.g. than 8, e.g. than 9, e.g. than 10, e.g. than 20, e.g. than 50, e.g. than 100, e.g. than 200, e.g. than 1000.

[0091] As can be seen, the time delay causes an iteratively delayed effect of the control intervention, which only becomes visible after the time delay has elapsed, whereby at this point in time (“now”) further control interventions have already taken place, which subsequently lead to an overreaction.

[0092] As can be seen, with each control intervention Δy unot only the measurement result x, but also a correction Δx̂ is taken into account. The correction Δx̂ can be a function of the time delay. For example, Δy u = K · (x + Δx̂). The same can be applied analogously to the parallel correction, where Δy u = K · x + Δx̂.

[0093] This ensures that the control intervention (also referred to as a control event) already takes into account that its effect is only reflected in the measurement result after the time period has elapsed.

[0094] Fig. 6 illustrates the method 100 according to various embodiments 600 in schematic diagrams comparing when the time delay is ignored (in diagram 500) and when the time delay is taken into account (in diagram 600). Clearly, taking the time delay into account achieves a prediction about the measurement result as a result of processing the substrate by the process.

[0095] Without taking the time delay into account (in diagram 500), the same setpoint deviation leads to the same change in the manipulated variable.

[0096] Taking the time delay into account (in diagram 600), the same setpoint deviation leads to different changes in the manipulated variable, e.g., as a function of what portion of the substrate runtime has already elapsed. A clear memory of the control interventions already made is provided.

[0097] Further exemplary implementations of the correction element are explained below.

[0098] Fig. 7 illustrates the method 100 according to various embodiments 700 in a schematic process diagram.

[0099] The process group may comprise a plurality of processing devices, of which the n-th processing device is configured to process the n-th process P n(1≤n≤N). Examples of a process include: a pretreatment, one or more coating processes, and / or an annealing step.

[0100] The plurality of processing devices may be arranged in a vacuum chamber 802. The substrate 102 may be transported along a path 111 past each of the plurality of processing devices at a speed v.

[0101] The processing of the substrate by means of the processes takes place in a fixed time sequence, which results from the position s n of the n-th process along the path 111 (also referred to as transport path) along which the substrate 102 is transported, and the speed v (also referred to as transport speed) at which the substrate 102 is transported. By way of example, the first process (ie n=1) is at s1=t1 / v, the second process (ie n=2) is at s2=t2 / v and the last process (ie n=N) is at s N=t N / v arranged.

[0102] After the substrate has been processed using the N processes of the process group, the substrate 102 is measured 101 (also referred to as ex-situ measurement). This can be used to measure the achieved product properties, such as color and / or sheet resistance. Optionally, after the substrate 102 has been measured 101, the substrate can be processed using one or more additional processes. For example, one or more measuring elements can record the actual state of the substrate in a state partially processed using the N processes (also referred to as in-situ measurement).

[0103] As a result, there is a time delay between the nth process and measurement 101. For example, in an architectural glass coating system, this time delay can range from approximately 600 seconds (first process) to approximately 60 seconds (Nth process). This could, for example, correspond to 10 processed substrates being processed using the first process before measurement 101 of the first of the 10 processed substrates takes place (in other words, before the result of the first process becomes visible in the measurement result).

[0104] According to various embodiments, the control variables of the individual processes can be adjusted using a control loop (also referred to as a closed-loop controller) to minimize the deviation of the actual state of the coating properties from a target state of the coating properties. For example, the layer thickness of the coating processes could be influenced to achieve a defined color impression.

[0105] The closed loop (also called a closed loop) ensures that human-performed actions are not absolutely necessary. For example, the evaluation of measurement data, the calculation of appropriate corrective actions, and the execution of physical adjustments can be fully automated. Operator control and intervention options are, of course, optional, but not mandatory.

[0106] According to various embodiments, method 100 achieves simultaneous control of all relevant processes with the shortest possible settling times. Depending on the process and actuator, the time delay (also referred to as delay time) between control intervention and measurement of the product properties can vary and be taken into account.

[0107] The method 100 can be configured such that the controller's adjustments can cooperate with other control systems. For example, an operator can manually make adjustments, such as balancing the power of multiple coating sources. As a second example, another process automation system could adjust the coating rate and substrate feed rate in parallel to maximize the current system throughput.

[0108] The control 103 and / or the measurement 101 can be performed discretely in time. For example, the control 103 can involve controlling the actuator at intervals of t=ΔT (also referred to as a control intervention). Alternatively or additionally, the measurement 101 can be performed at intervals of t=ΔT. For example, each control intervention can be initiated by the supply of the measurement result (triggered by the arrival of measurement data).

[0109] If measurement 101 is performed at multiple locations (e.g., using multiple sensor stations) within the system, e.g., ex-situ and in-situ, the method described here can be applied to each of these sensor stations as an independent sub-controller. Each sub-controller then operates in sync with the respective measurement.

[0110] In a first exemplary embodiment, the processing of a first substrate takes place (or ends) by means of the first process P1 at time t1 and by means of the second process P2 at time t2. When processing the first substrate, the process group can be at a first operating point A1. Subsequently, the measurement 101 of the first substrate and the first control 103 take place at time t3. As a result of the control 103 at time t3, the process group, e.g. its first process P1 and its second process P2, is at a second operating point A2 after time t3. Immediately after the first control 103, in the first exemplary embodiment, the processing of a second substrate takes place (or begins) by means of the first process P1 at time t4 and by means of the second process P2 at time t5. When processing the second substrate, the process group can be at the second operating point A2.Subsequently, the measurement 101 of the second substrate and the second control 103 take place at time t6. As a result of the control 103 at time t6, the process group, e.g., its first process P1 and its second process P2, is at a third operating point A3 after time t6.

[0111] As can be seen from the first embodiment, the result of the first control 103 can only be determined at time t6. Additional substrates processed in the period between t4 and t6 may therefore be rejected if the operating point A2 does not meet the requirements.

[0112] In various applications, the actual state of the substrate can have multiple measured values ​​as components, each of which can be uniquely assigned to a process in the process group. This can apply, for example, to the case where the substrate is coated with a reflective layer and subsequently with a wavelength-filtering layer. In this case, the intensity and wavelength of the radiation reflected by the substrate can be recorded as components, with the intensity being assigned to the reflective layer and the wavelength to the wavelength-filtering layer. More generally, each component of the measurement result can only be influenced by one of the processes.

[0113] In other diverse application cases, the actual state of the substrate may have multiple components, each of which may be influenced by more than one process (also referred to as a complex case). In this complex case, the unambiguous assignment between component and process is not always readily possible. This can apply, for example, to the case where the substrate is coated with multiple layers of different refractive indices. The interaction between the multiple layers can lead to a complex optical spectrum, which is completely altered if the operating point of just one process is changed.

[0114] To facilitate control in this complex case, the detected actual state of the substrate can be converted into one or more process parameters, as described in more detail below.

[0115] If the number of layers is small, a numerical layer model can be varied until it approximately predicts the recorded actual state of the substrate (e.g., the optical spectrum). To do this, analogous to layer planning, the parameters of the layer model are varied using an adjustment calculation until the resulting simulated state of the substrate (also referred to as the simulated substrate) and the recorded actual state (e.g., the measured optical properties) of the substrate match as closely as possible. The parameters of the layer model then correspond to the actual layers of the substrate, such as their thickness and / or refractive index, which are stored as process parameters 212a. Based on the comparison of the process parameters 212a with the target state of the substrate, it can be determined how the process group is to be controlled. The control can then be carried out based on the process parameters 212a as a measurement result.

[0116] However, the complexity increases significantly with the number of layers, so that even more than five layers result in an almost insurmountable computational effort and a high risk that the simulated substrate will not match the actually manufactured substrate, i.e., the simulation will fail. This is due, among other things, to the aforementioned circumstance that there is a large and difficult to fully determine variety of layer systems that match a recorded actual state of the substrate (e.g., optical properties). Therefore, the adjustment calculation may require a large variety of convergence scenarios and, depending on the selected rank conditions, associated parameter sets against which the adjustment calculation can converge.

[0117] In this complex case, a mapping function (e.g., a functional relationship) can be determined that converts the recorded actual state of the substrate into the process parameters. The mapping function can, for example, convert a deviation of the spectrum from a target spectrum into one or more process parameters per process. The mapping function can, for example, be the inverse (or pseudo-inverse) of a transformation that converts a change in process parameters into a change in the spectrum. The transformation can, for example, be determined based on a calculus of variations or real measured values. The calculus of variations can, for example, use the numerical layer model.

[0118] More generally, in complex application cases, the recorded actual state of the substrate is converted into several process parameters using one of the mechanisms described above in order to draw conclusions about the individual process steps from the achieved product properties.

