WO2020169183A1

WO2020169183A1 - Sensor data extrapolation method and system

Info

Publication number: WO2020169183A1
Application number: PCT/EP2019/054082
Authority: WO
Inventors: Max DIEZ
Original assignee: Applied Materials, Inc.
Priority date: 2019-02-19
Filing date: 2019-02-19
Publication date: 2020-08-27

Abstract

A method for generating controller data, a computer program for performing the method and a system is provided. The controller data may be used to control in particular a vacuum evaporation or sputtering process. The method includes executing the following: Determining sensor data describing a state of a controllable system; determining first filter data, the determining including filtering of the sensor data with at least one first filter; determining second filter, the determining of second filter data comprising filtering the first filter data with at least one second filter, the second filter data having a higher data density than the first filter data; determining controller data based on the second filter data; and issuing the controller data to the at least one controller for controlling the state of the controllable system.

Description

SENSOR DATA EXTRAPOLATION METHOD AND SYSTEM

Field

Embodiments of the present disclosure relate to a computer-implemented method for controlling a controller, a computer program for performing the computer- implemented method and a system including at least one computer according to the present disclosure and at least one controller.

Background

Controllers typically control a system of which they may be an integral part. The system performance may be dependent on the controller and the control of the system.

Accordingly, there is a demand for an optimized control of a system.

Summary

The present disclosure therefore aims to at least partly solve or reduce the problems in the art.

In light of the above, a method for generating controller data according to independent claim 1 , a computer program for performing the method and a system according to the respective claims is provided. Further aspects, advantages, and the features are apparent from the dependent claims, the description, and the accompanying drawings.

In general, the aforementioned objective is reached by providing, in an aspect, a computer-implemented method of determining controller data. The method includes executing, on an at least one processor of at least one computer, exemplarily the following which is executed by the at least one processor:

Determining sensor data describing a state of a controllable system; determining first filter data, the determining including filtering of the sensor data with at least one first filter; determining second filter, the determining of second filter data including filtering the first filter data with at least one second filter, the second filter data having a higher data density than the first filter data; determining controller data based on the second filter data; and issuing the controller data to the at least one controller for controlling the state of the controllable system.

The controller data may be used to control in particular a vacuum evaporation or sputtering process.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following, embodiments of the present disclosure are described with reference to the appended figures. The scope is however not limited to the specific features disclosed in the context of the figures, wherein

Fig. 1 illustrates an exemplary method according to an aspect;

Fig. 2 shows a schematic exemplary embodiment; and

Fig. 3 is a schematic illustration of the system according to an aspect.

DESCRIPTION OF EMBODIMENTS

In some embodiments, which can be combined with other embodiments described herein, the computer-implemented method may include at least one of determining sensor data, determining first filter data, determining second filter data, determining controller data and issuing controller data to the at least one controller for controlling the state of a controllable system.

According to some embodiments, which can be combined with other embodiments described herein, the sensor data may describe a state of a controllable system. A controllable system may for example refer to an evaporation system, such as including e.g. an evaporator or vaporizer; or a movable system, such as e.g. a robotic system including at least one robot. A controllable system may for example be an electron beam system (i.e. a system including at least one electron beam source), an inductive evaporation system (i.e. a system including at least one inductive evaporator) or a deposition system, such as e.g. a physical vapor deposition system or a chemical vapor deposition system. A deposition system may include one or more of the following elements: one or more chambers, a reception for receiving a deposition unit such as one or more sputter cathodes, one or more guiding rolls, and one or more vacuum pumps. An evaporation system may include one or more of the following elements: One or more chambers, a reception for receiving an evaporation source, one or more guiding rolls, and one or more vacuum pumps.

The state of a controllable system may be determined with at least one sensor, the sensor providing sensor data describing the state of the system. A state of the system may be a single parameter or multiple parameters that may describe the system in the system’sentirety or an aspect of the system. A state of the system may be the state of a subsystem of the system or properties of the system or subsystem that can be determined by at least one sensor.

A sensor may measure one, additionally or alternatively at least one property of a system. A sensor may convert a property or a state of the system into at least one of a digital and an analog signal. A sensor may provide analogue or digital sensor data that describes a state of a controllable system. The analog signal may then be sampled by an analog to digital converter (ADC) that may be integrated with the sensor and additionally or alternatively may be an external device or an entity separate from the sensor, such as e.g. a programmable logic controller or a computer. A sensor as described herein may, for example, refer to a virtual sensor in a virtual system or a simulation.

