Method of monitoring a PVD machine, monitoring system, and PVD machine system

Abstract

A method of monitoring a physical vapor deposition (PVD) machine is described. The PVD machine comprises a plurality of evaporator boats (14) arranged in a fixture (16), and a plurality of supply wire drives (24) being configured to advance supply wires (20) to the evaporator boats (14). The method comprises the steps of - capturing an image of at least one evaporator boat (14) of the plurality of evaporator boats (14); - extracting at least one image portion of the image corresponding to the at least one evaporator boat (14); - processing the at least one extracted image portion, thereby obtaining at least one processed image portion, wherein processing the at least one extracted image portion comprises detecting a pool of molten material, detecting a supply wire (20), detecting a contact point between the supply wire (20) and the at least one evaporator boat, detecting obscured areas, and / or correcting the at least one extracted image portion for perspective; and - generating visualization data based on the at least one processed image portion. Further, a monitoring system and a PVD machine system are described.

Description

[0001] Method of monitoring a PVD machine, monitoring system, and PVD machine system

[0002] The present invention generally relates to a method of monitoring a physical vapor deposition (PVD) machine. The present invention further relates to a monitoring system for a PVD machine, and to a PVD machine system.

[0003] A PVD machine is a machine with which a material is deposited on a substrate. The material forms a layer consisting of a metal and / or a metal oxide, such as aluminum oxide, on the substrate. The substrate can be a thin film (such as a plastic foil, paper or card) which may be used in different applications (such as a packaging material-used for packaging food). The layer may be used to provide a barrier against ingress of gas and / or water and / or light. The layer provided on the foil, paper or card may be transparent and / or mechanically dense and / or mechanically stable.

[0004] In some types of PVD machines, evaporator boats are used for melting and evaporating the deposition material. The evaporator boats are arranged in a process chamber so that a vacuum can be established for conducting the deposition process. The deposition material is applied to the evaporator boats by supply wires that are advanced towards the evaporator boats by supply wire drives. The evaporator boats are heated in order to melt and evaporate the deposition material.

[0005] Typically, the evaporator boats are controlled manually by the operator. The manual process involves the operator looking through a transparent window to visually inspect a pool of molten material on the evaporator boat and adjusting electrically via power, voltage or current to ensure an optimized pool shape of evaporant material based on variables such as wire feed rate, evaporant material, different processes (e.g. AIOx, Dark Night and AluBond) and evaporator boat age. Failure to do so will result in poor product quality and a reduction in life of the evaporator boats.

[0006] In a typical metallizing process the operator requires to make adjustments every 5 minutes to up to 60 evaporators (or even more) for a 1 hr cycle time to ensure good product quality. This operator dependent task requires a very experienced machine operator to ensure product quality, maximizing yield and increasing the life of the evaporator boat, as the contrast between the pool and boat surface is typically low and thus difficult to assess correctly.

[0007] The object of the present invention is to facilitate the control of PVD machines.

[0008] According to the present invention, the problem is solved by a method of monitoring a physical vapor deposition (PVD) machine. The PVD machine comprises a plurality of evaporator boats arranged in a fixture, and a plurality of supply wire drives being configured to advance supply wires to the evaporator boats. The method comprises the steps of capturing, by means of at least one camera, an image of at least one evaporator boat of the plurality of evaporator boats; extracting, by means of an image processing module, at least one image portion of the image corresponding to the at least one evaporator boat; processing, by the image processing module and / or by a machine-learning module, the at least one extracted image portion, thereby obtaining at least one processed image portion, wherein processing the at least one extracted image portion comprises detecting a pool of molten material, detecting a supply wire, detecting a contact point between the supply wire and the at least one evaporator boat, detecting obscured areas, and / or correcting the at least one extracted image portion for perspective; and generating, by a visualization module, visualization data based on the at least one processed image portion.

[0009] Therein and in the following, the term “module” is understood to describe suitable hardware, suitable software, or a combination of hardware and software that is configured to have a certain functionality.

[0010] The hardware may, inter alia, comprise a CPU, a GPU, an FPGA, an ASIC, or other types of electronic circuitry.

[0011] Further, the term “visualization data” is understood to denote still images that may be generated in certain time intervals and / or a continuous stream of images, i.e. a video. Obscured areas are portions of the image that are obscured by components of the PVD machine or other objects. For example, the obscured areas may be caused by deposits or rather debris on a portion of the fixture holding the evaporator boats in place.

[0012] The method according to the present invention is based on the idea to facilitate controlling the PVD machine by providing additional information on an operational state of the PVD machine to an operator by processing the image of the at least one evaporator boat.