[0119] For example, a recorded optical spectrum of the substrate can be converted into the thickness of individual layers or groups of layers as process parameters. For example, a resistance measurement of the substrate can be converted into the conductivity of a layer in the layer stack as a process parameter. For example, an X-ray fluorescence measurement (XRF measurement) can be converted into the mass deposition of several layers of different materials as process parameters.

[0120] The result is several process parameters, each of which is uniquely assigned to a process in the process group. This allows individual corrections 222o (e.g., for the respective manipulated variable) to be determined for each process.

[0121] In a second embodiment, the first process may comprise atomizing a material (also referred to as target material) using a plasma (also referred to as sputtering), which is applied to form a layer on the substrate (also referred to as coating). The average coating rate may be proportionally varied, for example, by varying the electrical power supplied to the first process as a first control variable. Alternatively or additionally, a transverse distribution of the coating rate may be varied by varying the spatial distribution of the plasma used for atomization.Examples of a control variable for influencing the spatial distribution of the plasma include: a spatial distribution with which a gas is supplied to the plasma (also called gas supply distribution); a spatial distribution of a magnetic field strength (also called magnetic field distribution), by means of which the plasma is formed.

[0122] In a third embodiment, the first process may comprise thermal evaporation of an evaporation material using an electron beam (also referred to as electron beam evaporation), which is used to form a layer on the substrate (also referred to as coating). The average coating rate can be influenced by varying the beam power as a control variable. The transverse distribution of the coating rate can be changed by changing the spatial distribution with which the evaporation material is irradiated by the electron beam (i.e., and thus the applied power density).

[0123] As described above, the control 103 can be implemented using multiple, independently operating elementary controllers. For example, each of these elementary controllers can be assigned only one process of the process group, with one or more process parameters assigned only to the process being supplied to the elementary controller (also referred to as individual control). Individual control ensures that it is sufficient for each elementary controller to consider only one time delay, namely the time delay between the measurement 103 and the processing 101 by the process assigned to the elementary controller.

[0124] Taking the time delay into account can be implemented in various ways, two of which are explained below as examples.

[0125] The time delay can be taken into account using a Smith predictor, which, for example, maps to the process characteristic. The Smith predictor then outputs the corrected process characteristic 212o, which clearly takes into account the expected effect of the control interventions made in the meantime. The Smith predictor can be used, for example, to implement feedback correction.

[0126] Alternatively or additionally, taking the time delay into account can involve continuously storing the temporal progression of the operating point of the process group, e.g., its manipulated variables (e.g., in a FIFO buffer). A FIFO buffer is set up to compensate for differences in processing speed and makes it possible to tolerate a backlog in a process by temporarily storing the processing objects for later processing. For example, the manipulated variables used in control can be continuously stored. In this way, past manipulated variable values ​​and change times are clearly stored in the FIFO buffer. For each measurement 101 (also referred to as a measurement event), a correction 222o is calculated based on the manipulated variable value 114a that was valid when the substrate was processed (i.e., before the measurement), and the current manipulated variable value is applied.By saving the chronological history of the actual state of the process group, feedback correction can be implemented, for example.

[0127] The control of a scalar manipulated variable (e.g., a coating rate) can always be achieved using an integral-action controller. In this case, any adjustments made remain in effect even if the setpoint deviation becomes zero, unlike a proportional controller. A proportional or derivative component (e.g., a full PID controller) does not necessarily have to be used for all types of manipulated variables. For example, the proportional control element can be omitted. In this case, the proportional control element can be replaced, for example, by a cascaded controller.

[0128] The control of a vectorial manipulated variable (e.g., a gas supply distribution) can be achieved analogously using a generalized vector integral controller. This will be described in more detail later.

[0129] The reaction speed of each elementary controller can be set to be shorter than the time delay. This ensures that when a fault is detected at time t3, only those substrates processed in the meantime (between t1 and t3) are faulty.

[0130] Optionally, the method 100 can implement cooperative behavior, for example by instructing exclusively relative changes to the manipulated variable as control intervention. For example, a sputter or electron beam power can be changed by a relative factor. The control of the process group then comprises, for example, determining a first control factor and a second control factor (e.g., by means of the correction element), wherein the control of the process group by means of the first manipulated variable is based on the first scalar control factor by which the target state of the first manipulated variable is changed, and wherein the control of the process group by means of the second manipulated variable is based on the second scalar control factor by which the target state of the second manipulated variable is changed.

[0131] For example, the old target state of the electrical power supplied to the process (e.g., electron beam power or plasma power) can be multiplied by the scalar control factor to obtain the new target state of the electrical power supplied to the process. As a second example, a gas flow change can be made additively. Then, the old target state of the gas supply supplied to the process (e.g., reactive gas supply or working gas supply) can be added to the scalar control factor to obtain the new target state of the gas supply supplied to the process. Optionally, the total gas supply can also be scaled.

[0132] To put it more simply, independent adjustments of the manipulated variables can be made between control interventions. However, at the moment of a control intervention, the current actual state can also be recorded and the control factor applied to it.

[0133] More generally, the control of the process group by means of the first manipulated variable can further be based on a state (e.g., actual state or target state) of the first manipulated variable at the time of measurement 101. Alternatively or additionally, the control of the process group by means of the second manipulated variable can further be based on a state (e.g., actual state or target state) of the second manipulated variable at the time of measurement 101.

[0134] This simultaneously enables the control of several similar processes. For example, a layer formed by sputtering can be produced sequentially by several similar processing devices to achieve a sufficient coating thickness. Thanks to cooperative behavior, these several similar processes can be controlled jointly, even if they are set to individually different control values ​​(also referred to as manipulated values).

[0135] Alternatively or additionally, the control of multiple processes can be weighted differently, or individual processes can be selectively included or excluded from the control system during operation, for example, in the event of a technical defect. The same can apply to individual actuators, for example, if they are defective.

[0136] It may occur that the measurement 101 (which can serve as a trigger for the control 103) occasionally deviates from the predetermined fixed cycle. For example, the recorded actual state of the substrate may be fed in at a temporarily more dense cycle because additional measurements were triggered manually. For example, the measurement 101 may be skipped for one or more cycles because a substrate is missing or has already been identified as reject. If the transport speed (also referred to as feed speed) and / or the distance between the substrates transported directly one after the other is changed, e.g., to change the throughput, the cycle of the measurement 101 can even be changed permanently.

[0137] The controller can be configured to account for this deviation from the clock pulse and handle it smoothly. The most common and dangerous misadjustment of PID-type controllers is excessively high gain values. This has the effect that random disturbances lead to increasing oscillation of the control output. This can result in rejects or even physical damage. The elementary controller can therefore incorporate a self-monitoring feature (also called a watchdog) that detects and compensates for this type of misadjustment, as described in more detail later.

[0138] Fig. Figure 8 illustrates the method 100 according to various embodiments 800 in a schematic process diagram. Similar to the above, several substrates are processed sequentially, which are referred to herein as substrate 1 through substrate 6.

[0139] To prevent previously executed control steps from being repeated, a predictor can be used. A predictor is clearly implemented by using information about the controlled system model to predict future controlled variables. The predictor is explained below using the Smith predictor as an example. The description for the Smith predictor type can also apply analogously to a different type of predictor.

[0140] Assume that a substrate 2 acquired at time t4 was processed at a previous time t2 = t4 - t using the first process P1. The corresponding measured value is denoted as x, which was generated by the process setting y = A1 of the first process P1.

[0141] At measurement time t4, the process setting of the second process P2 has already been changed by Δy to y' = A2. To account for this, the virtual target state deviation x' can be determined.

[0142] Determining the virtual target state deviation x' may involve using a controlled system model to simulate the process group in parallel with the real controlled process group. The controlled system model receives the control output curve y as input and calculates the expected controlled system response Δx̂. The expected system response Δx̂ can be added to the measured value, providing a prediction as if the expected response had already occurred. This can be expressed as: x'=x+Δx^

[0143] The virtual target state deviation x' (illustratively a virtual measured value x') can be fed as a corrected measurement result 212o to one or more than one control element (e.g. PID control element) (compare the feedback correction).

[0144] The controlled system model can be used to predict the response of the process group (also referred to as the system response). An exemplary controlled system model can implement the system response as a step function (i.e., a step-like or instantaneous change) with a time delay Δt (also referred to as the predictor delay); and the magnitude of the step of the step function is determined based on the control gain setting, for example, according to the relations: Δx^:=s⋅∑−tt0Δys:=14K

[0145] The Δy term clearly reflects the sum of all control interventions Δy applied in the time interval from -t to the present. The choice of the so-called system gain s is based on the experience that, with a reasonable controller setting, the gain K typically lies in a range from approximately 0.2 to approximately 0.5x of the ideal correction. For example, K denotes only the integral gain of the I control element.