Sensor data may describe a state of a virtual controllable system, for example in a simulation environment. The virtual controllable system may include virtual sensors. The virtual sensors may acquire data describing at least one of a state of the virtual system and a virtual subsystem. The virtual system may simulate, for example, critical states of a system in the real world or it may simulate use cases of the real world system.

A sensor may, for example, be a temperature sensor, a pressure sensor, an optical sensor, or a sonic sensor. The sensor may be e.g. a quartz crystal microbalance (QCM) sensor, or a light based sensor, such as a photodetector or a LiDAR (light and laser based distance) sensor.

The sensor may be a sensor that outputs e.g. an analog signal or a digital signal. The digital signal may be a sampled signal, sampled at a certain sampling rate, wherein the sampling rate may be greater than 1/250 milliseconds, or greater than1/350 milliseconds. Additionally or alternatively, the sampling rate may be smaller than 1/500 millisecond, smaller than 1/400 millisecond, or smaller than 1/300 millisecond.

The sensor data may be derived from the output of the sensor by e.g. sampling an analog output signal of the sensor with a sampling rate.

The sensor data may include invalid data sections, such as sensor-invalid data sections that are sensor data that may be sampled during a time in which the sensor is covered by e.g. a shutter or a lid or during a cleaning period of the sensor, e.g. through shaper cleaning, or wherein the sensor is overloaded.

First filter data may be determined based on the sensor data, wherein determining the first filter data includes filtering the sensor data with at least one first filter. A first filter may be a digital or an analog filter. An analog filter may be a filter built in hardware, such as e.g. electronic components, such as e.g. resistors, capacitors, inductors or integrated circuits, additionally or alternatively, an analog filter may include converting an analog signal to a digital signal and applying a filter, such as e.g. a digital (software) based filter. A digital or analogue filter may include one or more filter stages, additionally or alternatively at least one filter stage. The filter may be based on an operation of the filter in the frequency domain, additionally or alternatively in the time domain, additionally or alternatively, the filter may be an energy transfer filter. According to some embodiments, which can be combined with other embodiments described herein, the filter may be a linear or a non-linear filter. According to some embodiments, which can be combined with other embodiments described herein, the filter may be a high-pass filter, additionally or alternatively a low-pass filter. The filter may include or may be a band-pass filter.

According to some embodiments, which can be combined with other embodiments described herein, the first filter may include a band-pass filter. The first filter may include at least one of a first moving average filter and a second moving average filter different form the first moving average filter.

According to embodiments, which can be combined with other embodiments described herein, the band-pass filter may be a two-level band-pass filter. A two level band-pass filter may include two independent filtering levels. A first level of the two- level band-pass filter may include a clamping filter. A clamping filter may reduce the background noise or ambient noise, for example, by applying a clamping-band, such as e.g. a band-pass filter, a low-pass filter, or a high-pass filter with dynamically adjustable cut-off frequencies. The cut-off frequencies may be adjusted by the PLC, for example, through recursively taking into account determined first filter data. Recursively taking into account determined first filter data may include analysing the determined first filter data to adjust at least one cut-off frequency of the two-level band-pass filter. The first level of the two-level band-pass filter may beneficially remove unwanted signal artefacts that may lead to resonance within the system.

A second level of the two-level band-pass filter may include an outlier filter. An outlier filter may eliminate signal spikes and outliers, for example, by applying a band-pass filter. An outlier filter may filter for rate values that lie above a pre-determ ined rate limit. A pre-determ ined rate limit may be a value set by a user or a value determined by the at least one PLC, which may take determined first filter data into account to recursively set the properties of the outlier filter.

The first and the second level of the two-level band-pass filter may be interchangeable. A clamping and an outlier filter may be interchangeable and/or interchanged depending on a Signal-to-Noise ratio (SNR) of the signal data. For example, frequent changes in a moving average filter may indicate a low SNR. A high statistic variance value, which may be determined by a PLC (based on the signal data), may for example indicate a low SNR. A low SNR may for example be accounted for by applying a first level and a second level band-pass filter. A low SNR may beneficially be accounted for by a geometric filter, as described below, as the geometric filter may be tailored specifically to the disturbances of the signal data.

According to some embodiments, which can be combined with other embodiments described herein, first filter data may be used in the determination of first filter data, for example, in a recursive feedback loop.

Second filter data may be determined based on first filter data. The determining of second filter data may include filtering the first filter data with at least one second filter. The second filter data may have a higher data density than the first filter data.

