Laser irradiation device, information processing method, and program

Abstract

To provide a laser irradiation device and the like capable of efficiently predicting failures of components included in a laser irradiation device.SOLUTION: A laser annealing apparatus 1 is a laser irradiation apparatus equipped with a laser light source 2, and a control device 9 includes a control unit that functions as a failure prediction unit that predicts failure of movable parts such as a first vacuum pump 101 and a second vacuum pump 102 used in processing a substrate 8 with the laser light source 2, and the failure prediction unit acquires physical quantities when the movable parts are operated, and derives the time of failure of the movable parts on the basis of the acquired physical quantities. The movable parts also include at least any one of a diaphragm pump 105, a helium compressor 104, and a cold head 103.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a laser irradiation device, an information processing method, and a program. [Background technology]

[0002] A laser annealing device for forming a polycrystalline silicon thin film is known (for example, Patent Document 1). The laser annealing device described in Patent Document 1 includes a waveform shaping device that shapes the waveform of a laser light pulse, and the laser light shaped into a line by the waveform shaping device is irradiated onto an amorphous silicon film to form a polycrystalline silicon thin film. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2012-15545 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the laser annealing apparatus of Patent Document 1 does not take into consideration failure prediction of various components included in the laser annealing apparatus.

[0005] The present invention has been made in consideration of the above circumstances, and has an object to provide a laser irradiation device etc. that can efficiently perform failure prediction of components included in the laser irradiation device. [Means for solving the problem]

[0006] The laser irradiation device according to the present embodiment is a laser irradiation device equipped with a laser light source, and further equipped with a failure prediction unit that predicts failure of a movable part used in processing a substrate with the laser light source, the failure prediction unit acquiring a physical quantity when the movable part is moved, and deriving a failure time of the movable part based on the acquired physical quantity, the movable part including at least one of a diaphragm pump, a helium compressor, and a cold head.

[0007] The information processing method according to the present aspect includes causing a computer that performs failure prediction for a moving part used in processing a substrate with a laser light source provided in a laser irradiation device to (A) acquire physical quantities when the moving part is moved, and (B) execute a process to derive a time when the moving part will fail based on the acquired physical quantities, the moving part including at least one of a diaphragm pump, a helium compressor, and a cold head.

[0008] The program according to this embodiment causes a computer that performs failure prediction for a moving part used in processing a substrate with a laser light source provided in a laser irradiation device to (A) acquire physical quantities when the moving part is moved, and (B) execute a process of deriving the time of failure of the moving part based on the acquired physical quantities, the moving part including at least one of a diaphragm pump, a helium compressor, and a cold head. Effect of the Invention

[0009] According to the present invention, it is possible to provide a laser irradiation device or the like that efficiently performs failure prediction of components included in the laser irradiation device. [Brief description of the drawings]

[0010] [Figure 1] 1 is a diagram showing an example of the configuration of a laser annealing apparatus according to a first embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of a control device included in the laser annealing apparatus. [Diagram 3] FIG. 1 is an explanatory diagram showing an example of a learning model for a vacuum pump. [Figure 4] 10 is a flowchart illustrating an example of a processing procedure performed by a control unit. [Diagram 5] FIG. 13 is a diagram showing a configuration example of a laser annealing apparatus according to a second embodiment (various movable parts). [Figure 6] 10 is a flowchart illustrating an example of a processing procedure performed by a control unit. [Figure 7] 5 is a flowchart showing an example of a processing procedure (cold head) performed by a control unit. [Figure 8] 6 is a flowchart showing an example of a processing procedure (He compressor) by a control unit. [Figure 9] 13 is a flowchart showing an example of a processing procedure (diaphragm pump) performed by a control unit. [Figure 10] FIG. 13 is an explanatory diagram showing an example of a feature amount learning model according to the third embodiment (feature amount learning model). [Figure 11] FIG. 13 is a diagram illustrating an example of a management screen for movable parts. [Figure 12] 10A to 10C are cross-sectional views showing steps of a method for manufacturing a semiconductor device according to another embodiment (a method for manufacturing a semiconductor device). [Figure 13] 10A to 10C are cross-sectional views showing steps of a method for manufacturing a semiconductor device according to another embodiment (a method for manufacturing a semiconductor device). [Figure 14] 10A to 10C are cross-sectional views showing steps of a method for manufacturing a semiconductor device according to another embodiment (a method for manufacturing a semiconductor device). [Figure 15] 10A to 10C are cross-sectional views showing steps of a method for manufacturing a semiconductor device according to another embodiment (a method for manufacturing a semiconductor device). [Figure 16] 10A to 10C are cross-sectional views showing steps of a method for manufacturing a semiconductor device according to another embodiment (a method for manufacturing a semiconductor device).

[0011] (Embodiment 1) Hereinafter, an embodiment of the present invention will be described. Fig. 1 is a diagram showing a configuration example of a laser annealing apparatus 1 according to the first embodiment. Fig. 2 is a diagram showing a configuration example of a control device 9 included in the laser annealing apparatus 1. The laser annealing apparatus 1 (laser irradiation apparatus) is, for example, an excimer laser annealing apparatus for forming a low temperature polysilicon (LTPS: Low Temperature Poly-Silicon) film. (ELA: Excimer laser Anneal) equipment.

[0012] The laser annealing apparatus 1 irradiates a laser beam onto a silicon film formed on a substrate 8. This allows the non-crystalline silicon film (amorphous silicon film: a-Si film) to be converted into a polycrystalline silicon film (polysilicon film: p-Si film). The substrate 8 is, for example, a transparent substrate such as a glass substrate.