[0146] For an average value of K=14Kideal s gives exactly the correct change amplitude.

[0147] Of course, a more precise model can also be used as the controlled system model. The controlled system model described above only requires one additional parameter (namely, the time delay). In many applications, this can already achieve very good control behavior.

[0148] Alternatively, or in addition to correcting the setpoint deviation x (for example, to provide the corrected measurement result 212o), the output of one or more control elements can be corrected. This can be expressed as: Δy':=Δy+y(−t)−y(t0).

[0149] The expression y(-t), which represents the individual control values ​​of the relevant actuators, can optionally be captured at the moment of processing 101 and stored substrate-specifically, for example, assigned to a substrate identifier. This allows for a more robust assignment using a substrate identifier (also referred to as a product ID) as an alternative or in addition to the assignment using the substrate runtime.

[0150] More generally, the actual state of the process group during processing of the substrate using the first process and / or during processing of the substrate using the second process can be assigned to the substrate (also referred to as a substrate identifier), e.g., an identifier (also referred to as a substrate identifier) ​​of the substrate. The substrate identifier can, for example, have a consecutive substrate number. The correction element can then take into account the assignment of the actual state of the process group to the substrate (also referred to as a substrate assignment).

[0151] Substrate assignment eliminates the need for a heuristic, potentially inaccurate, controlled system model. For example, if a layer is built up sequentially using several similar processes (e.g., coating processes each performed using magnetrons), the assumption of a step change at time t0 is no longer accurate. The same applies if the rate change occurs with a finitely fast ramp.

[0152] Optionally, the substrate allocation can be configured to better take into account a variable time delay (changed transport speed, temporary standstill).

[0153] Further exemplary implementations are explained below, where one or more constraints (also referred to as limitations or positioning constraints) are taken into account. Please note that the description refers to the displacement by Δy'. The dynamic behavior can be essentially the same or similar to the implemented configuration.

[0154] For example, it can be considered that several actuators are or will be positioned differently. In this case, an individual difference y(-t)-y(t0) is assigned to each actuator, which can be taken into account, for example, by specifying the combination of the individual results.

[0155] The elementary controller then determines the relative change 222o of the manipulated variable: Δyu:=Kwt⋅x'

[0156] In it, Δy uthe change in the manipulated variable before the limitation (unlimited control output); K is the control gain (e.g. having a scalar constant); W t the clock weighting factor (e.g. having a time-dependent scaling), which will be described in more detail later; x' the virtual target state deviation, for example comprising the process characteristic value corrected by the output of the predictor.

[0157] The consideration of the clock weighting factor w t clearly promotes the stability of the control when deviations occur in the actual measurement cycle. The base cycle ΔT corresponds clearly to the nominal or average measurement cycle of the process group under production conditions. Using a time measurement, the actual elapsed time Δt (also referred to as the actual cycle interval) since the last control intervention can be determined. Based on the actual cycle interval, the cycle weighting factor W tdetermine, for example according to the relation: wt:=min{ΔtΔT,1.0}

[0158] If the actual clock interval Δt decreases (indicated by measuring intervals that are too short), the control action is dampened, resulting in an increase in the sampling rate of a conventional controller. The dynamic behavior as a function of (real) time then remains essentially the same.

[0159] If the actual clock interval Δt increases (illustratively, measuring intervals that are too short), an overreaction is inhibited. Consecutive measuring intervals that are too long slow down (with respect to real time) the reaction speed of the elementary controller, which at best extends the settling time. The above-mentioned relationship for the clock weighting factor W t results in good control behavior, even if the actual clock interval fluctuates completely irregularly.

[0160] More generally, the clock weighting factor W t be a (e.g. non-linear) function of the actual clock interval.

[0161] The (still unlimited) control output Δy u can optionally be checked for limit violations. Checking the control output Δy u indicates that instead of the control output Δy u the limit value is used when the control output Δy u outside predefined limits (e.g. Δy low , Δy high ) and / or that a limitation of the control output Δy u to a predefined rate limit Δy rate occurs when the magnitude of Δy u is greater than the rate limit Δy rate .

[0162] Optionally, the limits can be determined dynamically, as explained below. For user-friendliness, the parameters are configured in actuator units (control unit). The Δy limits are dynamically calculated from the actuator limits.

[0163] For example, to control a generator power, the parameterization is done as an absolute limitation of the power: Plow≤P≤Phigh.

[0164] By changing P neu := P ist expΔy we get logPlowPist≤Δy≤logPhighPist.

[0165] The rate limit P rate is specified as the change in rate within one clock cycle: |Tyre−Pist|≤!wtPrate. At this point, the clock weighting factor is applied, since otherwise rapidly successive steps could override the rate limit. Here, too, rearranging the formula yields: |Δy|≤log(Pist+PratePist)≤−log(Pist+PratePist).

[0166] The properties of the logarithm mean that positive change leads to a tighter limit than negative, so the limit used is: Δyrate:=log(Pist+PratePist).

[0167] If several generators are controlled in parallel as actuators, the limits are determined for each generator and the strongest limitation is applied in each direction.

[0168] For other actuators (e.g., gas inlet, magnetic system), different equations apply for determining the control value from y. From these, the dynamic limit is calculated in an analogous manner by solving for Δy.

[0169] The limited control output Δy can be determined as explained below. Determining the limited control output Δy involves applying an absolute limit, then applying the rate limit, and optionally applying another absolute limit. The absolute limitation can, for example, be made according to the following relation: Δy1:={Δyunlif Δylow≤Δyunl≤Δyhigh0if Δylow>ΔyhighΔylowif Δyunl<ΔylowΔyhighif Δyhigh<Δyunl.

[0170] If |Δy1 > Δy rate the following can also be done: Determining an overdraft factor v:=|Δyunl|Δyrate;

[0171] Determine a damped response y2 :=yunl·1 / v; and if a limit was applied during the absolute limiting, repeat the absolute limiting with the damped response y2 as input.

[0172] The limited control output is then Δy:=:Δy1, where this is optionally the value after repeating the absolute limitation.

[0173] This results in the following properties. Since the absolute limit is always applied last, it is guaranteed that it will not be violated. In the case of unsatisfied boundary conditions, 0 (no control intervention) is output. The rate limit is only applied if it is not already satisfied by absolute limitation. The damping method (scaling of y unl and renewed absolute limitation) prevents unnecessary falsification of y→.

[0174] As a by-product, the overdraft factor v is generated, which can be used in the subsequent self-monitoring.

[0175] The self-monitoring function is designed to detect and inhibit unstable controller behavior. Any control action can trigger a self-monitoring function. In principle, the self-monitoring function reacts to exceedances of the rate limit Δy. rate . The user's configuration of the rate limit can be the basis for the functioning of the self-monitoring (also known as a watchdog).

[0176] In each control intervention, the watchdog receives the overrun factor v:=|Δyunl|Δyrate.

[0177] This overdraft factor is classified, for example qualitatively, according to v < 1.0 (“OK” or “no overdraft”) and v ≥ 1.0 (“not OK” or “overdraft”).

[0178] During normal operation, transient overshoots are possible, e.g., as a step response after a setpoint change. A tolerance period applies to these, calculated as a number of control interventions for which they are accepted.

[0179] To ensure this, the watchdog can be configured as a state machine that can be switched between one or more of the following states.

[0180] The basic state (also called sleeping) of the watchdog can be such that when an “OK” value is received, the basic state remains, and when an “exceed” value is received, the state changes to the transition state.

[0181] The transition state (also called awakening) can have the state change to the reaction state after a maximum of 10 control interventions, an incoming value "OK" triggers a change to the reaction state if 3 consecutive values ​​are OK (for example, it can be assumed that the transient is over; and a newly arriving exceedance triggers an action); and an incoming value "exceedance" only increments the counter.

[0182] The reaction state (also called wake) can have an incoming "OK" value triggering a change back to the base state if 25 consecutive values ​​are "OK"; and an incoming "overshoot" value triggering a reaction from the watchdog.

[0183] The behavior in the transition state (also called the awakening state) leads to a rapid transition to the desired case of oscillatory behavior. The initial rate overshoot occurs during the maximum displacement of x, whereas during the subsequent rebound, "OK" values ​​occur, causing the rapid transition to the reaction state.

[0184] If an overshoot occurs in the reaction state (also called the awake state), the controller's gain factor K is reduced as a reaction. The reduction is based on the overshoot factor v, for example, according to the relationship: K:=max(K1.1v,K2),

[0185] To illustrate, the gain is then reduced to such an extent that when the step is repeated, the rate limit is only utilized by approximately 90%, but by a maximum factor of 2.