Having a higher data density as describe herein, may refer to increasing data entries for a specific time period. Having a higher data density by increasing data entries may refer to increasing the data entries per time period from a first number of data entries before the determining of second filter data to a second number of data entries per time period after the determination of second filter data, the second number of data entries being higher than a first number of data entries per time period. The determination of the second filter data as described herein particularly includes increasing the data density as compared to the first filter data. Having a higher data density as described herein may refer to having an increased amount of data entries for a specific time period. If not stated otherwise herein, the higher data density refers to the data density of the second filter data as compared to the data density of the first filter data. A specific time period may, for example, refer to a sampling rate of at least one of the components of the controllable system, such as e.g. a sensor or a virtual sensor or a pre-determ ined time interval. A pre-determ ined time interval may refer to a time interval (pre-)set by a user or a time interval that is dependent on a clock signal of at least one of the hardware or virtual components of a controllable system or the virtual controllable system. Increasing data entries may refer to adding data entries for a specific time period in the future. Adding data entries may refer to predicting data entries based on at least one of historic or previous first filter data, historic or previous second filter data, current first filter data and current second filter data.

A second filter may, for example, be a geometric filter as described herein.

Controller data may be determined based on the second filter data. The controller data may describe data usable with at least one controller. Usable with at least one controller, as described herein, may refer to data that can be used as a control signal for at least one controller. A controller may be capable of at least one of proportional, integral and derivative control. A controller may, for example, be a PID-controller.

Controller data may be issued to the at least one controller of the controllable system. Controller data may be issued to more than one controller of the controllable system.

The system may be a real-time system. A real-time system as described herein refers to, for example, real-time control systems, real-time computing systems, or real-time simulations. The system may be a near real-time system. A response of the method or the system may be issued in less than 300 milliseconds, less than 100 milliseconds, less than 50 milliseconds, or less than 10 milliseconds. Additionally or alternatively, a system response may be issued in more than 0.1 milliseconds, or more than 1 millisecond.

According to an aspect a computer program which, when running on at least one processor for example, a processor of at least one computer or when loaded into at least one memory of at least one computer, causes the at least one computer to perform the method according to an aspect as described herein.

Alternatively or additionally an aspect relates to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, carrying information which represents the program, for example the aforementioned program, which for example includes code adapted to perform the method according to an aspect.

A computer program stored on a disc is a data file, and when the file is read out and transmitted it becomes a data stream for example in the form of a (physical, for example electrical, for example technically generated) signal. The signal can be implemented as the signal wave which is described herein. For example, the signal, for example the signal wave is constituted to be transmitted via a computer network, for example LAN, WLAN, WAN, mobile network, for example the internet. For example, the signal, for example the signal wave, is constituted to be transmitted by optic or acoustic data transmission. An aspect therefore may alternatively or additionally relate to a data stream representative of the aforementioned program.

In an aspect, a non-transitory computer-readable program storage medium may be provided on which the program according to an aspect is stored.

In an aspect, at least one computer (for example, a computer) is provided, including at least one processor (for example, a processor) and at least one memory (for example, a memory), wherein the program according to an aspect is running on the processor or is loaded into the memory, or wherein the at least one computer includes the computer-readable program storage medium according to an aspect.

In an aspect, a system is provided, including: the at least one computer according to an aspect; at least one electronic data storage device storing at least the sensor data; at least one sensor or at least one virtual sensor; and a controller for controlling the state of a controllable system, wherein the at least one computer is operably coupled to the at least one electronic data storage device for acquiring, from the at least one data storage device, at least the sensor data, and the controller for issuing a control signal to the controller for controlling the operation of the controllable system on the basis of controller data.

The present also relates to the use of the system or any embodiment thereof for determining controller data with the system. The use includes for example at least one of the following:

Determining sensor data, determining first filter data, determining second filter data, determining controller data, and issuing the controller data to the controller, according to the an aspect for use in an industrial system, for example, a production system, such as e.g. an evaporation system with a metal source or an organic source, a thermic system, such as a heat treating system, or an acting or moving system, such as e.g. robotic systems. The controller may control at least one parameter of the industrial system. Controlling the operation of the controllable system on the basis of controller data may be for example, but is not limited to, controlling a coating rate, controlling a substrate speed, controlling an applied voltage and/or a frequency.