[0013] As illustrated in the present embodiment, in an XYZ three-dimensional Cartesian coordinate system, the Z direction is the vertical direction, which is a direction perpendicular to the substrate 8. The XY plane is a plane parallel to the surface of the substrate 8 on which the silicon film is formed. For example, the X direction is the longitudinal direction of the rectangular substrate 8, and the Y direction is the lateral direction of the substrate 8. When a Θ-axis stage 71 that can rotate from 0° to 90° about the Z axis is used, the X direction can be the lateral direction of the substrate 8, and the Y direction can be the longitudinal direction of the substrate 8.

[0014] The laser annealing apparatus 1 includes an annealing optical system 11, a laser irradiation chamber 7, and a control device 9. The laser irradiation chamber 7 houses a base 72 and a stage 71 arranged on the base 72. In the laser annealing apparatus 1, a silicon film 201 is irradiated with laser light while a substrate 8 is transported in the +X direction by the stage 71.

[0015] The annealing optical system 11 is an optical system for generating laser light for crystallizing the amorphous silicon film formed on the substrate 8 and converting it into a polysilicon film, and irradiating the amorphous silicon film with the laser light. The annealing optical system 11 is an optical system that includes a laser light source 2 including a first laser light source 21 and a second laser light source 22, a first attenuator 31, a second attenuator 32, a combining optical system 5, a beam shaping optical system 6, an epi-illumination mirror 61, and a projection lens 65, and emits a laser light obtained by combining a plurality of laser lights.

[0016] The laser light source 2 includes a first laser light source 21 and a second laser light source 22, and is composed of a plurality of laser light sources 2. In the present embodiment, the laser light source 21 and the second laser light source 22 are configured as two laser light sources, but this is an example and is not limited to the two-unit configuration. The laser light source 2 may be composed of a single laser light source 2, or three or more laser light sources 2.

[0017] The first laser light source 21 and the second laser light source 22 are laser generating devices that generate pulsed laser light as laser light for irradiating an amorphous silicon film (object to be processed). The generated laser light is laser light for crystallizing a non-crystalline film on the substrate 8 to form a crystallized film, and is, for example, a gas laser light such as an excimer laser light having a central wavelength of 308 nm.

[0018] In the first laser light source 21 and the second laser light source 22, a gas such as xenon is sealed in a chamber, and two resonator mirrors are arranged to face each other with the gas in between. One resonator mirror is a total reflection mirror that reflects all light, and the other resonator mirror is a partial reflection mirror that transmits a part of the light. Gas light excited by the gas is repeatedly reflected between the resonator mirrors, and the amplified light is emitted from the resonator mirror as a laser light. For example, the first laser light source 21 and the second laser light source 22 repeatedly emit pulsed laser light at a period of 500 Hz to 600 Hz. The first laser light source 21 emits a laser light toward the first attenuator 31. The second laser light source 22 emits a laser light toward the second attenuator 32.

[0019] The first attenuator 31 and the second attenuator 32 attenuate the incident laser light and adjust it to a predetermined energy density. These attenuators have a transmittance, which indicates the ratio of the emitted laser light to the incident laser light, as a characteristic, and the transmittance is configured to be variable based on a signal from the control device 9.

[0020] The first attenuator 31 is provided in the middle of the optical path from the first laser light source 21 to the combining optical system 5. The second attenuator 32 is provided in the middle of the optical path from the second laser light source 22 to the combining optical system 5. The first attenuator 31 attenuates the laser light emitted by the first laser light source 21 in accordance with the transmittance. The second attenuator 32 attenuates the laser light emitted by the second laser light source 22 in accordance with the transmittance.

[0021] A first polarization ratio control unit 41 and a second polarization ratio control unit 42 are disposed on the output sides of the first attenuator 31 and the second attenuator 32, respectively. The first polarization ratio control unit 41 and the second polarization ratio control unit 42 are configured with, for example, a half-wave plate (λ / 2 plate) and a polarizing beam splitter, and change the polarization ratio between the P polarization and the S polarization of the incident laser light.

[0022] The first polarization ratio control unit 41 changes the polarization ratio of the laser light output from the first attenuator 31. The second polarization ratio control unit 42 changes the polarization ratio of the laser light output from the second attenuator 32. The first polarization ratio control unit 41 and the second polarization ratio control unit 42 are configured to change (variably) their respective polarization ratios based on a control signal output from the control device 9.

[0023] The combining optical system 5 has, for example, a beam splitter, and combines the laser emitted from the first laser light source 21 and the laser light emitted from the second laser light source 22. Since the first attenuator 31 and the second attenuator 32 are arranged on the optical path between the combining optical system 5 and the first laser light source 21 and the second laser light source 22, the laser emitted from the first attenuator 31 and the laser light emitted from the second attenuator 32 are incident on the combining optical system 5, and these laser lights are combined by the combining optical system 5.

[0024] In a plan view perpendicular to the optical axis, the laser light from the first laser light source 21 and the laser light from the second laser light source 22 overlap. The laser light from the first laser light source 21 and the laser light from the second laser light source 22 are spatially overlapped and coaxial by the combining optical system 5 to become a combined laser light. The laser light combined by the combining optical system 5 is incident on the beam shaping optical system 6.

[0025] The laser light combined by the combining optical system 5 is incident on the beam shaping optical system 6, which shapes the incident laser light (combined laser light) to generate laser light having a beam shape suitable for irradiating a silicon film. The beam shaping optical system 6 generates a line beam that is linear along the Y direction.

[0026] The beam shaping optical system 6 splits one beam into multiple beams (multiple line beams aligned in the Z direction) using, for example, a homogenizer made up of a lens array. After splitting into multiple beams, the multiple beams can be combined using a condenser lens to form a line beam. The beam shaping optical system 6 emits the generated (shaped) linear laser light to the epi-illumination mirror 61.