[0186] After returning to the default state (also known as the sleep state), K can optionally be left at its new setting. However, it can also be specified that permanent, automatic changes to user-specified values ​​should not be possible. In this case, the implemented configuration can be such that the gain value is gradually increased back to the original value in the sleep state, which may cause the unstable behavior to recur after some time.

[0187] In the following, exemplary implementations are explained for converting the control output Δy into a control variable (according to which the corresponding actuator is controlled).

[0188] The manipulated variable for an actuator can be determined based on the control output Δy (illustratively a dimensionless change in the manipulated variable) and the actual state of the process group.

[0189] If the actuator has a generator, the generator power P can be changed as a control variable (e.g. exponentially), for example according to the relation: Pneu=Pist⋅expΔy

[0190] In the case of multiple generators j, the same relation can be applied to all generators, optionally specifying a scaling factor a for each generator j can be set: Pj,new=Pj,ist⋅exp(ajΔy).

[0191] The description can also apply to manipulated variables of other types, such as an electrical voltage as a manipulated variable or the total gas flow (e.g. total reactive gas flow) as a manipulated variable.

[0192] The gas supply distribution (also called trim gas distribution) can be set as follows. For the gas supply distribution, the manipulated variable can be written as a vector, e.g., according to: Δy→≡yi,i=1..N.

[0193] The control value is then the gas flow fi≡f→ per gas inlet channel (also referred to as individual gas flow), and can be specified, for example, in sccm (standard cubic centimeters per minute) or slm (standard liters per minute). Control is performed, for example, additively and relative to the sum of the individual gas flows: fi,neu=fi,ist+aj⋅Δyi⋅∑ifi,ist. Analogous to multiple generators, a scaling factor a can also be optionally specified here. j for each of the gas supply channels.

[0194] In the case of the magnetic field distribution, the manipulated variable can also be written as a vector, e.g. as: Δy→≡yi,i=1..N.

[0195] The control value is then, for example, the displacement of at least one of several magnets which provides the magnetic field distribution, for example the vertical stroke hi≡h→ of the individual support points, for example in millimeters or micrometers.

[0196] The taxable amount can then be determined, for example, according to the relation: hi,new=hi,actual+aj⋅Δyi.

[0197] In the following, we will look in more detail at multi-component variables, the components of which can be, for example, measured values, characteristic values ​​or control values ​​used in parallel with one another.

[0198] Fig. 9 illustrates a processing device 900 according to various embodiments in a schematic configuration diagram, which is configured to perform a coating process as an exemplary process. The coating process may include forming a layer on a substrate using a coating material. For this purpose, the coating process may be supplied with at least the coating material or a reaction product of the coating material, with one or more than one gas, with electrical power, and optionally with thermal energy, which is supplied to the substrate. The layer may, for example, comprise a reaction product of the reactive gas and the coating material, e.g., an oxide or a nitride.

[0199] The coating process 902d may include a vapor deposition process. In the following, sputtering is referred to as an exemplary physical vapor deposition (PVD), whereby the description may apply analogously to another PVD or chemical vapor deposition (CVD). The sputtered coating material may be provided using a magnetron (also referred to as a sputtering device).

[0200] The processing device 900 can perform several sub-processes that interact along a chain of interactions, each of which can influence the coating process. A first sub-process 902a of the processing device 900 can include forming a plasma. A second sub-process 902b of the processing device 900 can include atomizing the coating material using the plasma. An optional third sub-process 902c of the processing device 900 can include reacting the atomized coating material with a reactive gas.

[0201] Fundamentally, various measurable variables can be used as manipulated variables or controlled variables, depending on how the coating process is controlled. Therefore, the following refers to the more easily understood actuators that influence the operating point of the coating process.

[0202] An exemplary first actuator 852 is configured to influence the electrical supply to the plasma. For example, the first actuator may comprise a generator and / or be configured to adjust an electrical voltage supplied to the plasma, an electrical power supplied to the plasma, and / or an electrical current supplied to the plasma. Changing the electrical supply to the plasma may change the rate at which the coating material is atomized.

[0203] One or more exemplary second actuators 854 are configured to influence the magnetic field in which the plasma is generated, e.g., its spatial distribution. For example, each second actuator 854 can have a lifting device and / or be configured to displace a magnet of the magnet system. Changing the magnetic field can change the plasma and thus the rate at which the coating material is atomized (also referred to as the atomization rate).

[0204] An exemplary third actuator 856 is configured to influence the supply of working gas to the plasma, e.g., the total flow of the working gas. The working gas can clearly be the gas that is ionized to form the plasma. For example, the third actuator 856 can have a valve and / or be configured to change the total flow of the working gas. Changing the total flow can change the pressure to which the plasma and / or the atomized coating material are exposed. A greater pressure can scatter the atomized coating material more, which affects the transfer of the coating material to the substrate, and / or can affect the formation of the plasma.

[0205] One or more exemplary fourth actuators 858 are configured to influence the spatial distribution of the working gas. For example, each fourth actuator 858 may include a valve and / or be configured to divide the total flow of the working gas among multiple gas supply channels (also referred to as gas channels). Changing the spatial distribution of the supply of working gas to the plasma may change a pressure gradient to which the plasma and / or the atomized coating material are exposed.

[0206] The second sub-process 902b, e.g., the sputtering itself, cannot always be directly influenced. An exemplary fifth actuator (not shown) is configured to purge the solid coating material with a purge gas. The purge gas displaces, for example, the plasma or optionally present reactive gas and thus influences the sputtering rate. If the magnetron is a tubular magnetron, an exemplary additional fifth actuator (not shown) can be configured to influence the rotational speed of the tubular coating material. This influences the duration a surface section is exposed to the plasma, and thus its temperature and / or chemical composition.

[0207] More generally, the first and second sub-processes provide for the transfer of the solid coating material into the gas phase. If plasma is not used for this, appropriate actuators can be configured to influence the transfer of the solid coating material into the gas phase.

[0208] An exemplary sixth actuator 860 and one or more exemplary seventh actuators 862 are configured analogously to the third / fourth actuator(s), with the difference that they are configured to influence the supply of a reactive gas to the atomized coating material, e.g., a spatial distribution of the supply of reactive gas. This influences the chemical composition of the formed layer and influences the atomization rate.

[0209] Of course, more or fewer of the aforementioned actuators can be used, or other manipulated variables can be used. It should also be understood that the above separation of the subprocesses is only for clarity. For example, the aforementioned subprocesses do not have to be strictly separated from one another. Alternatively or additionally, one actuator can influence multiple variables, or multiple actuators can interact with one another.

[0210] Therefore, the entirety of the sub-processes 902a, 902b, 902c will be referred to more clearly below as a process, wherein the process is, for example, supplied with one or more than one gas, supplied with the coating material, supplied with electrical energy, and the like. The actuators can then comprise those components by means of which the supply and / or maintenance of the process takes place. Supplying the process can, for example, comprise supplying the process with electrical power, supplying it with one or more than one gas, supplying it with thermal energy, supplying it with a magnetic field, etc.

[0211] More generally, those actuators that influence the operating point of the process can be controlled.

[0212] Fig. 10 illustrates a vector space 1000 according to various embodiments in a schematic cross-sectional view. As explained above, in the case of multiple actuators, the actuator output may have multiple components, which is notated herein in the more easily understandable vector notation. The resulting vector can, for example, be used as a characteristic Φ or at least more easily converted into a characteristic Φ that represents the changes of all actuators together.

[0213] Generally speaking, the characteristic can be a function of the vector components of the vector. In the following, reference is made to the easily understandable magnitude of the vector as an exemplary characteristic. Likewise, reference is made to the easily understandable spatial coordinate to which the vector is directed as an exemplary characteristic. It should be understood that the characteristic can also be determined using another operation, e.g., according to the applied norm of the vector space. For example, the intersection point of the vector with a plane can also be used as a characteristic.

[0214] For example, the parameter can be a scalar value or at least based on a dot product. The magnitude of the vector can be defined as a dot product with itself.

[0215] In the following, reference is made to the easily understandable Cartesian vector space. It should be understood that the multicomponent control output can also be notated differently, for example, not necessarily in a vector space or not necessarily in Cartesian coordinates.

[0216] The Cartesian vector space is spanned by the corresponding Cartesian coordinates, each of which is assigned to at least one of the multiple actuators. The value at the coordinate then represents the control value according to which the actuator to which the coordinate is assigned is controlled. Each component (also referred to as a vector component) of the control output, notated as a vector, can be assigned analogously to one of the actuators.