Figure 1 illustrates an exemplary embodiment of the method according to an aspect, in which S101 encompasses determining sensor data, S102 encompasses determining first filter data, S103 encompasses determining second filter data, S104 encompasses determining controller data and S105 encompasses issuing controller data to the at least one controller of the control system. Determined controller data may influence determining the first filter data through, for example, a closed-loop control characteristic of the control method or the control circuit established by the method or the system. Determined first filter data may influence the determination of first filter data through, for example, a feedback loop in the determining of first filter data. Determined second filter data may influence the determination of second filter data through, for example, a feedback loop in the determining of second filter data. For example first filter data or second filter data of time t-1 may influence first filter data or second filter data of time t. Second filter data of time t may influence second filter data of time t+1 or t+n, where n is a number of second filter data that may be predicted or extrapolated. Influencing the determination of filter data may include, for example, adjusting the filter characteristics, such as e.g. a limiting property of a filter, a bandwidth of a filter, or a cut-off frequency of a filter. Influencing the determination of filter data may be direct or indirect. Indirectly influencing the determination of filter data may include e.g. analysing a trend in the data and adjusting the filter characteristics according to the trend. Analysing may be performed by the programmable logic controller.

Figure 2 illustrates an exemplary process 200 of the method. Sensor data 202 is determined. 210 is a first filter that includes a Band-pass filter 212, a first moving average filter 214 and a second moving average filter 216 different from the first moving average filter 214. The Band-pass filter 212 filters the determined sensor data 202. The first moving average filter 214 uses the band-pass filtered data for filtering, using a first moving average. The second moving average filter 216 uses the data determined with the first moving average filter 214 for filtering, using a second moving average.

Figure 2 exemplarily shows a Programmable Logic Controller (PLC) 260. The system may include at least one PLC configured to perform the method according to embodiments described herein. The method may be executed by at least one PLC. The PLC may include data storage for storing or buffering data determined within the method or by the system executing the method.

220, as exemplarily shown in Figure 2, is a second filter including a geometric filter 222, a third moving average filter 224 and a fourth moving average filter 226.

A filter as described herein may include one or more components for filtering and/or smoothing, such as e.g. a high-pass filter, a low-pass filter, a band-pass filter, or a moving average filter. According to some embodiments, which can be combined with other embodiments described herein, a first moving average filter and a second moving average filter may have a different window size. A first moving average filter and a second moving average filter may have the same window size.

According to some embodiments, which can be combined with other embodiments described herein, the second filter may include a geometric filter. Additionally, the second filter may include at least one of a third moving average filter and a fourth moving average filter.

According to embodiments, which can be combined with other embodiments described herein, a geometric filter may be a filter in the time domain. A geometric filter may have a geometric form in dimensions other than the time domain. A geometric filter may have a geometric form in two virtual dimensions. The geometric filter may have a two dimensional form, whose shape can, for example, be defined as polygon or whose shape is bounded by curves, such as e.g. a shape composed of circular arcs or a shape without circular arcs, such as e.g. an ellipse. The geometric filter may have a form that is a combination of at least two different shapes. A combination of different shapes may be the result of at least one of joining, subtracting, merging, intersecting, fragmenting or combining of at least two shapes.

According to some embodiments, which can be combined with other embodiments described herein, the geometric filter may have the form of an ellipse. The geometric filter in the form of an ellipse as described herein may (herein) be referred to as elliptical filter.

The geometric filter may have the form of an ellipse. A geometric filter in the form of an ellipse is not comparable to an elliptic filter that is also known as Cauer filter or Zolotarev filter.

The elliptical filter may be used in the determination of second filter data. The determination of second filter data may include comparing first filter data to the shape of the ellipse. First filter data that lies within the virtual body of the ellipse may be let through the elliptical filter. First filter data that is not within the virtual body of the ellipse may be adjusted to a value within the body of the ellipse or may be deleted. The former may be a form of damping of the data signal and may, for example, be adjusted by adjusting at least one radius of the ellipse. Adjusting at least one radius of the ellipse may be performed by the PLC. The PLC may adjust at least one radius of the ellipse based on determined second filter data or based on data, such as e.g. second filter data or controller data that is fed back with a feedback loop.

The elliptical filter may beneficially increase a controller or a PID-controller performance, additionally or alternatively reduce noise, additionally or alternatively predict second filter data and increase a data density of the controller data issued to the controller.

The geometric filter may predict or extrapolate sensor data or first filter data to determine second filter data. Sensor data may have a temporal resolution based on the sensor. The temporal resolution may be too small for a better performance of a controller. The performance of a controller may depend on the temporal resolution of controller data issued to the controller for controlling a controllable system. The geometric filter may predict or extrapolate second filter data based on a pre determined extrapolation value. The pre-determ ined extrapolation value may be a value pre-determ ined by a user or a value pre-determ ined by the PLC. For determining a pre-determined extrapolation value, the PLC may, for example, take into consideration the data density of sensor data. In cases, where the data density of sensor data is lower than a desired data density for controller data, the PLC may determine an extrapolation value to be used, for example, in the determination of second filter data to increase the data density of second filter data. As a consequence, the data density of following data determinations can be increased.