[0027] The epi-mirror 61 is a rectangular reflection mirror extending in the Y direction, and reflects the laser light, which is a plurality of line beams generated by the beam shaping optical system 6. The epi-mirror 61 is, for example, a dichroic mirror, and is a partial reflection mirror that transmits a portion of the light. The epi-mirror 61 reflects the line-shaped laser light to generate reflected light, and transmits a portion of the line-shaped laser light to generate transmitted light. The epi-mirror 61 irradiates the reflected laser light onto the silicon film of the substrate 8, and emits the transmitted laser light to a pulse measuring device, such as a biplanar phototube.

[0028] The projection lens 65 is disposed above the substrate. The projection lens 65 has a plurality of lenses for projecting the laser light onto the substrate, i.e., onto the silicon film. The projection lens 65 focuses the laser light onto the substrate. On the substrate 8, the laser light forms a linear irradiation area along the Y direction. That is, on the substrate 8, the laser light is a line beam with the Y direction as the longitudinal direction. In addition, the silicon film is irradiated with the laser light while the substrate 8 is being transported in the +X direction. This allows the laser light to be irradiated onto a belt-shaped area whose width is the length of the irradiation area in the Y direction.

[0029] The line beam-like laser light irradiated to the epi-illumination mirror 61 has a beam shape with a widened short axis width, that is, the short axis width is somewhat widened and the shape is distorted after being emitted from the condenser lens. The laser light reflected by the epi-illumination mirror 61 passes through the projection lens 65 and is shaped into a line beam-like laser light with a short axis width of about 1 / 5.

[0030] The control device 9 is an information processing device such as a personal computer or a server device that performs overall or integrated control or management of the laser annealing apparatus 1. The control device 9 includes a control unit 91, a storage unit 92, a communication unit 93, and an input / output I / F 94, and is communicatively connected to a control device (another control device) that controls the laser light source 2 or each optical system in the annealing optical system 11 via the communication unit 93 or the input / output I / F 94. The control device 9 is communicatively connected to various measuring devices such as a pulse measuring instrument and a photodetector included in the laser annealing apparatus 1, and performs various controls on the laser light source 2 or the annealing optical system 11 based on measurement data output from these various measuring devices.

[0031] The control unit 91 has one or more central processing units (CPUs), micro-processing units (MPUs), graphics processing units (GPUs), or other calculation processing devices equipped with timing functions, and performs various information processing and control processing for each optical system included in the laser light source 2 or the annealing optical system 11 by reading and executing a program P (program product) stored in the memory unit 92.

[0032] The storage unit 92 includes a volatile storage area such as a static random access memory (SRAM), a dynamic random access memory (DRAM), or a flash memory, and a non-volatile storage area such as an EEPROM or a hard disk. The storage unit 92 stores in advance a program P (program product) and data to be referenced during processing. The program P stored in the storage unit 92 may be a program P (program product) read from a recording medium 920 readable by the control unit 91. The program P may also be a program P (program product) downloaded from an external computer (not shown) connected to a communication network (not shown) and stored in the storage unit 92. The storage unit 92 stores actual files of learning models, such as a learning model 921 for a vacuum pump, which will be described later. The actual files of the learning models may be configured as modules included in the program P (program product).

[0033] The communication unit 93 is, for example, a communication module or communication interface conforming to the Ethernet (registered trademark) standard, and an Ethernet cable is connected to the communication unit 93. The communication unit 93 is not limited to being wired, such as an Ethernet cable, and may be, for example, a communication interface compatible with wireless communication, such as a short-range wireless communication module, such as Wi-Fi (registered trademark) or Bluetooth (registered trademark), or a wide-area wireless communication module, such as 4G or 5G.

[0034] The input / output I / F 94 is a communication interface that complies with a communication standard such as RS232C or USB, etc. To the input / output I / F 94, an input device such as a keyboard, or a display device 941 such as a liquid crystal display is connected.

[0035] The respective chambers of the first laser light source 21 and the second laser light source 22 are in communication (connected) with vacuum pumps (first vacuum pump 101, second vacuum pump 102) via, for example, gas piping. That is, the first vacuum pump 101 is connected to the chamber of the first laser light source 21, and the second vacuum pump 102 is connected to the chamber of the second laser light source 22.

[0036] The gas such as xenon sealed in the chamber of the first laser light source 21 and the second laser light source 22 deteriorates due to excitation when emitting laser light, and needs to be replaced periodically according to the gas life. In order to perform the gas exchange, the first vacuum pump 101 and the second vacuum pump 102 suck (draw a vacuum) the gas in the chambers of the first laser light source 21 and the second laser light source 22, which are connected to the first vacuum pump 101 and the second vacuum pump 102, respectively, to create a vacuum state in these chambers below a predetermined pressure.

[0037] A pressure sensor 113 is provided in each of the chambers of the first laser light source 21 and the second laser light source 22. These pressure sensors 113 are communicatively connected to the control device 9 via a communication line or the like, detect pressure values ​​in the chambers, and output them to the control device 9. Although details will be described later, the control device 9 can obtain the time when a vacuum state is reached in these chambers based on the pressure values ​​output by the pressure sensors 113 provided in the chambers of the first laser light source 21 and the second laser light source 22. The control device 9 obtains the elapsed time from the time when suction (vacuum drawing) of gas in these chambers is started to the time when a vacuum state is reached as a vacuum attainment time.

[0038] The first vacuum pump 101 and the second vacuum pump 102 are provided with a temperature sensor 111 and a vibration sensor 112. These temperature sensor 111 and vibration sensor 112 correspond to detection units that detect physical quantities when movable parts such as the first vacuum pump 101 are moved. The temperature sensor 111 is configured with, for example, a thermistor, and is provided on the outer shells of the first vacuum pump 101 and the second vacuum pump 102, respectively, and detects the surface temperatures of the outer shells of the first vacuum pump 101 and the second vacuum pump 102. These temperature sensors 111, like the pressure sensor 113, are connected to the control device 9 via a communication line or the like so as to be able to communicate with each other, and output the detected temperatures to the control device 9.