[0013] More precisely, the generated visualization data may comprise information on the detected pool of molten material, the detected supply wire, the detected contact point, and / or the detected obscured areas. Thus, instead of having to inspect the evaporator boats through a window or based on raw camera images, processed images of the at least one evaporator boat are provided to the operator that are easier to interpret and thus facilitate control of the PVD machine.

[0014] Alternatively or additionally, the at least one extracted image portion is corrected for perspective. Correcting the at least one extracted image portion for perspective distortions also makes it easier for an operator to assess whether the PVD machine is operating within nominal parameters.

[0015] For example, the step of correcting the at least one extracted image portion for perspective may be performed by the image processing module.

[0016] The step(s) of detecting the pool of molten material, detecting the supply wire, detecting the contact point between the supply wire and the at least one evaporator boat, and / or detecting the obscured areas may be performed by the machinelearning module.

[0017] In fact, the machine-learning module may be pre-trained to detect the pool, supply wire, contact point, and / or obscured areas, as will be described in more detail below.

[0018] Correctly identifying the obscured areas can lead to an enhanced accuracy of detecting the pool, the supply wire, and / or the contact point. Further, providing information on the detected obscured areas in the at least one processed image portion helps the operator in assessing whether the PVD machine needs to be cleaned of debris.

[0019] Further, it is also conceivable that the machine-learning module may generate a warning signal if more than predefined threshold of the at least one image portion corresponds to an obscured area, and / or if the obscured areas cover the pool, wire, and / or (presumed) contact point. Thus, an operator may be automatically warned that the PVD machine has to be cleaned of the debris.

[0020] An aspect of the present invention provides that the fixture is configured to supply energy from an energy source to the evaporator boats.

[0021] A control signal may be generated by a control module for the energy source and / or for the supply wire drives based on the detected pool, supply wire, and / or contact point.

[0022] Particularly, the control module may automatically control the energy source and / or the supply wire drives such that an optimal pool shape, an optimal pool size, and / or an optimal contact point is maintained.

[0023] However, it is still possible that a human operator supervising the PVD machine may manually adjust individual settings of the PVD machine, such as the energy supplied to the evaporator boats and / or the supply wire speed.

[0024] According to an aspect of the present invention, the at least one extracted image portion is corrected for perspective by means of the image processing module, thereby obtaining at least one corrected image portion, wherein the pool, the supply wire, the contact point, and / or the obscured areas are / is detected in the at least one corrected image portion. Thus, before the extracted image portions are provided to the machine-learning module for detection of the pool, wire, contact point, and / or the obscured areas, the at least one image portion is pre-processed by the image processing module in order to account for perspective distortion (e.g. homography) of the at least one evaporator boat depicted in the at least one image portion. This may enhance the accuracy of detecting the pool, the supply wire, the contact point, and / or the obscured areas.

[0025] In an embodiment of the present invention, the at least one extracted image portion is transformed to a standard format. Therein, the term “format” relates to the geometry of the extracted image portion, particularly to the dimensions of the extracted image portion.

[0026] Thus, it is easier for an operator to assess information comprised in the extracted image portions, as all extracted image portions are converted into the standard format and are thus easily comparable.

[0027] Further, as the at least one corrected image portion has a standard format, the machine-learning module can be specifically trained for that format, which may further enhance the achievable detection accuracy.

[0028] For example, the standard format may be a rectangle having a predefined length and a predefined width.

[0029] According to another aspect of the present invention, the at least one image comprises at least two evaporator boats. Thus, less cameras are necessary for capturing images of all evaporator boats, such that the costs for controlling the PVD machine are reduced.

[0030] For example, each camera may capture images of three evaporator boats.

[0031] In a further embodiment of the present invention, the at least one image portion is augmented with additional information regarding the detected pool, wire, contact point, and / or obscured areas by means of the machine-learning module, thereby obtaining at least one augmented image portion, wherein the visualization data is generated based on the at least one augmented image portion. In general, the additional information facilitates recognizing the pool, wire, contact point, and / or obscured areas in the at least one image portion. Thus, supervision and control of the PVD machine is facilitated.

[0032] The at least one augmented image portion or rather the visualization data generated based on the at least one augmented image portion may be displayed on a display. Thus, instead of the raw images captured by the at least one camera, the augmented images comprising the additional information may be displayed to an operator of the PVD machine, thereby facilitating supervision and control of the PVD machine.

[0033] The augmented image portion may comprise colors, markers, lines, and / or text labels marking the detected pool, wire, contact point, and / or obscured areas. Thus, the pool, wire, contact point, and / or obscured areas are easier to discern for an operator of the PVD machine, as the pool, wire, contact point, and / or obscured areas may be clearly marked.