The data density of sensor data may for example have a temporal resolution of sensor data of more than 100 milliseconds, more than 200 milliseconds, or more than 300 milliseconds. Additionally or alternatively, the sensor data may have a temporal resolution of less than 1000 milliseconds, less than 700 milliseconds, or less than 500 milliseconds. A desired data density of controller data may have a temporal resolution of controller data of less than 50 milliseconds, less than 40 milliseconds, less than 30 milliseconds, or less than 20 milliseconds. A desired data density of controller data may have a temporal resolution of more than 1 millisecond, more than 5 milliseconds, or more than 7 milliseconds. To achieve a desired data density for controller data, the PLC may use the data density of sensor data and compare it, for example, to the desired data density of the controller data. The comparison of the data density of sensor data and the desired data density of controller data may result in an extrapolation value that describes the number of necessary extrapolations or predictions to e.g. achieve a desired data density of the controller data. As a non-limiting example, a data density of a sensor, such as e.g. a quartz crystal microbalance (QCM) sensor may have a data density of one sample per 300 milliseconds. A desired data density, for example, may be one per 10 milliseconds for e.g. controller data to be issued to the at least one controller. To achieve a data density of one per 10 milliseconds, determining second filter data may introduce 29 predicted or extrapolated samples with the sample of the sensor in the timespan of 300 milliseconds. The second filter data may then have a data density that is equal to the desired data density.

A desired data density may be pre-determ ined or may be a value dependent on the at least one controller of the controllable system.

The virtual dimensions of the geometric filter may be based on the sensor data. A first virtual dimension may be based on a linear fit of the at least two last sensor data entries. The first virtual dimension may be based on a combination of the linear fit and a moving median of a moving median window. The moving median window may have a size of more than two sensor data entries, more than three sensor data entries, more than four sensor data entries. Additionally or alternatively, the moving median window may have a size of smaller than ten sensor data entries, smaller than 8 sensor data entries, or smaller than 6 sensor data entries. Additionally or alternatively, the moving median window may have a window size of substantially five sensor data entries. A second virtual dimension may be based on the derivative of a parabolic fit of the last three sensor data entries. A second virtual dimension may be based on the derivative of an nth order fit of the last n sensor data entries.

A virtual dimension or the virtual dimensions may be based on sensor data in the time-domain. Sensor data may be transformed into the time-domain from a frequency domain by an inverse Fourier transform, such as with for example an inverse fast Fourier transform (iFFT) to determine a virtual dimension based on the sensor data. The virtual dimensions may span a virtual triangle with the first virtual dimension and the second virtual dimension. The determination of second filter data may be based on a centroid of the virtual triangle. The determination of predicted or extrapolated second filter data may be based on a centroid of the virtual triangle. The virtual position of the centroid of the virtual triangle may then be extrapolated in time (into the future). The extrapolation may be filtered with a geometric filter, such as e.g. an elliptical filter.

Extrapolated or predicted second filter data may increase the data density that may have, for example, a higher temporal resolution or a desired data density.

According to some embodiments, which can be combined with other embodiments described herein, a third moving average filter and a fourth moving average filter may have a different window size. A third moving average filter and a fourth moving average filter may have the same window size.

According to some embodiments which can be combined with other embodiments described herein, a moving average filter may have a window size of more than 2, more than 5, more than 10, more than 20, or more than 30 chronological data entries. Additionally or alternatively, a moving average filter may have a window size of less than 100, less than 50, or less than 20 data entries. Moving average filters may have the same or different window sizes. The number of data entries of a window size as described herein, may also refer to the number of points in the moving average filter.

A moving average filter as described herein may refer to, for example, a simple moving average, a cumulative moving average, a weighted moving average, an exponential moving average, or a moving median.

The window of the moving average filter may be a window function, such as e.g. a rectangular window, a B-spline window, a sine window, an adjustable window, such as e.g. a Gaussian window or a Dolph-Chebyshev window, a hybrid window, such as a Planck-Bessel or a Hann-Possion window.