[0039] The vibration sensors 112 are formed of, for example, piezoelectric elements, and are provided on the outer shells of the first vacuum pump 101 and the second vacuum pump 102, respectively, to detect the vibration frequencies of the first vacuum pump 101 and the second vacuum pump 102. Similar to the pressure sensor 113, these vibration sensors 112 are communicatively connected to the control device 9 via a communication line or the like, and output the detected vibration frequencies to the control device 9.

[0040] The temperature sensor 111 and the vibration sensor 112 provided on each of the first vacuum pump 101 and the second vacuum pump 102 detect the temperature and the vibration frequency at a predetermined sampling period during the period when evacuation is being performed, i.e., from the time when suction of gas (vacuum pumping) in the chamber starts to the time when a vacuum state is reached, and output the detected temperatures and vibration frequencies to the control device 9. This allows the control device 9 to acquire the temperatures and vibration frequencies of the first vacuum pump 101 and the second vacuum pump 102, respectively, as time-series data during the period when evacuation is being performed, i.e., during the time when the vacuum is reached.

[0041] The control device 9 may associate the acquired time to vacuum attainment, time series data of the temperature and vibration frequency at the time to vacuum attainment, and time information such as the date and time when the evacuation was performed, and store the associated data as operation history data in table format, for example, in the storage unit 92. The control device 9 may calculate the integrated operation time of each of the first vacuum pump 101 and the second vacuum pump 102 by adding up the time to vacuum attainment of each of the multiple evacuation operations.

[0042] 3 is an explanatory diagram showing an example of the vacuum pump learning model 921. The control unit 91 of the control device 9 trains a neural network using training data, and generates the vacuum pump learning model 921 that outputs the time until the vacuum pump breaks down when physical quantities such as vibration and temperature when the vacuum pump is operated, the time to reach vacuum, and the accumulated operating time of the vacuum pump up to the present time are input.

[0043] In this embodiment, as described above, the vacuum pump is configured of two pumps, the first vacuum pump 101 and the second vacuum pump 102, but the following description will be given by way of example in which the first vacuum pump 101 is applied. It goes without saying that the vacuum pump learning model 921 is a model that can be applied to both the first vacuum pump 101 and the second vacuum pump 102.

[0044] The training data is composed of question data including physical quantities such as vibration and temperature of the first vacuum pump 101, time to reach vacuum, and accumulated operating time, and answer data including time until failure of the first vacuum pump 101, and is stored in the memory unit 92 of the control device 9. The original data of the training data can be generated, for example, by aggregating performance data of gas exchanges performed in a plurality of laser annealing apparatuses 1.

[0045] The neural network (vacuum pump learning model 921) trained using the training data is expected to be used as a program module that is part of artificial intelligence software. The vacuum pump learning model 921 is used in the control device 9, and is executed in the control device 9 having such a computing capability to configure a neural network system.

[0046] The learning model 921 for the vacuum pump is composed of a DNN (Deep Neural Network) and has an input layer that accepts inputs of physical quantities such as the vibration and temperature of the first vacuum pump 101, the time to reach vacuum, and the accumulated operating time, an intermediate layer that extracts features of the physical quantities, etc., and an output layer that outputs the time until the first vacuum pump 101 fails.

[0047] The input layer has a number of neurons that accept inputs of physical quantities such as vibration (frequency data) and temperature (temperature data), time to reach vacuum, and accumulated operating time, and passes the input values ​​to the middle layer. The middle layer is defined using an activation function such as a ReLu function or a sigmoid function, has a number of neurons that extract features of each input value, and passes the extracted features to the output layer. Parameters such as the weighting coefficient and bias value of the activation function are optimized using the backpropagation method. The output layer is composed of, for example, a fully connected layer, and outputs the time to failure based on the features output from the middle layer.

[0048] The vibration frequency data and temperature data input to the learning model 921 for the vacuum pump may be data in an array or matrix format including a plurality of temperatures and vibration frequencies acquired in time series from the temperature sensor 111 and the vibration sensor 112 during the period from when the first vacuum pump 101 starts operating until the vacuum state is reached (vacuuming period). Alternatively, the vibration frequency data input to the learning model 921 for the vacuum pump may be the average value (average vibration frequency) or the maximum value (maximum vibration frequency) of the plurality of vibration frequencies acquired during the vacuuming period. Similarly, the temperature data input to the learning model 921 for the vacuum pump may be the average value (average temperature) or the maximum value (maximum temperature) of the plurality of temperatures acquired during the vacuuming period.

[0049] The training data may include not only data when the first vacuum pump 101 is normal, but also data when the first vacuum pump 101 is broken. The problem data included as the data when the first vacuum pump 101 is broken includes at least one of physical quantities such as vibration and temperature, time to reach vacuum, and integrated operating time when the first vacuum pump 101 is broken, and the time until failure, which is the answer data, indicates 0. The training data may include data of the first vacuum pump 101 when the first vacuum pumping is performed immediately after replacement with a new pump, for example. When the physical quantities such as temperature and vibration frequency detected during the first vacuum pumping are set as the problem data, the answer data (time until failure) labeled with the problem data may be, for example, the guaranteed operating time defined in the product specifications of the first vacuum pump 101.

[0050] In this way, by having the training data include data at the time of failure (vibration, temperature, time to reach vacuum, etc.) labeled with the time to failure being 0, and having the vacuum pump learning model 921 learn from this, it is possible to perform a failure judgment for the first vacuum pump 101 using the vacuum pump learning model 921. In other words, if the vacuum pump learning model 921 outputs the time to failure as 0, it is possible to judge that the first vacuum pump 101 is at fault.