[0034] However, it is to be understood that the pool, wire, contact point, and / or obscured areas may be marked in any other suitable way.

[0035] A further aspect of the present invention provides that at least one artificial image portion is generated by means of the machine-learning module based on the at least one image portion and based on the detected pool, wire, contact point, and / or obscured areas, wherein the detected pool, wire, contact point, and / or obscured areas are represented in the at least one artificial image portion. In general, the at least one artificial image portion may be generated such that the detected pool, wire, contact point, and / or obscured areas are easily discernable in the at least one artificial image portion, thereby facilitating supervision and control of the PVD machine.

[0036] The visualization data may be generated based on the at least one artificial image portion.

[0037] For example, the at least one artificial image portion may comprise colors, markers, lines, and / or text labels marking the detected pool, wire, contact point, and / or obscured areas.

[0038] Therein and in the following, the term “artificial image portion” is understood to denote an image portion that is generated by the machine-learning module from scratch, while the term “augmented image portion” is understood to denote an image portion captured by the camera that is being adapted by the machinelearning module.

[0039] Particularly, the at least one artificial image portion or rather visualization data generated based on the at least one artificial image portion is displayed on a display. Thus, instead of the raw images captured by the at least one camera, the artificial images comprising the additional information may be displayed to an operator of the PVD machine, thereby facilitating supervision and control of the PVD machine. In an embodiment of the present invention, process data relating to the PVD machine is generated, wherein the visualization data is generated based on the process data. Particularly, the process data comprises information on a pool size, the wire, the contact point, the obscured areas, the at least one camera, and / or energy supplied to the at least one evaporator boat. Thus, in addition to the processed image portion(s), the process data may be displayed to an operator of the PVD machine, thereby further facilitating supervision and control of the PVD machine.

[0040] For example, the process data may comprise at least one process parameter such as a wire feed rate, a wire position, a wire set point, a heating current, a heating voltage, a camera position, a camera exposure time, a camera trigger time, an image width, an image height, a strobe speed, etc.

[0041] In fact, the generated visualization data may comprise a diagram of the at least one process parameter over time. Thus, information on an evolution of the at least one process parameter over time may be provided to the operator, which further facilitates supervision and control of the PVD machine, particularly recognizing anomalous behavior of the PVD machine.

[0042] The visualization data may be generated continuously, periodically, and / or on demand. Particularly, the visualization data may be saved in a memory.

[0043] Likewise, the process data may be generated continuously, periodically, and / or on demand.

[0044] Therein and in the following, the term “on demand” is understood to denote that the operator may request an update of the visualization data, for example in between periodical automatic updates.

[0045] Saving the visualization data in the memory enables a later review of the visualization data. This facilitates the supervision and control of the PVD machine, as an evolution of an operating state of the PVD machine as a whole or of individual evaporator boats can easily be reviewed, which may be particularly useful for trouble shooting purposes.

[0046] According to an aspect of the present invention, at least the visualization data is streamed to an external monitoring device. Optionally, the process data may be streamed to the external monitoring device. Thus, the PVD machine may be remotely monitored, for example from a central monitoring room that is located on the same production site as the PVD machine or even by operators that are located off-site. In fact, multiple PVD machines may be monitored via the same external monitoring device receiving visualization data and, optionally, process data from the multiple PVD machines.

[0047] Further, streaming the visualization data enables an improved customer support, as the visualization data may be streamed to an expert operator of the manufacturer of the PVD machine, which may assist in trouble shooting and / or optimizing process parameters of the PVD machine remotely, i.e. without having to be on-site.

[0048] For example, the external monitoring device may be a PC, a laptop, a smartphone, a tablet, or another type of smart device.

[0049] In an embodiment of the present invention, the machine-learning module comprises a first machine-learning sub-module and a second machine-learning sub-module, wherein the first machine-learning sub-module is trained to detect the pool of molten material, and wherein the second machine-learning sub-module is trained to detect the supply wire and / or the contact point. In other words, different sub-modules of the machine-learning module are specifically pre-trained to detect the pool of molten material on one hand, and the supply wire and / or the contact point on the other hand. This way, the detection accuracy of the individual machinelearning sub-modules is enhanced, as specialized training can be applied to the individual sub-modules.

[0050] The image processing module may be a sub-module of the machine-learning module. In other words, the image processing module may be a machine-learning sub-module of the machine-learning module, wherein the image processing module is pre-trained to extract the at least one image portion from the image captured by the at least one camera.