According to some embodiments, which can be combined with other embodiments described herein, window sizes of the moving average filter may be increased or window sizes of moving average filters may be decreased. A moving average filter may introduce“phase lag”, which, in a time-domain, corresponds to a time-lag of the filtered data, depending on the window size of the moving average filter. An allowed time-lag may be pre-defined by e.g. a user or may be pre-determ ined by e.g. the PLC before adjusting a window size of the moving average filter. Adjusting the window size of the at least one moving average filter may be performed by the PLC. Adjusting a window size of the moving average filter may beneficially increase the smoothing of the filtered data or may beneficially reduce smoothing or reduce the introduction of time-lag.

A geometric filter may beneficially improve the SNR. The geometric filter may beneficially predict future data points or add lacking data points. The prediction or extrapolation may, for example, be based on extrapolating first filter data with an extrapolation coefficient or extrapolating a centroid of a virtual triangle with an extrapolation coefficient. The geometric filter may beneficially have no phase lag.

Figure 2 exemplarily shows a feedback loop 240 that feeds back determined first filter data to the Band-pass filter 212 to adjust the filter characteristics of the Band-pass filter 212. 242 is a feedback loop that feeds back determined second filter data to the geometric filter 222. The feedback may adjust the characteristics of the geometric filter 222, such as e.g. at least one radius of an elliptic filter or a property of a geometric filter. A property of a geometric filter may influence the filter characteristics of the geometric filter.

According to some embodiments, which can be combined with other embodiments described herein, the method can be used in parallel on a plurality of sensor data.

Figure 2 exemplarily shows controller data 230 being issued to the at least one controller of the control system.

Figure 3 is a schematic illustration of the system 1 according to an aspect. The system as a whole is identified by reference sign 1 and includes a computer 2, an electronic data storage device (such as a hard disc) 3 for storing at least sensor data and a controller 4. The components of the system 1 have the functionalities and properties explained herein with regard to an aspect of this disclosure.

The method as described herein is for example a computer implemented method. For example, the method can be executed by a computer, for example, at least one computer. An embodiment of the computer implemented method is a use of the computer for performing a data processing method. An embodiment of the computer implemented method is a method concerning the operation of the computer such that the computer is operated to perform the method.

The computer for example includes at least one processor and for example at least one memory in order to (technically) process the data, for example electronically and/or optically. The processor being for example made of a substance or composition which is a semiconductor, for example at least partly n- and/or p-doped semiconductor, for example at least one of II-, III-, IV-, V-, Vl-semiconductor materials, for example (doped) silicon and/or gallium arsenide.

The calculating or determining described is for example performed by a computer. The determining or the calculating is for example determining data within the framework of the technical method, for example within the framework of a program. A computer is for example any kind of data processing device, for example an electronic data processing device. A computer can be a device which is generally thought of as such, for example desktop PCs, notebooks, netbooks, etc., but can also be any programmable apparatus, such as for example a mobile phone or an embedded processor. A Computer may also be a programmable logic controller (PLC), a microcontroller, a system-on-a-chip (SoC), an FPGA (field programmable gate array), an integrated circuit (1C) or an application-specific integrated circuit (ASIC) or a quantum computer.

A computer can for example include a system (network) of "sub-computers", wherein each sub-computer represents an operative computer. The term "computer" includes a cloud computer, for example a cloud server. The term "cloud computer" includes a cloud computer system which for example includes a system of at least one cloud computer and for example a plurality of operatively interconnected cloud computers such as a server farm. Such a cloud computer may be connected to a wide area network such as the world wide web (WWW) and located in a so-called cloud of computers which are all connected to the world wide web. Such an infrastructure is used for "cloud computing", which describes computation, software, data access and storage services which do not require the end user to know the physical location and/or configuration of the computer delivering a specific service. For example, the term "cloud" is used in this respect as a metaphor for the Internet (world wide web). For example, the cloud provides computing infrastructure as a service (laaS). The cloud computer can function as a virtual host for an operating system and/or data processing application which is used to execute the method or embodiments of the method as described herein. The cloud computer is for example an elastic compute cloud (EC2).

A computer for example includes interfaces in order to receive or output data and/or perform an analogue-to-digital conversion. The data are for example data which represent physical properties and/or which are generated from technical signals. The technical signals are for example generated by (technical) detection devices (such as for example devices for detecting material properties) and/or (technical) analytical devices (such as for example devices for performing sensing methods), wherein the technical signals are for example electrical or optical signals.