[0051] In this embodiment, the data input to the learning model 921 for a vacuum pump are physical quantities such as vibration (frequency data) and temperature (temperature data), the time to reach vacuum, and the accumulated operating time, but are not limited to this. The data input to the learning model 921 for a vacuum pump may be any one of the data of vibration (frequency data), temperature (temperature data), the time to reach vacuum, and the accumulated operating time, or any combination of data.

[0052] In this embodiment, the vacuum pump learning model 921 is a DNN, but is not limited to this and may be a learning model constructed with other learning algorithms, such as a neural network other than a DNN, a transformer, a recurrent neural network (RNN), a long-short term model (LSTM), a CNN, a support vector machine (SVM), a Bayesian network, linear regression, a regression tree, multiple regression, a random forest, an ensemble, etc.

[0053] As an example of using a method other than the DNN, the control unit 91 of the control device 9 may derive a failure prediction line by performing, for example, regression analysis based on the current physical quantity and a plurality of past physical quantities acquired. In this case, the failure prediction line corresponds to the vacuum pump learning model 921 generated by machine learning.

[0054] For example, when deriving a failure prediction line as a straight line (linear approximation), the control unit 91 of the control device 9 may use a least squares method based on the acquired current physical quantity and a plurality of past physical quantities. Alternatively, the control unit 91 may derive a failure prediction line using various methods such as a logarithmic approximation curve, a polynomial approximation curve, a power approximation curve, or an exponential approximation curve. When the failure prediction line is graphed, the horizontal axis may indicate elapsed time, and the vertical axis may indicate the physical quantity or a feature quantity extracted by subjecting the physical quantity to, for example, principal component analysis or dimensional reduction.

[0055] The control unit 91 of the control device 9 sets a failure threshold at which failure of the first vacuum pump 101, which is a movable part, is expected within a range of a physical quantity (feature quantity) defined in the derived failure prediction line. In other words, the control unit 91 of the control device 9 may predict that the first vacuum pump 101 will fail when the acquired physical quantity of the first vacuum pump 101 (movable part) reaches the failure threshold, and calculate the period from the present time until the failure threshold is reached as the time until failure.

[0056] The control unit 91 of the control device 9 functions as a failure prediction unit that predicts (derives) the time until failure of the first vacuum pump 101 based on physical quantities such as vibration and temperature of the first vacuum pump 101 when evacuation is performed, the time to reach vacuum, and the accumulated operating time by using the vacuum pump learning model 921 stored in the memory unit 92. The control unit 91 of the control device 9 functioning as the failure prediction unit derives the expected failure time by adding the derived time until failure and the planned stop period to the current date and time, and outputs (transmits) the derived failure time to, for example, the display device 941 or a mobile terminal of the administrator of the laser annealing apparatus 1 via the communication unit 93.

[0057] Although the control device 9 included in the laser annealing apparatus 1 has been described as generating the learning model 921 for the vacuum pump, this is not limiting, and the learning model 921 for the vacuum pump may be learned and generated by an external server device, such as a cloud server, other than the control device 9. Although the learning model 921 for the vacuum pump is used by the control device 9, this is not limiting, and the control device 9 may communicate with a cloud server, for example, connected to the Internet, via the communication unit 93, and obtain the time until failure output by the learning model 921 for the vacuum pump implemented in the cloud server.

[0058] 4 is a flowchart showing an example of a processing procedure by the control unit 91. The control unit 91 of the control device 9 included in the laser annealing apparatus 1 receives an operation by an operator using, for example, a keyboard connected to an input / output, and performs the following processing based on the received operation.

[0059] The control unit 91 of the control device 9 acquires physical information and the like of the first vacuum pump 101 and the second vacuum pump 102 (S100). When the first vacuum pump 101 and the second vacuum pump 102 are performing evacuation, the control unit 91 of the control device 9 acquires physical quantities such as temperature and vibration frequency output from the temperature sensor 111 and the vibration sensor 112 provided in each of the first vacuum pump 101 and the second vacuum pump 102.

[0060] Based on the acquired temperatures and frequencies, the control unit 91 of the control device 9 generates temperature data and frequency data for each of the first vacuum pump 101 and the second vacuum pump 102. The temperature data and frequency data may be data in an array format based on a plurality of temperatures and frequencies acquired in chronological order, or may be the average value or maximum value of a plurality of frequencies and temperatures acquired during the evacuation period.

[0061] The control unit 91 of the control device 9 calculates the time to vacuum, which is the elapsed time from when the first vacuum pump 101 and the second vacuum pump 102 start suctioning (evacuating) the gas in the chamber to when a vacuum state is reached, based on the pressure output from the pressure sensor 113. The control unit 91 of the control device 9 refers to the memory unit 92, and calculates the cumulative operation time of each by summing up the times to vacuum of the first vacuum pump 101 and the second vacuum pump 102 up to the present time. By performing these processes, the control unit 91 of the control device 9 acquires the temperature data, vibration frequency data, time to vacuum and cumulative operation time of each of the first vacuum pump 101 and the second vacuum pump 102.

[0062] The control unit 91 of the control device 9 derives the time until failure of the first vacuum pump 101 by inputting physical information of the first vacuum pump 101, etc., into a learning model 921 for a vacuum pump (S101). The control unit 91 of the control device 9 inputs the temperature data, vibration frequency data, time to reach vacuum, and accumulated operating time of the first vacuum pump 101 into the learning model 921 for a vacuum pump, and obtains the time until failure output by the learning model 921 for a vacuum pump.

[0063] The control unit 91 of the control device 9 determines whether the time until failure is 0 (S102). As described above, the vacuum pump learning model 921 is trained using training data including physical quantities at the time of failure, and therefore, when the value output from the vacuum pump learning model 921 is 0 (the time until failure is 0), it means that the first vacuum pump 101 is estimated to be in a state of failure. The control unit 91 of the control device 9 determines whether the value output from the vacuum pump learning model 921 is 0 (the time until failure is 0).