[0217] A measurable physical property of a substrate coating, such as a layer thickness and / or an optical refractive index, can be used as a measurement variable. This physical property can be optimized with regard to its homogeneity (also referred to as uniformity) on a flat, extended substrate. For example, the measured value of the property can exhibit the smallest possible deviation from the average value of all locations at each recorded location on the substrate (e.g., the acceptable area). This can be expressed as: maxi|yi−y¯|→min., with y¯:=1N∑iyi

[0218] For this purpose, a coating system can have several groups of similar actuators that allow a spatially specific change in the layer properties. Examples of these are several fourth actuators, several seventh actuators, and / or several second actuators. For example, several spatially distributed gas channels at different locations in the coating device can bring about a local change in the layer thickness. For example, at least for some coating materials, the refractive index can be changed via the local reactive gas inflow. For example, a magnetron can have a magnet system with several second actuators, each of which is configured to change the spatial distribution of the magnets of the magnet system, so that the magnetic field strength on the coating material to be atomized is locally changed, thus causing a change in the coating thickness.The magnet system (in the elongated case also referred to as a magnet bar) can comprise a carrier and a plurality of segments carried by the carrier (also referred to as support points), each segment of which has a second actuator (e.g. a lifting device) and one or more than one magnet, wherein the second actuator is arranged to displace the one or more than one magnet relative to the carrier, e.g. away from it or towards it (herein also simply referred to as a stroke).

[0219] The manipulated variable is the output variable (illustratively the position) of the actuator used (e.g. a mass flow controller or a servo motor). Examples of the manipulated variable include a gas flow (e.g. in sccm) or a servo position (e.g. in mm), which can be represented as a scalar (also called a numerical value). A group of similar actuators, for example, has one manipulated variable per actuator. Such a list of manipulated variables is noted herein as a vector manipulated variable (also called a manipulated vector). Each component of the manipulated vector contains the manipulated variable for the actuator assigned to the respective index position of the component.

[0220] According to various embodiments, the method 100 may provide one or more of the following: - The measured or calculated property value is available as input at several (at least two) locations on the substrate (also referred to as measuring points). The number and location of the measuring points can be a function of the number and spatial assignment of the actuators. - The output is the change in the vectorial manipulated variable in order to achieve optimal or at least improved uniformity. - If one or more actuators reach a control limit assigned to it, the actuators that are still free are controlled in such a way that the best uniformity possible under the restriction is achieved. - Crosstalk of the actuating effect on spatially distant areas of the substrate is suppressed.

[0221] Inhibiting crosstalk is advantageous, for example, when applied to a gas supply system with multiple gas channels. With a native 1:1 mapping of measuring point to actuator, changing a control value would significantly change the layer properties at neighboring and more distant measuring points, which would cause undesirable feedback effects and oscillations in a closed-loop control system.

[0222] In the following, a so-called conversion of the measurement grid (also called grid conversion or resampling) is explained, which can be done optionally if, for example, the dimension of the vectorial measured variable and the dimension of the control vector differ from each other.

[0223] The raster conversion may involve forming zone-wise partial averages based on the acquired property values ​​to obtain an input in the dimension (e.g., channel number) of the control vector. A spatial area (illustratively a "zone," e.g., a spatially extended section along the coating device) is specified for each actuator, and all measurement points located within this zone are averaged. This type of raster conversion provides one or more of the following: - The measuring grid can be changed between two control interventions, e.g. to record more or fewer measuring points without affecting the control. - The failure of individual measuring points or sensors has less impact on control, for example as long as each zone has at least one measuring point. - If necessary, several zones can overlap at least partially or even completely.

[0224] The raster conversion may alternatively or additionally include an interpolation and / or extrapolation of the measured values, e.g. by means of a polynomial train.

[0225] The processing chain of the elementary controller is briefly explained below, followed by a more detailed discussion. The elementary controller can operate, for example, as an integral controller. As described above, the elementary controller stores output corrections and the output time (time of control intervention) in order to compensate for the substrate transit times between control intervention and measurement using the Smith predictor.

[0226] The control system can detect only relative changes based on the detected actual state of the substrate. This enables cooperation with other systems that control the actuators (also referred to as cooperative behavior).

[0227] The control output can be formed as a linear scaling of the deviation vector (integral control). If a limit is violated, a limited control vector can be determined that satisfies all boundary conditions while maintaining a minimal Euclidean distance from the ideal (unlimited) control vector.

[0228] The intermediate result is the so-called correlated control output Δy→ This can optionally be filtered using a linear transformation (also called decorrelation) to compensate or at least inhibit crosstalk between the control channels. The result of the decorrelation is the control output Δz→, whose components are uniquely assigned to the actuators.

[0229] The representation of a boundary can be done analogously in vectorial notation. In Hesse normal form, it can be written: y→⋅c→≤ξ If this relation is fulfilled, the criterion of limitation is met or the limitation is not violated. y→ the position vector in vector space, c→ a (e.g. normalized) normal vector of a plane, which specifies the position of the boundary, and ξ the distance of the plane from the origin of the vector space.

[0230] In this way, every boundary along a coordinate of the vector space can be represented by a normal vector c→ (with |c→|=1) and the scalar value ξ. The limitation along a coordinate (illustratively an individual control channel) can then be expressed as c→=(0…010…0)T.

[0231] This representation makes the processing of boundary conditions accessible to linear algebra. It allows boundary conditions to be combined and constrained optimization to be performed under the influence of the applicable boundary condition(s).

[0232] Furthermore, other types of boundary conditions can also be formulated as linear equations with respect to the vectorial manipulated variable (also called the manipulated vector). For example, an additional constraint can be defined that the sum of all vector elements should assume a certain value.

[0233] In addition, this can enable a linear transformation of control outputs, for example by performing an inverse transformation of the limits.

[0234] Optionally, in case of a repeated violation of a limit (e.g. a rate limit), the watchdog can be configured to reduce the control gain in order to inhibit oscillation of the elementary controller.

[0235] To compensate for or at least inhibit crosstalk between neighboring or even more distant actuators, decorrelation of the actuator output can be performed. In a relatively simple implementation, this decorrelation involves multiplication by a decorrelation operator D, e.g., a matrix containing or formed from it. In principle, the decorrelation operator can be freely chosen. For example, the crosstalk factors of the actuators can be determined experimentally and the decorrelation operator can be determined from this by (pseudo)inversion.

[0236] However, decorrelation is not necessarily required to set up a magnetic system.

[0237] To adjust the spatial gas supply distribution, decorrelation can optionally be implemented, for example, by adjusting directly adjacent gas supply channels in opposite directions. Without decorrelation, feedback (e.g., triggering oscillations) can occur between the gas supply channels.

[0238] In the following, reference is made to the control of several actuators, which can be done analogously to what was previously described.

[0239] The actual state of the substrate can be represented by the dimensionless setpoint deviation, i.e., the setpoint deviation x, which is fed to the elementary controller. In the case x = 0, the actual state is equal to the target state of the substrate. The case x ≠ 0 can indicate a quantized deviation from the target state.

[0240] For the vectorial notation, the target state deviation x described above becomes the target state deviation vector x→ (also referred to as deviation vector for short). This contains as many components as there are control variables (i.e., control channels).

[0241] Analogous to the target state deviation, the control output Δz→ be recorded as a dimensionless quantity, which is then converted into a physical manipulated variable.

[0242] The display issue Δz→ can clearly represent the relative scaling (in a multiplication) or shift (in an addition) of a control value.

[0243] This ensures that the method 100 can be applied to several related processes with different actual states. For example, the elementary controller works exclusively with the relative change Δz→.

[0244] In case of a limit violation, a limited control vector (also called a limited control vector) is determined which has the smallest possible Euclidean distance to the ideal (unlimited) control vector.

[0245] An exemplary implementation of the method 100 may include one or more of the following: - For all zones, the measurement points contained therein are searched for and their layer thickness values ​​are averaged. The zone mean values ​​are incorporated into the deviation vector. x→ transferred. - The sum of the corrections already output but not yet effective for the measurement is used to calculate a virtual deviation vector from the measured deviation vector. The virtual deviation vector represents the expected measured values ​​applying the corrections already made (in other words, a prediction). - An unlimited correlated output is Δy→u (e.g. as a parameter) is calculated by multiplying the deviation vector by a proportional factor. Δy→u is a vector whose components are assigned to the actuators. - The correlated control output is checked for violations of limits (e.g., control limits and rate limit). In case of violations, a new correlated control output is generated. Δy→ calculated, which corresponds to the previously determined control output Δy→u as close as possible. - Using Δy→u and the configured change rate limit (also called rate limit limitation), the watchdog is optionally applied. - The decorrelated output is obtained from the correlated output by linear transformation Δz→ calculated. - The vectorial manipulated variable is increased by the vector Δz→ changed: Z→1=Z→0+a⋅Δz→, where optionally a scaling factor α ≠1 can be applied, e.g. a scaling according to the total gas flow or a relative distribution to several similar actuators.