[0051] More precisely, the image processing module may be pre-trained to detect evaporator boats in the image captured by the at least one camera, and to cut out the detected evaporator boats from the image, thereby obtaining the at least one image portion. The machine-learning module may comprise at least one artificial neural network, wherein the at least one artificial neural network is trained to detect the pool, the supply wire, the contact point, and / or the obscured areas.

[0052] In fact, the machine-learning module may comprise a plurality of artificial neural networks, wherein the individual artificial neural networks may be pre-trained to at least one of detect the pool of molten material in the at least one image portion, detect the supply wire in the at least one image portion, detect the contact point in the at least one image portion, detect obscured areas in the at least one image portion, extract the at least one image portion, and correct the at least one image portion for perspective.

[0053] According to the present invention, the problem further is solved by a monitoring system for a PVD machine. The monitoring system comprises at least one camera, an image processing module, a machine-learning module, and a visualization module. The monitoring system is configured to perform the method according to any one of the variants described above.

[0054] Regarding the further advantages and properties of the monitoring system, reference is made to the explanations given above with respect to the method of monitoring a PVD machine, which also hold for the monitoring system and vice versa.

[0055] In an embodiment of the present invention, the monitoring system comprises at least one master monitoring device configured to display the visualization data and / or at least one subsidiary monitoring device configured to display the visualization data.

[0056] The master monitoring device may be configured to display visualization data associated with different cameras simultaneously and / or selectively. In other words, the operator may select a camera or a set of cameras, and the corresponding visualization data may be displayed on the master monitoring device. Thus, the master monitoring device enables supervision of evaporator boats captured by multiple cameras or even evaporator boats of multiple PVD machines.

[0057] For example, the master monitoring device may be a PC, a laptop, a smartphone, a tablet, or another type of smart device. The subsidiary monitoring device may be configured to display visualization data associated with a predefined camera or a predefined set of cameras. Thus, the subsidiary monitoring device enables supervision of a predefined set of evaporator boats.

[0058] The at least one master monitoring device may comprise a user interface, wherein the user interface is configured to receive a user input, and wherein the monitoring system is configured to set operational parameters of the PVD machine based on the user input received. In other words, the operator may adapt operational parameters of the PVD machine, such as a wire feed rate or an energy supplied to the individual evaporator boats, via the master monitoring device.

[0059] For example, the master monitoring device may be mounted to the PVD machine. As another example, the master monitoring device may be mounted near the PVD machine or in a control room.

[0060] According to an aspect of the present invention, the at least one subsidiary monitoring device is mounted to the PVD machine, particularly adjacent to the at least one camera and / or adjacent to controls of the PVD machine. In fact, the at least one subsidiary monitoring device may be mounted adjacent to the at least one camera associated with the visualization data displayed on the at least one subsidiary monitoring device. Thus, it is particularly easy for an operator to assess which portions of the PVD machine the displayed visualization data corresponds to.

[0061] According to the present invention, the problem further is solved by a PVD machine system. The PVD machine system comprises a PVD machine, and a monitoring system according to any one of the variants described above.

[0062] The monitoring system may at least partially be integrated into the PVD machine, particularly completely. For example, the at least one camera, the image processing module, the machine-learning module, and / or the visualization module may be integrated into the PVD machine. As another example, the at least one master monitoring device and / or the at least one subsidiary monitoring device may be mounted to the PVD machine.

[0063] However, it is to be understood that the monitoring system may be established separately from the PVD machine at least partially. For example, the at least one master monitoring device and / or the at least one subsidiary monitoring device may be provided separately from the PVD machine.

[0064] Regarding the further advantages and properties of the PVD machine system, reference is made to the explanations given above with respect to the method of monitoring a PVD machine and with respect to the monitoring system, which also hold for the PVD machine system and vice versa.

[0065] The foregoing aspects and many of the attendant advantages of the claimed subject matter will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 schematically shows a first variant of a PVD machine according to the present invention;

[0067] Figure 2 schematically shows a second variant of a PVD machine according to the present invention;

[0068] Figure 3 is a schematic view of a fixture for evaporator boats as used in the machine of Figures 1 and 2;

[0069] Figure 4 schematically shows a control system according to the present invention;

[0070] Figure 5 shows a flow chart of a method of controlling a PVD machine according to the present disclosure;

[0071] Figures 6 to 8 show images illustrating individual steps of the method of Figure 5;

[0072] The detailed description set forth below in connection with the appended drawings, where like numerals reference like elements, is intended as a description of various embodiments of the disclosed subject matter and is not intended to represent the only embodiments. Each embodiment described in this disclosure is provided merely as an example or illustration and should not be construed as preferred or advantageous over other embodiments. The illustrative examples provided herein are not intended to be exhaustive or to limit the claimed subject matter to the precise forms disclosed. For the purposes of the present disclosure, the phrase “at least one of A, B, and C”, for example, means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C), including all further possible permutations when more than three elements are listed. In other words, the term “at least one of A and B” generally means “A and / or B”, namely “A” alone, “B” alone or “A and B”.