[0064] If the time until failure is 0 (S102: YES), the control unit 91 of the control device 9 generates an alarm signal indicating that the first vacuum pump 101 is in a faulty state (S103). If the time until failure is 0, the control unit 91 of the control device 9 determines that the first vacuum pump 101 is in a faulty state, generates an alarm signal indicating that the first vacuum pump 101 is in a faulty state, and outputs the alarm signal to the display device 941 or the like.

[0065] If the time to failure is not 0 (S102: NO), the control unit 91 of the control device 9 calculates the time of failure of the first vacuum pump 101 based on the time to failure (S104). If the time to failure is not 0, the control unit 91 of the control device 9 determines that the first vacuum pump 101 is normal, and derives the expected time of failure by adding the time to failure and the planned outage period output by the vacuum pump learning model 921 to the current date and time.

[0066] The control unit 91 of the control device 9 outputs the calculated failure time (S105). The control unit 91 of the control device 9 outputs (transmits) the failure time to, for example, the display device 941 or a mobile terminal of an administrator of the laser annealing device 1 via the communication unit 93.

[0067] The control unit 91 of the control device 9 inputs the physical information of the second vacuum pump 102 and the like into the vacuum pump learning model 921 to derive the time until failure (S106).

[0068] The control unit 91 of the control device 9 determines whether the time until failure is 0 or not (S107).

[0069] If the time until failure is 0 (S107: YES), the control unit 91 of the control device 9 generates an alarm signal indicating that the second vacuum pump 102 is in a failure state (S108).

[0070] If the time until failure is not 0 (S107: NO), the control unit 91 of the control device 9 calculates the time when the second vacuum pump 102 will fail based on the time until failure (S109).

[0071] The control unit 91 of the control device 9 outputs the calculated failure time (S110). The control unit 91 of the control device 9 performs processes (S106 to S110) related to the second vacuum pump 102, similar to the processes (S101 to S105) related to the first vacuum pump 101, using the temperature data, vibration frequency data, time to reach vacuum, and integrated operation time of the second vacuum pump 102.

[0072] The control unit 91 of the control device 9 may perform processing related to the first vacuum pump 101 (S101 to S105) and processing related to the second vacuum pump 102 (S106 to S110) in parallel (parallel processing), for example, by using multi-threading or multi-processing with multiple sub-processes.

[0073] According to this embodiment, the control unit 91 (failure prediction unit) of the control device 9 included in the laser annealing apparatus 1 (laser irradiation device) acquires physical quantities (measured values) such as vibrations generated when the vacuum pumps (first vacuum pump 101, second vacuum pump 102) are operated and the temperature of the vacuum pumps from a temperature sensor 111 and a vibration sensor 112 provided in the vacuum pumps. The control device 9 derives the time of failure of the vacuum pump based on the physical quantities such as the vibration and temperature of the vacuum pumps, and therefore can efficiently predict failures of the components included in the laser annealing apparatus 1. The control device 9 outputs the time of failure to, for example, a display device 941, thereby urging the manager of the laser annealing apparatus 1 to perform maintenance at an appropriate time, and can suppress (reduce) the downtime (period of operation suspension) of the laser annealing apparatus 1.

[0074] According to this embodiment, the time when the vacuum pump will fail is derived based on information obtained by combining the physical quantities, such as vibration and temperature when the vacuum pump is operating, with the vacuum attainment time from when the vacuum pump starts to suck in gas until the chamber reaches a vacuum state, thereby improving the accuracy of estimating the time when the vacuum pump will fail.

[0075] According to this embodiment, the time until failure can be efficiently derived (acquired) by using a learning model that outputs the time until failure of the vacuum pump when inputting physical quantities such as vibration and temperature when the vacuum pump is operated, the time to reach vacuum, and the accumulated operating time of the vacuum pump up to the present time. The control device 9 (failure prediction unit) can efficiently calculate the time until failure of the vacuum pump by adding the time until the failure and the planned stop period to the date and time when the vacuum pump is operated.

[0076] According to this embodiment, the control device 9 (failure prediction unit) derives the failure time of each of the multiple vacuum pumps (first vacuum pump 101, second vacuum pump 102) corresponding to each of the multiple laser light sources 2 (first laser light source 21, second laser light source 22) based on the physical quantity and vacuum attainment time of each of the vacuum pumps (first vacuum pump 101, second vacuum pump 102). As a result, even if the laser irradiation device is equipped with multiple laser light sources 2 and vacuum pumps, the failure time of these vacuum pumps (first vacuum pump 101, second vacuum pump 102) can be derived individually.

[0077] 5 is a diagram showing a configuration example of the laser annealing apparatus 1 according to the embodiment 2 (various movable parts). The laser annealing apparatus 1 according to the embodiment 2 includes, as movable parts, a cold head 103, a He compressor 104, and a diaphragm pump 105 in addition to a first vacuum pump 101 and a second vacuum pump 102. Note that the cold head 103 and the like may be configured by separate cold heads 103 (first cold head and second cold head) corresponding to the first laser light source 21 and the second laser light source 22, respectively, similar to the first vacuum pump 101 and the second vacuum pump 102.

[0078] The laser annealing apparatus 1 is equipped with a refrigerator that cools a filter for removing impurities from the gas sucked from the chamber by vacuuming, and the refrigerator includes a cold head 103 and a He compressor 104 as movable parts. The cold head 103 includes a cylinder, a displacer that reciprocates within the cylinder to expand a refrigerant gas such as helium and generate cold, and a cold heat transfer member that is provided at the tip of the cylinder and transfers the generated cold to an object to be cooled. The He compressor 104 transfers helium, which is the refrigerant gas, to the This is a compressor that compresses helium (He) and supplies the compressed helium to cold head 103. Diaphragm pump 105 circulates laser gas (gas such as xenon sealed in the chamber) through a refrigerator including cold head 103 etc.