[0246] An exemplary implementation of the formation of zone averages is explained below. Each actuator is assigned a spatial area (descriptively referred to as a zone, e.g., a spatially extended section along the coating device), with all measuring points of the substrate located in this zone being averaged. As a criterion, the zone can be assigned a position interval [x start ,x end ] and / or a list of track identifiers.

[0247] One or more than one of the criteria of the zone can also be empty (descriptively "not set"), i.e. omitted. All measuring points are assigned to the zone whose location lies within the position interval assigned to the zone [x start ,x end] and / or whose identifier is included in the list of track identifiers assigned to the zone. If one or more of the two criteria are not set, their verification can be skipped.

[0248] The spatial allocation of the zones does not necessarily have to correspond exactly to the location of the actuators. For example, the measurable substrate width in an inline sputtering system can be narrower than the width of the coating device and / or the spatial distance of the external gas supply channels (also referred to as the gas supply width). In this case, the detectable area on the substrate can optionally be divided according to the proportionally scaled width of the gas supply.

[0249] The target deviation x is then obtained as the result of the measurement at the time of measurement. For example, the target deviation x can represent the deviation of the actual layer thickness of the substrate or its coating from the target layer thickness of the substrate or its coating, e.g., expressed in relative units. For example, x = -0.05 can indicate that the actual layer thickness is 95% of the target layer thickness (more generally referred to as the reference value for the layer thickness).

[0250] The zone mean value per zone can, for example, be an arithmetic mean of the individual measured values. xi:=〈xj〉j∈Zi.

[0251] Of course, another relation can also be used to convert the individual measured values ​​per zone into a target state deviation x representing the i-th zone. i to transfer.

[0252] Two zones can also overlap each other, e.g. by their position intervals [x start,x end ] overlap each other. This allows one or more than one measurement point to be included in the target state deviation x i several zones and thus achieves more robust processing.

[0253] The individual target state deviations x i (i=1 to I) form the components of the deviation vector x→.

[0254] The mechanism described above for controlling based on the scalar target state deviation x (also called scalar control) can be applied component-wise to the deviation vector x→ The virtual target state deviation x→' results from anticipating the expected system reaction to the corrective steps taken in the meantime x→^, which can be expressed as: x→'=x→+x→^.

[0255] The expected system response can be calculated based on the correlated control output Δy→ It can be assumed that the effect of decorrelation (ie the transfer of Δy→−Δz→ ) is reversed (e.g. inverted) by the system. As for the scalar quantity, a step function can be implemented as the system response, which is dependent on a time delay Δt and an estimated step height 14K can be expressed as follows: x→^:=14K∑−tt0Δy→

[0256] In this case, x→' serve as input for the integral control step.

[0257] This can be expressed as: Δy→u:=Kwt⋅x→', where K is the control gain (e.g. a scalar constant) and W tis the clock weighting factor (e.g., causing a time-dependent scaling). It can then be expressed as: wt:=min{ΔtΔT,1.0}, where Δt is the time elapsed since the immediately preceding control intervention and ΔT is the configured base clock (input parameter).

[0258] As described above, one or more than one control limit (e.g. having an upper limit and / or a lower limit) can be expressed as: y→⋅c→≤ξ

[0259] This makes it easier to see that the scalar product y→⋅c→ can be used as a scalar parameter Φ to determine whether the parameter satisfies the criterion Φ ≤ ξ. The parameter is in this case a function of the vector components of y→ (also known as y i denoted) and the vector components of c→ (also known as c iThe parameter Φ can be expressed, for example, as: Φ=∑iciyi

[0260] The parameter set (c→,ξ) is referred to herein as a constraint. The constraint represents a limitation of y→ to the half-space on one side of the hyperplane c→y→=ξ. The hyperplane is referred to here as the boundary surface of the constraint.

[0261] An example implementation that allows updating the constraint is explained below.

[0262] To determine the appropriate limits for the current control intervention, the process for determining the value output can be carried out in reverse. This is explained below using several fourth actuators 858 as an example, although the description can also apply analogously to other actuators.

[0263] Each exemplary fourth actuator 858 is configured to influence the spatial distribution with which the plasma is supplied with working gas. For example, each fourth actuator 858 may include one or more valves configured to divide the total flow of working gas among multiple gas supply channels (also referred to as gas supply channels).

[0264] For the i-th channel of the gas supply, for example, the limits f low , f high Formulated as constraints, it can be written: • c→f,i,low=(0…0(−1)0…0)T,ξf,low=fi,ist−flow • c→f,i,high=(0…010…0)T,ξf,high=fhigh−fi,is

[0265] In the following, the index "i,low" or "i,high" is omitted to simplify notation. The description applies analogously to any gas supply channel or the respective constraints.

[0266] By rearranging fi,new = fi,ist + a j · Δz j · Σ i f i,ist we obtain the transformed constraint for Δz: c→z=c→f;ξz=ξfaj∑ifi,ist.

[0267] The optional decorrelation is then carried out by linear transformation Δz→:=D_⋅Δy→.

[0268] To escape the constraint (c→z,ξz) for Δz→ the corresponding constraint (c→y,ξy) for Δy→ To determine the equation c→zΔz→<ξz The left side is identical to c→zD_Δz→, which results in: c˜y:=c→z⋅D_;ξ˜y:=ξz.

[0269] Renormalized, this results in: c→y:=c˜y|c˜y|;ξy:=ξ˜y|c˜y|, where c→y can generally have several components other than zero, and in this case can no longer be clearly assigned to a gas channel.

[0270] In the case of the second actuator 854, which is configured to influence the magnetic field generated by the magnetic system, the same applies. Only the transformation of the displacement h i of the magnet (e.g. having a stroke) into the change Δz i is determined somewhat differently.

[0271] From h i,neu = h i,ist + a j ·Δ yi This results in: c→z=c→h;ξz=ξhaj

[0272] Fig. 11 illustrates a vector space according to various embodiments in a schematic cross-sectional view 1100. The application of the limitation (also referred to as limiting) is explained below.

[0273] Determining the limited output Δy→ (also called second output) can have: absolute limitation by one or more than one orthogonal projection onto one or more than one boundary plane; checking the rate limit by means of Δy→1, If this limit is exceeded, a transfer of Δy→u→Δy→2 and optionally a new absolute limitation by orthogonal projection onto one or more boundary planes, which involves a transfer of Δy→2→Δy→3 provides.

[0274] This sequence clearly provides one or more of the following: - It avoids unnecessarily shortening the output vector, which can occur, for example, if the rate limit is executed first and then the projections. - An unnecessarily large misalignment of the positioning vector is avoided, which can occur if the projected vector Δy→1 would be shortened, as shown in cross-sectional view 1100. - The unlimited vector is clearly limited to the amount Δy rate shortened and the result Δy→2 limited again, which explains the order Δy→u→Δy→2→Δy→3 results.

[0275] Cross-sectional view 1100 shows an exemplary case in which two vectors are converted into each other and the verification is performed, with all three processes leading to a changed intermediate result. The resulting vector Δy→3 is as close as possible to the ideal vector 31. The deviation between Δy→3 and the vector 31 can, for example, be accepted in order to require as little computing power as possible.

[0276] The absolute limitation mentioned above is explained below. The absolute limitation is based, for example, on any number of constraints (c→y,n,ξy,n).

[0277] A minimum control vector is determined ε→, which leads to the sum Δy→u+ε→ the condition of all constraints is met.

[0278] The given constraints for Δy→ are in constraints (c→n,ξn) with reference to ε→ transformed, which can be written as: c→y,n(Δy→u+ε→)≤ξy,nc→y,nε→≤ξy,n−c→y,n⋅Δy→u

[0279] This shows that: (c→n,ξn):=(c→y,n,ξy,n−c→y,n⋅Δy→u) is.

[0280] Based on this, two cases can be distinguished. In case 1, all ξ n ≥ 0m or in other words, fulfills ε→=0→ all constraints. In case 1, no limitation is necessary. In case 2, there is at least one n such that ξ n < 0.

[0281] This boundary-value problem can be solved using quadratic optimization or other computer science methods. Quadratic optimization (also known as quadratic programming) and the quadratic programs used for it represent a special area of ​​mathematical optimization that uses particularly simple functions.

[0282] There are also other methods by which the optimal control vector ε→ can be determined.

[0283] Examples for solving this problem include: the gradient method; the interior point method; active set methods, such as the primal dual active set algorithm; Lemke's algorithm; and unique sink orientations. If the boundary value problem conditions lead to a linear complementarity problem, more specialized algorithms such as Lemke's algorithm or unique sink orientations are available.