The technical signals for example represent the data received or outputted by the computer. The computer may be operatively coupled to a display device which allows information outputted by the computer to be displayed, for example to a user. One example of a display device is a virtual reality device or an augmented reality device (also referred to as virtual reality glasses or augmented reality glasses) which can be used as "goggles" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device or a virtual reality device can be used both to input information into the computer by user interaction and to display information outputted by the computer.

Another example of a display device would be a standard computer monitor including for example a liquid crystal display or an organic light emitting diode (OLED) display operatively coupled to the computer for receiving display control data from the computer for generating signals used to display image information content on the display device. The monitor may also be the monitor of a portable, for example handheld, device such as a smart phone or personal digital assistant or digital media player.

Aspects, as described herein, also relate to a program which, when running on a computer, causes the computer to perform one or more or all parts of the method described herein and/or to a program storage medium on which the program is stored (in particular in a non-transitory form) and/or to a computer including said program storage medium and/or to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, carrying information which represents the program, for example the aforementioned program, which for example includes code means which are adapted to perform any or all of the parts of the method described herein.

Elements of the computer program can be embodied by hardware and/or software (this includes firmware, resident software, micro-code, etc.). Elements of the computer program can take the form of a computer program product which can be embodied by a computer-usable, for example computer-readable data storage medium including computer-usable, for example computer-readable program instructions, "code" or a "computer program" embodied in said data storage medium for use on or in connection with the instruction-executing system.

Such a system can be a computer; a computer can be a data processing device including a unit for executing the elements of the computer program and/or the program in accordance with the aspects and/or embodiments herein, for example a data processing device including a digital processor (central processing unit or CPU) which executes the computer program elements, and optionally a volatile memory (for example a random access memory or RAM) for storing data used for and/or produced by executing the computer program elements. A computer-usable, for example, computer-readable data storage medium can be any data storage medium which can include, store, communicate, propagate or transport the program for use on or in connection with the instruction-executing system, apparatus or device.

The computer-usable, for example computer-readable, data storage medium can for example be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device or a medium of propagation such as for example the Internet. The computer-usable or computer-readable data storage medium could even for example be paper or another suitable medium onto which the program is printed, since the program could be electronically captured, for example by optically scanning the paper or other suitable medium, and then compiled, interpreted or otherwise processed in a suitable manner. The data storage medium may be a non-volatile data storage medium. The computer program product and any software and/or hardware described here may be used to perform the functions according to aspects and/or embodiments described herein. The computer and/or data processing device can for example include a guidance information device which includes a unit for outputting guidance information. The guidance information can be outputted, for example to a user, visually by a visual indicating unit (for example, a monitor and/or a lamp) and/or acoustically by an acoustic indicating unit (for example, a loudspeaker and/or a digital speech output device) and/or tactilely by a tactile indicating unit (for example, a vibrating element or a vibration element incorporated into an instrument). For the purpose of this document, a computer is a technical computer which for example includes technical, for example tangible components, for example mechanical and/or electronic components. Any device mentioned as such in this document is a technical and for example tangible device.

The expression "acquiring data" for example encompasses (within the framework of a computer implemented method) the scenario in which the data are determined by the computer implemented method or program. Determining data for example encompasses measuring physical quantities and transforming the measured values into data, for example digital data, and/or computing (and e.g. outputting) the data by a computer and for example within the framework of the method.

The“determining” as described herein for example includes or consists of issuing a command to perform the determination described herein. For example, the determining includes or consists of issuing a command to cause a computer, for example a remote computer, for example a remote server, for example in the cloud, to perform the determination. Alternatively or additionally, the “determination” as described herein for example includes or consists of receiving the data resulting from the determination described herein, for example receiving the resulting data from the remote computer, for example from that remote computer which has been caused to perform the determination.

The meaning of "acquiring data" also for example encompasses the scenario in which the data are received or retrieved by (e.g. input to) the computer implemented method or program, for example from another program, a previous part of the method or a data storage medium, for example for further processing by the computer implemented method or program. Generation of the data to be acquired may but need not be part of the method.

The expression "acquiring data" can therefore also for example mean waiting to receive data and/or receiving the data. The received data can for example be inputted via an interface. The expression "acquiring data" can also mean that the computer implemented method or program (actively) receives or retrieves the data from a data source, for instance a data storage medium (such as for example a ROM, RAM, database, hard drive, etc.), or via the interface (for instance, from another computer or a network).