[0073] Figures 1 and 2 show components of a PVD machine system comprising a PVD machine.

[0074] The PVD machine comprises a process drum 10 or free span rollers 11 around which a substrate 12 in the form of a web is guided. The substrate 12 can be a thin plastic foil which is used for packaging food.

[0075] Without restriction of generality, the exemplary embodiment of the PVD machine shown in Figure 1 is described hereinafter. However, it is to be understood that the explanations given hereinafter likewise apply to the embodiment shown in Figure 2.

[0076] For providing a deposition material to be deposited on the substrate 12, a plurality of evaporator boats 14 is provided in the vicinity of the process drum 10. Evaporator boats 14 are arranged in a fixture 16 (please see Figure 2) so as to form a row of adjacent evaporator boats, the row being arranged in parallel with the axis of rotation of the process drum 10 so that the entirety of the evaporator boats 14 extends over the entire width of substrate 12. The arrangement of the evaporator boats does not necessarily need to be in a row. This could be in any other form.

[0077] As is shown in Figure 3, the evaporator boats 14 may be arranged in a staggered arrangement relative to one another. Other arrangements are possible, e.g. arranging the evaporator boats in line.

[0078] The fixture 16 is adapted for supplying electric energy from an energy source 18 (schematically depicted in Figures 1 and 2) to the evaporator boats 14. The amount of energy supplied to the evaporator boats 14 can be controlled separately for each evaporator boat 14.

[0079] For example, depending on the width of the substrate 12, up to 60 evaporator boats 14 can be arranged adjacent to each other in the fixture 16. The deposition material is supplied to each of the evaporator boats 14 in the form of a supply wire 20 which is stored on a supply reel 22. For each of the evaporator boats 14, a supply wire drive 24 is provided which controls the speed with which the supply wire 20 is advanced towards the respective evaporator boat 14.

[0080] The supply wire drive 24 may be implemented in the form of a stepper motor.

[0081] A control and monitoring system 26 is provided for controlling and monitoring various functions of the PVD machine.

[0082] In general, the control and monitoring system 26 is configured to control the amount of energy provided to each of the evaporator boats 14, and the respective speed of the supply wire drives 24.

[0083] A surface inspection system 28 may be provided which inspects the surface of substrate 12 downstream of process drum 10, wherein the surface inspection system 28 may be connected to the control and monitoring system 26.

[0084] Surface defects of the substrate provided with the deposition material as well as other quality issues can be detected by the surface inspection system 28.

[0085] A process chamber 30 is formed which allows establishing a vacuum in the area in which the deposition material is deposited on the substrate 12.

[0086] At least one camera 32 is provided for capturing an image of at least one of the evaporator boats 14. The term „camera“ here designates each and every device which is able convert optical information within the viewing area of the device into electronic information.

[0087] It is possible to use one camera 32 for each of the evaporator boats 14. In order to reduce the number of necessary cameras 32, it however is preferred to use cameras 32 which each cover a plurality of evaporator boats 14. As an example, each of the cameras 32 can capture the images of three evaporator boats 14.

[0088] The cameras 32 are arranged outside of the process chamber 30 or protected inside of the process chamber. A viewing window 34 is provided in a wall of the process chamber 30 so as to allow the cameras to capture the images of the evaporator boats 14. The images captured by the cameras 32 (infrared and visible light emission) are supplied to the control and monitoring system 26 which analyzes the captured images as described in more detail hereinafter.

[0089] In order to facilitate image analyzation, light filters (not shown) can be provided in front of the cameras 32 to increase the contrast between the surface of the pool of molten material and the surface of the evaporator boat 14. In fact, the filter facilitates detection of the pool of molten material on the evaporator boats 14.

[0090] Figure 4 schematically shows the control and monitoring system 26 in more detail.

[0091] It is noted that while the controlling functionalities and the monitoring functionalities are hereinafter described to be integrated into a common control and monitoring system 26, the controlling functionalities and monitoring functionalities may also be separated into different systems.

[0092] Besides the at least one camera 32, the control and monitoring system 26 comprises a machine-learning module 36 that is connected to the at least one camera 32.

[0093] The machine-learning module 36 comprises an image processing module 38 that is connected to the at least one camera 32 so as to receive the images captured by the at least one camera 32.

[0094] It is noted that the image processing module 38 may be established separately from the machine-learning module 36.

[0095] Without restriction of generality, the exemplary case of the image processing module 38 being established as a sub-module of the machine-learning module 36 is described in the following.