[0079] These movable parts, namely, cold head 103, He compressor 104, and diaphragm pump 105, are provided with a temperature sensor 111 and a vibration sensor 112, similar to the first vacuum pump 101, etc. The temperature sensor 111 and vibration sensor 112 provided on the cold head 103, He compressor 104, and diaphragm pump 105 are communicatively connected to the control device 9 via a communication line or the like, similar to the first embodiment, and output detected physical quantities such as temperature and vibration frequency to the control device 9.

[0080] The control unit 91 of the control device 9 stores the temperatures and vibration frequencies acquired from the temperature sensors 111 and vibration sensors 112 provided on the cold head 103, the He compressor 104, and the diaphragm pump 105, respectively, in the memory unit 92, for example in table format, similar to the temperatures and vibration frequencies of the first vacuum pump 101, etc. of embodiment 1. The memory unit 92 of the control device 9 stores the accumulated operating times of the cold head 103, the He compressor 104, and the diaphragm pump 105, similar to the accumulated operating times of the first vacuum pump 101, etc. of embodiment 1.

[0081] The control unit 91 of the control device 9 acquires the temperatures and vibration frequencies detected by the respective temperature sensors 111 and vibration sensors 112, or refers to these temperatures, vibration frequencies, and accumulated operating times stored in the memory unit 92. In this way, the control unit 91 of the control device 9 can acquire the temperatures, vibration frequencies, and accumulated operating times of the cold head 103, the He compressor 104, and the diaphragm pump 105 when they are in operation.

[0082] In addition to the vacuum pump learning model 921, the memory unit 92 of the control device 9 of the second embodiment stores actual files of a cold head learning model 922, a He compressor learning model 923, and a diaphragm pump learning model 924. The cold head learning model 922, the He compressor learning model 923, and the diaphragm pump learning model 924 are configured by DNN or the like, similar to the vacuum pump learning model 921 of the first embodiment.

[0083] When physical quantities such as vibration and temperature of the cold head 103 and the accumulated operating time are input, the cold head learning model 922 outputs the time until failure of the cold head 103. By using the cold head learning model 922, the control unit 91 of the control device 9 can determine whether the cold head 103 has failed and derive the time until failure.

[0084] The He compressor learning model 923 outputs the time until failure of the He compressor 104 when the physical quantities such as vibration and temperature of the He compressor 104 and the accumulated operating time are input. The control unit 91 of the control device 9 can use the He compressor learning model 923 to determine whether the He compressor 104 has failed and derive the time until failure.

[0085] The diaphragm pump learning model 924 outputs the time until failure of the diaphragm pump 105 when the physical quantities such as vibration and temperature of the diaphragm pump 105 and the accumulated operating time are input. The control unit 91 of the control device 9 can use the diaphragm pump learning model 924 to determine whether the diaphragm pump 105 has failed and derive the time until failure.

[0086] Fig. 6 is a flowchart showing an example of a processing procedure by the control unit 91. Fig. 7 is a flowchart showing an example of a processing procedure (cold head 103) by the control unit 91. Fig. 8 is a flowchart showing an example of a processing procedure (He compressor 104) by the control unit 91. Fig. 9 is a flowchart showing an example of a processing procedure (diaphragm pump 105) by the control unit 91. The control unit 91 of the control device 9 included in the laser annealing apparatus 1 receives an operation by an operator using, for example, a keyboard connected to an input / output, and performs the following processes based on the received operation.

[0087] The control unit 91 of the control device 9 performs processing related to the vacuum pump (S21). The processing related to the vacuum pump is performed, for example, as a subroutine processing according to the flow shown in Fig. 4, similar to the first embodiment. The control unit 91 of the control device 9 performs the same processing related to the vacuum pump as the series of processing (S100 to S110) described in the first embodiment.

[0088] The control unit 91 of the control device 9 performs a process related to the cold head 103 (S22). The process related to the cold head 103 is performed, for example, as a subroutine process according to the following flow shown in FIG.

[0089] The control unit 91 of the control device 9 acquires physical information and the like of the cold head 103 (S220). The control unit 91 of the control device 9 acquires the temperature and vibration frequency detected by the temperature sensor 111 and vibration sensor 112 of the cold head 103, or acquires the temperature and vibration frequency when the cold head 103 is in operation and the accumulated operating time by referring to the memory unit 92.

[0090] The control unit 91 of the control device 9 derives the time until failure of the cold head 103 by inputting physical information of the cold head 103, etc., into the learning model 922 for the cold head (S221). The control unit 91 of the control device 9 inputs the acquired temperature, frequency, and integrated operating time of the cold head 103 into the learning model 922 for the cold head, and derives the time until failure of the cold head 103.

[0091] The control unit 91 of the control device 9 determines whether the time until failure is 0 (S222). If the time until failure is 0 (S222: YES), the control unit 91 of the control device 9 generates a notification signal indicating that the cold head 103 is in a faulty state (S223). If the value output by the cold head learning model 922 is 0, the control unit 91 of the control device 9 determines that the cold head 103 is in a faulty state, generates a notification signal indicating that the cold head 103 is in a faulty state, and outputs it to the display device 941 or the like.

[0092] If the time to failure is not 0 (S222: NO), the control unit 91 of the control device 9 calculates the time of failure of the cold head 103 based on the time to failure (S224). The control unit 91 of the control device 9 outputs the calculated time of failure (S225). If the time to failure is not 0, the control unit 91 of the control device 9 determines that the cold head 103 is normal, and derives the expected time of failure by adding the time to failure and the planned outage period output by the cold head learning model 922 to the current date and time (when the cold head 103 is operational), and outputs it to the display device 941, etc.