The data acquired by the disclosed method or device, respectively, may be acquired from a database located in a data storage device which is operably coupled to a computer for data transfer between the database and the computer, for example from the database to the computer. The computer acquires the data for use as an input for the determination of data. The determined data can be output again to the same or another database to be stored for later use. The database or database used for implementing the disclosed method can be located on network data storage device or a network server (for example, a cloud data storage device or a cloud server) or a local data storage device (such as a mass storage device operably connected to at least one computer executing the disclosed method) or on an internal data storage device (such as: The data can be made "ready for use" by performing an additional activity before the acquiring. In accordance with this additionalactivity, the data are generated in order to be acquired.

The data are for example detected or captured (for example by an analytical device, such as a sensor or an analyzer). Alternatively or additionally, the data are inputted in accordance with the additional activity, for instance via interfaces. The data generated can for example be inputted (for instance into the computer). In accordance with the additional activity (which precedes the acquiring), the data can also be provided by performing the additional activity of storing the data in a data storage medium (such as for example a ROM, RAM, CD and/or hard drive), such that they are ready for use within the framework of the method or program. The acquiring of "acquiring data" can therefore also involve commanding a device to obtain and/or provide the data to be acquired. In order to distinguish the different data used by the present method, the data are denoted (i.e. referred to) as "XY data" and the like and are defined in terms of the information which they describe, which may then be referred to as "XY information" and the like.

One advantage may be that the method can be easily deployed on nearly any platform, such as computers as described herein. Another advantage may be that the computation of controller data is relatively cheap in the sense that it is time efficient.

Referring to an elliptic filter in the present disclosure is not comparable to the commonly known elliptic filter. It rather uses the shape of an ellipse to filter signals. The shape of the ellipse may be parametrizable to influence e.g. a data density, or a signal to noise ratio (SNR), or a degree of smoothness of the data, or the performance of a controller that receives the controller data, or sensor invalid sections or rate invalid sections.

Claims

1. A computer-implemented method of generating controller data for optimizing a controller performance of a controllable system, the method comprising:

- determining sensor data, the sensor data describing a state of the controllable system;

- determining first filter data, the determining of the first filter data comprising filtering the sensor data with at least one first filter,

- determining second filter data, based on first filter data, the determining of the second filter data comprising filtering the first filter data with at least one second filter, the second filter data having a higher data density than the first filter data,

- determining controller data based on the second filter data,

- issuing the controller data to the at least one controller for controlling the state of the controllable system.

2. The computer-implemented method according to claim 1 , wherein the first filter is a band-pass filter.

3. The computer-implemented method according to any of the preceding claims, wherein the second filter is a geometric filter.

4. The computer-implemented method according to any of the preceding claims, wherein the sensor data has a data density smaller than the controller data.

5. The computer-implemented method according to any of the preceding claims, wherein the second filter is a geometric filter.

6. The computer-implemented method according to any of the preceding claims, wherein the geometric filter has a shape that comprises non-circular curves.

7. The computer-implemented method according to any of the preceding claims, wherein the second filter substantially resembles an ellipse.

8. The computer-implemented method according to any of the preceding claims, wherein the controller is a controller in an industrial system.

9. The computer-implemented method according to any of the preceding claims, wherein the controller controls at least one parameter of an evaporation system with at least one of a metal source, an organic source and a ceramic source.

10. The computer-implemented method according to any of the preceding claims, wherein the first filter data influences at least one parameter of the first filter.

11. The computer-implemented method according to any of the preceding claims, wherein the controller data influences at least one parameter of the second filter.

12. The computer-implemented method according to any of the preceding claims, wherein determining the first filter data further comprises the use of a first moving average unit.

13. The computer-implemented method according to any of the preceding claims, wherein determining the second filter data further comprises the use of a second moving average unit.

14. A computer program which, when running on a computer or when loaded onto a computer, causes the computer to perform the method according to any one of the preceding claims; and/or a program storage medium on which the computer program is stored; and/or a computer comprising at least one processor and a memory and/or the program storage medium, wherein the computer program is running on the computer or loaded into the memory of the computer, and/or a signal wave or a digital signal wave, carrying information which represents the computer program; and/or a data stream which is representative of the computer program.

15. A system comprising: at least one computer comprising at least one processor and a memory and/or the program storage medium, wherein the computer is configured to execute the computer program of claim 14 on the computer or to load the computer program of claim 14 into the memory of the computer; at least one sensor for providing sensor data; at least one electronic storage device storing at least the sensor data; and a controller for controlling the state of a controllable system, wherein the at least one computer is operably coupled to the at least one electronic storage device for acquiring, from the at least one storage device, at least the sensor data, and the controller for issuing a control signal to the controller for controlling the operation of the controllable system on the basis of controller data.