[0096] The machine-learning module 36 further comprises a plurality of machinelearning sub-modules. In the exemplary embodiment shown in Figure 4, the machine-learning module 36 comprises a first machine-learning sub-module 40, a second machine-learning sub-module 42, and a third machine-learning submodule 44. Downstream of the machine-learning sub-modules 40, 42, 44, a visualization module 46 is provided.

[0097] The image processing module 38, the machine-learning sub-modules 40, 42, 44, and / or the visualization module 46 may comprise or be established as an artificial neural network, respectively.

[0098] It is noted that while the visualization module 46 is shown to be integrated into the machine-learning module 36 in Figure 4, it is also conceivable that the visualization module 46 may be provided separately from the machine-learning module 36 partially or completely.

[0099] The control and monitoring system 26 further comprises a control module 48 that is connected to outputs of the machine-learning sub-modules 40, 42, 44.

[0100] It is noted that while the control module 48 is illustrated to be established separately from the machine-learning module 36 in Figure 4, it is also conceivable that the control module 48 may be integrated into the machine-learning module 36.

[0101] For example, the control module 48 may comprise or be established as an artificial neural network.

[0102] In the exemplary embodiment shown in Figure 4, the control and monitoring system 26 further comprises at least one master monitoring device 50 and at least one subsidiary monitoring device 52 that are connected to the visualization module 46.

[0103] However, the control and monitoring system 26 may also only comprise at least one master monitoring device 50 or only at least one subsidiary monitoring device 52.

[0104] For example, the at least one master monitoring device 50 may be a PC, a laptop, a smartphone, a tablet, or another type of smart device.

[0105] The at least one master monitoring device 50 may be mounted to the PVD machine.

[0106] As another example, the at least one master monitoring device 50 may be mounted near the PVD machine or in a control room. Likewise, the at least one subsidiary monitoring device 52 may be a PC, a laptop, a smartphone, a tablet, or another type of smart device.

[0107] The at least one subsidiary monitoring device 52 may be mounted to the PVD machine, particularly adjacent to the at least one camera 32 and / or adjacent to controls of the PVD machine.

[0108] The control and monitoring system may further comprise a memory 54 that is connected to the visualization module 46.

[0109] The control and monitoring system 26 is configured to perform a method of monitoring the PVD machine that is described hereinafter with reference to Figure 5.

[0110] An image of at least one of the evaporator boats 14, particularly of all evaporator boats 14, is captured by the at least one camera 32 (step S1).

[0111] In the exemplary embodiment illustrated in Figure 6, the captured image comprises three evaporator boats 14. However, it is to be understood that the captured image may comprise any other number of evaporator boats 14.

[0112] The captured image is forwarded to the image processing module 38.

[0113] At least one image portion corresponding to the at least one evaporator boat 14 is extracted by means of the image processing module 38, and the at least one image portion is corrected for perspective by means of the image processing module 38, thereby obtaining at least one corrected image portion (step S2).

[0114] As is illustrated in Figure 7, the individual image portions are transformed to a standard format, i.e. to a standard length and width, wherein perspective distortions caused by different angles between the at least one camera 32 and the evaporator boats 14 are accounted for.

[0115] The at least one corrected image portion is forwarded to the machine-learning sub-modules 40, 42, 44, respectively.

[0116] A pool of molten material, a supply wire, a contact point between the supply wire and the at least one evaporator boat 14, and / or obscured areas are detected by the machine-learning sub-modules 40, 42, 44 based on the at least one corrected image portion (step S3). This is illustrated in Figure 8, which shows the at least one corrected image portion (“Source”), the detected pool of molten material (“Pool”), the detected contact point (“Wire”), and the detected obscured areas (“Obscured Area Mask”).

[0117] For example, the pool of molten material may be detected by the first machinelearning sub-module 40. The supply wire and / or the contact point may be detected by the second machine-learning sub-module 42. The obscured areas may be detected by the third machine-learning sub-module 44.

[0118] Based on the detected pool, supply wire, contact point, and / or obscured areas, visualization data is generated by the visualization module 46 (step S4).

[0119] In fact, at least one augmented image portion and / or at least one artificial image portion may be generated by the visualization module 46 based on the detected pool, wire, contact point, and / or obscured areas.

[0120] Therein, the at least one augmented image portion corresponds to the at least one corrected image portion, but augmented with additional information regarding the detected pool, wire, contact point, and / or obscured areas.

[0121] For example, the augmented image portion comprises colors, markers, lines, and / or text labels marking the detected pool, wire, contact point, and / or obscured areas.