[0284] The gradient descent method can involve proceeding from a starting point along a direction of descent / ascent until the numerical improvement stagnates. For example, if the negative gradient is chosen as the descent direction, i.e., the direction of the locally steepest descent, the steepest descent method is obtained. In general, the gradient descent method refers to an optimization method in which the descent direction is obtained using gradient information, which is therefore not necessarily limited to the negative gradient.

[0285] Active set methods comprise a class of iterative algorithms for solving quadratic optimization problems. The primal dual active set algorithm is a method for solving a quadratic optimization problem over a convex subset of a Hilbert space over a corresponding set. The interior point method comprises a class of algorithms for solving optimization problems. Their main application areas are linear or quadratic programs, for example. Compared to active set methods (e.g., having a simplex method), the interior point method has better theoretical properties (e.g., polynomial complexity) and faster convergence for very large sparse problems. The primal dual active set algorithm uses the Lagrange multiplier to arrive at a solution that is both feasible and as optimal as possible.

[0286] Optionally, it can be determined whether conflicting, i.e., unfulfillable, constraints exist. If these exist, the corresponding control action can be skipped, and no change to the process's operating point can be made, or none of the process's actuators can be controlled.

[0287] As a result of the iterative method, the control vector ε→ receive, or Δy→1=Δy→u+ε→or Δy→3=Δy→2+ε→.

[0288] The rate limit mentioned above is explained below. The rate limit is applied when |Δy→1|>Δyrate This shows that |Δy→1| can be used as a scalar parameter Φ to determine whether the parameter Φ satisfies the criterion that Φ > Δy race The parameter Φ can be expressed as: Φ=∑iyiyi

[0289] Applying the rate limit may involve: Determining the overdraft factor v:=|Δy→u|Δyrate; Determine a dampened response as Δy→1:=Δy→u⋅1 / v; If case 2 was present, ie the absolute limitation was already applied; apply the absolute limitation to the dampened reaction Δy→2.

[0290] The limited control output is then Δy := Δy1, where this is optionally the value after applying the absolute limitation to the damped response Δy→2 can be.

[0291] The balancing behavior is explained below. As a byproduct of the mechanisms explained above, the elementary controller can be configured to be balancing (also referred to as a balancing elementary controller). The balancing elementary controller can be configured to provide a mean-adjusted control intervention. For this purpose, a so-called sum constraint can be formulated, for example, according to the relation: (11…1)T⋅Δy→=!0→c→=1N(1…1)T,ξ=0

[0292] The change vector Δy→u and all real constraints can be projected onto the interface of the sum constraint.

[0293] In the following, an exemplary implementation of the above-mentioned decorrelation is explained in more detail. The decorrelation can have the correlated output Δy→ into the decorrelated output Δz→ to convert, which can be expressed, for example, as a transformation that reads: Δz→:=D_⋅Δy→, where D denotes the decorrelation operator.

[0294] If the magnetic field or the configuration of the magnetic system is to be changed, decorrelation is not necessarily required. Instead, the decorrelation can be such, or the decorrelation operator D can be such that the z is mean-corrected. Illustratively, the decorrelation operator D or the transformation performed with it can provide that all vector elements are changed by a constant.

[0295] If the spatial gas supply distribution is to be changed, the decorrelation operator D, written as a tensor, can be written as follows: D_:=12[1−10⋯0−12⋱⋱⋮0−1⋱−10⋮⋱⋱2−10⋯0−11]

[0296] Illustratively, the decorrelation operator D then causes a change in one actuator and an equal and opposite change in the immediately adjacent actuator. A change in an inner actuator is compensated by an opposite change in the neighboring actuators with half the magnitude. This ensures that fewer or no experimentally determined parameters are required.

[0297] Alternatively, the decorrelation operator D can also be determined experimentally, which improves its effectiveness. This can be done by determining how an actuator affects more than one controlled variable or measured variable, or how neighboring manipulated variables interact or are interdependent (also known as crosstalk).

[0298] In the case of gas supply distribution, the gas supplied from one gas supply channel can spread and overlap with the gas inflow from an immediately adjacent gas supply channel. This overlap can be determined, for example, by small adjustments of the gas inflow to each gas supply channel and subsequent measurement. The result is a so-called crosstalk matrix C, whose pseudoinverse can be used as the decorrelation operator D. However, the pseudoinversion of the crosstalk matrix can be very susceptible to amplifying small measurement errors.

[0299] Further exemplary implementations for changing the operating point of the process group or its processes are explained below. The control of an actuator can be based on the dimensionless control vector Δz→ and based on the actual state of the process. For this purpose, the dimensionless control vector Δz→ based on the actual state of the process, are converted into a manipulated variable or its manipulated value, which are fed to the actuator for control.

[0300] In the case of gas supply distribution, Δz→≡Δzi (i=1 to N). The control value can be the gas flow fi≡f→ of the individual gas supply channels, for example, specified in sccm or slm. The control intervention is additive and relative to the sum of the individual gas inflows, which can be expressed as: fi,neu=fi,ist+aj⋅Δzi⋅∑ifi,ist

[0301] The optional scaling factor a j is used for relative weighting when using multiple gas supply channels j (also called gas channels).

[0302] In the case of the magnetic field distribution, Δz→≡Δzi (i=1 to N). The vertical stroke can be used as the control value hi≡h→ per support point, for example, specified in mm or µm. The adjustment is additive, which can be expressed as: hi,neu=hi,ist+aj⋅Δzi

[0303] According to various embodiments, a measurement is converted into controlled variables, whereby an individual delay time is taken into account for each controlled variable.

[0304] According to various embodiments, a correction of the measurement or the controlled variable is carried out taking into account the individual delay time (e.g. by means of a Smith predictor).

[0305] According to various embodiments, the output of the control element is corrected (e.g. alternatively or in addition to the correction of the measurement or the controlled variable) taking into account the time-delayed state of the process in the past.

[0306] According to various embodiments, the measurement or the controlled variable is corrected taking into account past control interventions.

[0307] According to various embodiments, a prediction of the operating point of the process at the time of measurement is made on the basis of the past control interventions.

[0308] In the following, various examples are described which refer to those described above and those shown in the figures.

[0309] Example 1 is a method according to claim 1 for operating a process group which carries out at least a first process and a second process, the method comprising, among other things: detecting a substrate (e.g.the actual state of the substrate) which was processed by means of the first process and subsequently by means of the second process; and controlling the process group by means of a first manipulated variable of the first process and by means of a second manipulated variable of the second process based on a result of the detection; wherein the controlling of the process group by means of the first manipulated variable is based on a first correction element which takes into account a first time period between the processing of the substrate by means of the first process and the detection of the substrate; and wherein the controlling of the process group by means of the second manipulated variable is based on a second correction element which takes into account a second time period between the processing of the substrate by means of the second process and the detection of the substrate.

[0310] Example 2 is a method comprising: controlling a first actuator, by means of which an operating point of a first process is set and / or the first process is carried out (e.g., supplied or maintained), based on a detected substrate which was processed by means of the first process and subsequently by means of a second process, and based on a first correction element; and controlling a second actuator, by means of which an operating point of a second process is set and / or the second process is carried out (e.g., supplied or maintained).maintained) based on the detected substrate which was processed by means of the first process and subsequently by means of the second process, and based on a second correction term; wherein the first correction term takes into account a first time period between processing the substrate by means of the first process and detecting the substrate; wherein the second correction term takes into account a second time period between processing the substrate by means of the second process and detecting the substrate.

[0311] Example 3 is the method according to Example 2, wherein the first correction term and the second correction term differ from each other.

[0312] Example 4 is the method according to any one of examples 1 to 3, wherein the first time period is equal to a time interval between the time at which the processing of the substrate by means of the first process takes place or is completed and the time at which the detection of the substrate takes place; and / or wherein the second time period is equal to a time interval between the time at which the processing of the substrate by means of the second process takes place or is completed and the time at which the detection of the substrate takes place.

[0313] Example 5 is the method according to any one of examples 1 to 4, wherein a difference between the first time period and the second time period is equal to a time interval between the time at which the processing of the substrate by means of the first process takes place or is completed and the time at which the processing of the substrate by means of the second process takes place or is completed.

[0314] Example 6 is the method according to any one of examples 1 to 5, wherein (for example, comprising taking the first time period into account) the first correction element takes into account a duration of the first time period and / or at least one event in the first time period; and / or wherein (for example, comprising taking the second time period into account) the second correction element takes into account a duration of the second time period and / or at least one event in the second time period.