[0122] It is also conceivable that the at least one augmented image portion may comprise additional information on operational parameters of the individual evaporator boats 14, such as a temperature of the evaporator boats 14 and / or a temperature of the pool of molten material.

[0123] In a particular example, the contact point may be marked with a cross or a circle, as is illustrated in the left portion of Figure 8.

[0124] The pool of molten material, particularly portions of the pool of molten material having different temperatures, may be marked with different colors, as is illustrated in the portion of Figure 8 labeled “Pool”.

[0125] Likewise, the detected wire and / or the obscured areas may be marked with different colors. The explanations given above likewise apply to the at least one artificial image portion, wherein the at least one artificial image portion corresponds to an artificially generated image of the at least one corrected image portion, augmented with additional information regarding the detected pool, wire, contact point, and / or obscured areas.

[0126] Further, process data relating to the PVD machine may be generated by the control and monitoring system 26 and / or by corresponding sensors of the PVD machine, and the visualization data may be generated based on the process data, such that the visualization data comprises information on the process data.

[0127] For example, the process data may comprise information on a pool size, the wire, the contact point, the obscured areas, the at least one camera 32, and / or energy supplied to the at least one evaporator boat 14.

[0128] The visualization data described above may be generated continuously, periodically, and / or on demand.

[0129] Optionally, the visualization data may be saved in the memory 54.

[0130] The visualization data is streamed to and displayed on the at least one master monitoring device 50 and / or streamed to and displayed on the at least one subsidiary monitoring device 52 (step S5).

[0131] In fact, the visualization data may be streamed from the visualization module 46 to the at least one master monitoring device 50 and / or to the at least one subsidiary monitoring device 52 directly, such that an operator can monitor the corresponding evaporator boat(s) 14 in real time.

[0132] Alternatively or additionally, the visualization data may be requested from the memory 54, such that the operator may review or rather scroll through previously saved visualization data.

[0133] Therein, the at least one master monitoring device 50 may be configured to display visualization data associated with different cameras 32 simultaneously and / or selectively. In other words, the operator may select a camera or a set of cameras, and the corresponding visualization data may be displayed on the at least one master monitoring device 50.

[0134] The at least one subsidiary monitoring device 52 may be configured to display visualization data associated with a predefined camera or a predefined set of cameras.

[0135] Further, it is also conceivable that the visualization data may be streamed to an external monitoring device, i.e. to a monitoring device that is located in another room as the PVD machine or even off-site.

[0136] For example, the visualization data may be streamed to the external monitoring device via the internet or via a wide area network.

[0137] The at least one master monitoring device 50 may further comprise a user interface, wherein the user interface is configured to receive a user input. The control and monitoring system 26 may set operational parameters of the PVD machine based on the user input received.

[0138] More precisely, the control module 48 may generate a corresponding control signal based on the user input received, wherein the control signal may be forwarded to the energy source 18 and / or for the supply wire drives 24.

[0139] In general, the energy supplied to the individual evaporator boats 14 and thus the temperature of the individual evaporator boats 14 is controlled by means of the control signal. Alternatively or additionally, the speed with which the corresponding supply wire is advanced towards the individual evaporator boats 14 is controlled by means of the control signal.

[0140] Accordingly, the operator may manually adapt operational parameters of the PVD machine, such as a wire feed rate or an energy supplied to the individual evaporator boats, via the master monitoring device 50.

[0141] However, it is also possible that the control module 48 may automatically generate the control signal based on the detected pool, supply wire, contact point, and / or obscured areas. In fact, the control module 48 may automatically control the energy source 18 and / or the supply wire drives 24 such that an optimal pool shape, an optimal pool size, and / or an optimal contact point is maintained.

[0142] As far as the steps described above are performed by the machine-learning module 36 or its sub-modules, it is understood that the machine-learning module 36 or rather its sub-modules are pre-trained to perform the described functionality.

[0143] Certain embodiments disclosed herein, particularly the respective module(s) and / or unit(s), utilize circuitry (e.g., one or more circuits) in order to implement standards, protocols, methodologies or technologies disclosed herein, operably couple two or more components, generate information, process information, analyze information, generate signals, encode / decode signals, convert signals, transmit and / or receive signals, control other devices, etc. Circuitry of any type can be used.

[0144] In an embodiment, circuitry includes, among other things, one or more computing devices such as a processor (e.g., a microprocessor), a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a system on a chip (SoC), or the like, or any combinations thereof, and can include discrete digital or analog circuit elements or electronics, or combinations thereof. In an embodiment, circuitry includes hardware circuit implementations (e.g., implementations in analog circuitry, implementations in digital circuitry, and the like, and combinations thereof).

[0145] In an embodiment, circuitry includes combinations of circuits and computer program products having software or firmware instructions stored on one or more computer readable memories that work together to cause a device to perform one or more protocols, methodologies or technologies described herein. In an embodiment, circuitry includes circuits, such as, for example, microprocessors or portions of microprocessor, that require software, firmware, and the like for operation. In an embodiment, circuitry includes one or more processors or portions thereof and accompanying software, firmware, hardware, and the like.

[0146] The present application may reference quantities and numbers. Unless specifically stated, such quantities and numbers are not to be considered restrictive, but exemplary of the possible quantities or numbers associated with the present application. Also in this regard, the present application may use the term "plurality" to reference a quantity or number. In this regard, the term "plurality" is meant to be any number that is more than one, for example, two, three, four, five, etc. The terms "about", "approximately”, "near" etc., mean plus or minus 5% of the stated value.

Claims

Claims1. A method of monitoring a physical vapor deposition (PVD) machine, wherein the PVD machine comprises a plurality of evaporator boats (14) arranged in a fixture (16), wherein the PVD machine further comprises a plurality of supply wire drives (24) being configured to advance supply wires (20) to the evaporator boats (14), wherein the method comprises the steps of capturing, by means of at least one camera (32), an image of at least one evaporator boat (14) of the plurality of evaporator boats (14); extracting, by means of an image processing module (38), at least one image portion of the image corresponding to the at least one evaporator boat (14); processing, by the image processing module (38) and / or by a machinelearning module (36), the at least one extracted image portion, thereby obtaining at least one processed image portion, wherein processing the at least one extracted image portion comprises detecting a pool of molten material, detecting a supply wire (20), detecting a contact point between the supply wire (20) and the at least one evaporator boat (14), detecting obscured areas, and / or correcting the at least one extracted image portion for perspective; and generating, by a visualization module (46), visualization data based on the at least one processed image portion.

2. The method of claim 1 , wherein the at least one extracted image portion is corrected for perspective by means of the image processing module (38), thereby obtaining at least one corrected image portion, and wherein the pool, the supply wire (20), the contact point, and / or the obscured areas are / is detected in the at least one corrected image portion.

3. The method of claim 1 or 2, wherein the at least one extracted image portion is transformed to a standard format.

4. The method according to any one of the preceding claims, wherein the at least one image comprises at least two evaporator boats (14).

5. The method according to any one of the preceding claims, wherein the at least one image portion is augmented with additional information regarding the detected pool, wire (20), contact point, and / or obscured areas by means of the machine-learning module (36), thereby obtaining at least one augmented image portion, and wherein the visualization data is generated based on the at least one augmented image portion.

6. The method of claim 5, wherein the augmented image portion comprises colors, markers, lines, and / or text labels marking the detected pool, wire (20), contact point, and / or obscured areas.

7. The method according to any one of the preceding claims, wherein at least one artificial image portion is generated by means of the machine-learning module (36) based on the at least one image portion and based on the detected pool, wire (20), contact point, and / or obscured areas, wherein the detected pool, wire (20), contact point, and / or obscured areas are represented in the at least one artificial image portion.

8. The method according to any one of the preceding claims, wherein process data relating to the PVD machine is generated, and wherein the visualization data is generated based on the process data, particularly wherein the process data comprises information on a pool size, the wire (20), the contact point, the obscured areas, the at least one camera (32), and / or energy supplied to the at least one evaporator boat (14).

9. The method according to any one of the preceding claims, wherein the visualization data is generated continuously, periodically, and / or on demand, particularly wherein the visualization data is saved in a memory (54).

10. The method according to any one of the preceding claims, wherein at least the visualization data is streamed to an external monitoring device.

11. A monitoring system for a PVD machine, wherein the monitoring system comprises at least one camera (32), an image processing module (38), a machinelearning module (36), and a visualization module (46), wherein the monitoring system is configured to perform the method according to any one of the preceding claims.

12. The monitoring system of claim 11 , wherein the monitoring system comprises at least one master monitoring device (50) configured to display the visualization data and / or at least one subsidiary monitoring device (52) configured to display the visualization data.

13. The monitoring system of claim 12, wherein the at least one master monitoring device (50) comprises a user interface, wherein the user interface is configured to receive a user input, and wherein the monitoring system is configured to set operational parameters of the PVD machine based on the user input received.

14. The monitoring system of claim 12 or 13, wherein the at least one subsidiary monitoring device (52) is mounted to the PVD machine, particularly adjacent to the at least one camera (32) and / or adjacent to controls of the PVD machine.

15. A PVD machine system, comprising a PVD machine, and a monitoring system according to any one of claims 11 to 14.

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