[0315] Example 7 is the method according to one of Examples 1 to 6, wherein the result of the detection has a first process characteristic which is (e.g. only or uniquely) assigned to the first process (e.g. determined based on a first measured value), and has a second process characteristic which is (e.g. only or uniquely) assigned to the second process (e.g. determined based on a second measured value), and wherein the control of the process group is carried out by means of the first manipulated variable of the first process (or the control of the first actuator) and by means of the second manipulated variable of the second process (or the control of the second actuator) based on the first process characteristic and the second process characteristic; wherein, for example, the control of the process group is carried out by means of the first manipulated variable of the first process (or the control of the first actuator) (e.g.only) based on the first process parameter and the control of the process group by means of the second manipulated variable of the second process (or the control of the second actuator) (e.g. only) based on the second process parameter.

[0316] Example 8 is the method according to any one of examples 1 to 7, wherein the first correction element takes into account at least one (ie one or more than one, eg each) change in the first manipulated variable within the first time period, and / or wherein the second correction element takes into account at least one (ie one or more than one, eg each) change in the second manipulated variable within the second time period.

[0317] Example 9 is the method according to Example 8, wherein the first correction element takes into account and / or determines a prediction which is based on the at least one change in the first manipulated variable (e.g. a prediction about an effect of the at least one change in the first manipulated variable), and / or wherein the first correction element takes into account and / or determines a prediction which is based on the at least one change in the second manipulated variable (e.g. a prediction about an effect of the at least one change in the first manipulated variable).

[0318] Example 10 is the method according to Example 9, wherein the prediction is related to a time of detection of the substrate.

[0319] Example 11 is the method according to any one of Examples 1 to 10 (e.g., implementing the feedback correction), wherein the control of the process group by means of the first manipulated variable (or the actuation of the first actuator) is based on a first correction of the result of the detection (e.g., mapping the first correction to a manipulated value of the first manipulated variable), wherein the first correction is provided by the first correction element; and / or wherein the control of the process group by means of the second manipulated variable (or the actuation of the second actuator) is based on a second correction of the result of the detection (e.g., mapping the second correction to a manipulated value of the second manipulated variable), wherein the second correction is provided by the second correction element.

[0320] Example 12 is the method according to any one of Examples 1 to 11, wherein the first correction is based on the at least one change in the first manipulated variable or the prediction based thereon; and / or wherein the second correction is based on the at least one change in the second manipulated variable or the prediction based thereon.

[0321] Example 13 is the method according to any one of examples 1 to 12 (e.g., implementing parallel correction), wherein controlling the process group by means of the first manipulated variable (or controlling the first actuator) comprises determining a manipulated value of the first manipulated variable based on the result of the detection and correcting it by means of the first correction element; wherein controlling the process group by means of the second manipulated variable (or controlling the second actuator) comprises determining a manipulated value of the second manipulated variable based on the result of the detection and correcting it by means of the second correction element.

[0322] Example 14 is the method according to Example 13, wherein the correction of the manipulated value of the first manipulated variable is independent of a change in the first manipulated variable in the first period; and / or wherein the correction of the manipulated value of the second manipulated variable is independent of a change in the second manipulated variable in the first period.

[0323] Example 15 is the method according to any one of Examples 1 to 14, wherein the detection of the substrate and / or the control of the process group (or the control of the first and / or second actuator) are carried out in a clocked manner.

[0324] Example 16 is the method according to any one of Examples 1 to 15, wherein the first correction term and / or the second correction term are updated one or more than once taking into account a speed at which the substrate is transported, e.g. triggered by a change in speed.

[0325] Example 17 is the method according to one of examples 1 to 16, wherein for controlling the process group (or for controlling the first and / or second actuator), instructions are output (e.g. for controlling the respective actuator) which indicate by which value the first manipulated variable and / or the second manipulated variable are changed (e.g. additively or multiplicatively).

[0326] Example 18 is a control device (e.g., comprising a processor) configured to perform the method according to any one of Examples 1 to 17.

[0327] Example 19 are code segments configured, when executed by a processor, to cause the processor to perform the method of any one of Examples 1 to 17.

[0328] Example 20 is a non-transitory storage medium storing instructions (e.g., as code segments according to Example 19) configured, when executed by a processor, to cause the processor to perform the method according to any one of Examples 1 to 17.

Claims

[1] Method (100) for operating a process group which carries out at least a first process (P1) and a second process (P2), the method (100) comprising: • detecting (101) a substrate (102) which has been processed by the first process (P1) and then by the second process (P2); and • Controlling (103) the process group by means of a first manipulated variable of the first process (P1) and by means of a second manipulated variable of the second process (P2) based on a result of the detection (101); • wherein the control (103) of the process group by means of the first manipulated variable is based on a first correction element, which is implemented by a first elementary controller, which takes into account a first time period between the processing of the substrate (102) by means of the first process (P1) and the detection (101) of the substrate (102); and • wherein the control (103) of the process group by means of the second manipulated variable is based on a second correction element, which is implemented by a second elementary controller, which takes into account a second time period between the processing of the substrate (102) by means of the second process (P2) and the detection (101) of the substrate (102); • where the first elementary controller and the second elementary controller operate independently of each other. [2] The method (100) according to claim 1, wherein the first correction term and the second correction term are different from each other. [3] Method (100) according to claim 1 or 2, • wherein the first correction element takes into account a duration of the first time period and / or at least one event in the first time period; and • wherein the second correction element takes into account a duration of the second time period and / or at least one event in the second time period. [4] Method (100) according to one of claims 1 to 3, • wherein the result of the detection (101) has a first process characteristic which is assigned to the first process (P1) and a second process characteristic which is assigned to the second process (P2), and • wherein the control (103) of the process group is carried out by means of the first manipulated variable of the first process (P1) and by means of the second manipulated variable of the second process (P2) based on the first process characteristic and the second process characteristic. [5] Method (100) according to one of claims 1 to 4, • wherein the first correction element takes into account at least one change in the first manipulated variable within the first time period, and • wherein the second correction element takes into account at least one change in the second manipulated variable within the second time period. [6] Method (100) according to one of claims 1 to 5, • wherein the control (103) of the process group by means of the first manipulated variable is based on a first correction of the result of the detection (101), wherein the first correction is carried out by means of the first correction element; and • wherein the control (103) of the process group by means of the second manipulated variable is based on a second correction of the result of the detection (101), wherein the second correction is carried out by means of the second correction element. [7] Method (100) according to claim 5 and 6, • wherein the first correction is based on the at least one change in the first manipulated variable; and • wherein the second correction is based on the at least one change in the second manipulated variable. [8] Method (100) according to one of claims 1 to 7, • wherein the control (103) of the process group by means of the first manipulated variable comprises determining a manipulated value of the first manipulated variable based on the result of the detection (101) and correcting it by means of the first correction element; • wherein the control (103) of the process group by means of the second manipulated variable comprises determining a manipulated value of the second manipulated variable based on the result of the detection (101) and correcting it by means of the second correction element. [9] Method (100) according to one of claims 1 to 8, wherein the first correction term and the second correction term are updated taking into account a speed at which the substrate (102) is transported. [10] Method (100) according to one of claims 1 to 9, wherein for controlling (103) the process group, instructions are output which indicate by which value the first manipulated variable and / or the second manipulated variable are changed. [11] Control device (224) arranged to: • Controlling a first actuator, by means of which a first process (P1) is carried out, based on a detected substrate (102) which has been processed by means of the first process (P1) and then by means of a second process (P2), and based on a first correction element which is implemented by a first elementary controller; and • Controlling a second actuator by means of which a second process (P2) is carried out, based on the detected substrate (102) which was processed by means of the first process (P1) and then by means of the second process (P2), and based on a second correction element which is implemented by a second elementary controller; • wherein the first correction element takes into account a first time period between the processing of the substrate (102) by means of the first process (P1) and the detection (101) of the substrate (102); • wherein the second correction element takes into account a second time period between the processing of the substrate (102) by means of the second process (P2) and the detection (101) of the substrate (102); • where the first elementary controller and the second elementary controller operate independently of each other. [12] Non-volatile storage medium having code segments configured, when executed by a processor, to cause the processor to: • Controlling a first actuator, by means of which a first process (P1) is carried out, based on a detected substrate (102) which has been processed by means of the first process (P2) and subsequently by means of a second process (P2), and based on a first correction element which is implemented by a first elementary controller; and • Controlling a second actuator by means of which a second process (P2) is carried out, based on the detected substrate (102) which was processed by means of the first process (P1) and then by means of the second process (P2), and based on a second correction element which is implemented by a second elementary controller; • wherein the first correction element takes into account a first time period between the processing of the substrate (102) by means of the first process (P1) and the detection (101) of the substrate (102); • wherein the second correction element takes into account a second time period between the processing of the substrate (102) by means of the second process (P2) and the detection (101) of the substrate (102); • where the first elementary controller and the second elementary controller operate independently of each other.

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