[0093] The control unit 91 of the control device 9 performs processing related to the He compressor 104 (S23). The processing related to the He compressor 104 is performed, for example, as a subroutine processing according to the following flow shown in FIG.

[0094] The control unit 91 of the control device 9 acquires physical information and the like of the He compressor 104 (S230). The control unit 91 of the control device 9 acquires the temperature and vibration frequency detected by the temperature sensor 111 and vibration sensor 112 of the He compressor 104, or by referring to the memory unit 92, acquires the temperature and vibration frequency when the He compressor 104 is in operation, and the accumulated operating time.

[0095] The control unit 91 of the control device 9 derives the time until failure of the He compressor 104 by inputting physical information, etc. of the He compressor 104 into the He compressor learning model 923 (S231). The control unit 91 of the control device 9 inputs the acquired temperature, frequency, and integrated operating time of the He compressor 104 into the He compressor learning model 923 to derive the time until failure of the He compressor 104.

[0096] The control unit 91 of the control device 9 determines whether the time until failure is 0 or not (S232). If the time until failure is 0 (S232: YES), the control unit 91 of the control device 9 generates a notification signal indicating that the He compressor 104 is in a faulty state (S233). If the value output by the He compressor learning model 923 is 0, the control unit 91 of the control device 9 determines that the He compressor 104 is in a faulty state, and generates a notification signal indicating that the He compressor 104 is in a faulty state, and outputs it to the display device 941, etc.

[0097] If the time until failure is not 0 (S232: NO), the control unit 91 of the control device 9 calculates the failure time of the He compressor 104 based on the time until failure (S234). The control unit 91 of the control device 9 outputs the calculated failure time (S235). If the time until failure is not 0, the control unit 91 of the control device 9 determines that the He compressor 104 is normal, and derives the expected failure time by adding the time until failure and the planned shutdown period output by the He compressor learning model 923 to the current date and time (when the He compressor 104 is operating), and outputs this to the display device 941, etc.

[0098] The control unit 91 of the control device 9 performs a process related to the diaphragm pump 105 (S24). The process related to the diaphragm pump 105 is performed, for example, as a subroutine process according to the following flow shown in FIG.

[0099] The control unit 91 of the control device 9 acquires physical information and the like of the diaphragm pump 105 (S240). The control unit 91 of the control device 9 acquires the temperature and vibration frequency detected by the temperature sensor 111 and the vibration sensor 112 of the diaphragm pump 105, or acquires the temperature and vibration frequency when the diaphragm pump 105 is in operation and the accumulated operating time by referring to the memory unit 92.

[0100] The control unit 91 of the control device 9 inputs physical information of the diaphragm pump 105, etc., into the diaphragm pump learning model 924 to derive the time until the diaphragm pump 105 fails (S241). The control unit 91 of the control device 9 inputs the acquired temperature, frequency, and integrated operating time of the diaphragm pump 105 into the diaphragm pump learning model 924 to derive the time until the diaphragm pump 105 fails.

[0101] The control unit 91 of the control device 9 determines whether the time until failure is 0 (S242). If the time until failure is 0 (S242: YES), the control unit 91 of the control device 9 generates a notification signal indicating that the diaphragm pump 105 is in a faulty state (S243). If the value output by the diaphragm pump learning model 924 is 0, the control unit 91 of the control device 9 determines that the diaphragm pump 105 is in a faulty state, generates a notification signal indicating that the diaphragm pump 105 is in a faulty state, and outputs it to the display device 941 or the like.

[0102] If the time to failure is not 0 (S242: NO), the control unit 91 of the control device 9 calculates the time to failure of the diaphragm pump 105 based on the time to failure (S244). The control unit 91 of the control device 9 outputs the calculated time to failure (S245). If the time to failure is not 0, the control unit 91 of the control device 9 determines that the diaphragm pump 105 is normal, and derives the expected time to failure by adding the time to failure and the planned outage period output by the diaphragm pump learning model 924 to the current date and time (when the diaphragm pump 105 is operational), and outputs it to the display device 941, etc.

[0103] According to this embodiment, the movable parts include at least one of the vacuum pump, the diaphragm pump 105, the helium compressor, and the cold head 103, and therefore failure prediction for each of these various movable parts can be efficiently performed. The control unit 91 of the control device 9 may perform each of the processes from S21 to S24 corresponding to each driving part in parallel by multi-processing or the like. By performing the process related to the derivation of the time until failure for each of the multiple movable parts included in the laser annealing apparatus 1 in parallel, the processing time can be shortened.

[0104] (Embodiment 3) 10 is an explanatory diagram showing an example of a feature amount learning model 925 according to the third embodiment (feature amount learning model 925). The control unit 91 (fault prediction unit) of the control device 9 of the third embodiment inputs feature amounts derived from the vibration frequency, temperature, time to reach vacuum, and integrated operation time of movable parts such as the first vacuum pump 101 to the feature amount learning model 925. The control unit 91 of the control device 9 derives the feature amounts by, for example, principal component analysis or dimensional compression of the vibration frequency, temperature, time to reach vacuum, and integrated operation time.

[0105] The feature learning model 925 estimates (outputs) a feature (next-point feature) subsequent to the input feature based on the input feature. That is, by using the feature learning model 925, it is possible to derive (estimate) a feature for the future based on a plurality of feature amounts in a time series from the past to the present. The control unit 91 (failure prediction unit) of the control device 9 can derive the time until a failure of a moving part such as the first vacuum pump 101 by comparing the value of the future feature estimated (output) by the feature learning model 925 with a predetermined threshold value (value of the feature amount leading to a failure).

[0106] The control unit 91 of the control device 9 constructs (generates) a neural network (feature learning model 925) that uses multiple time-series feature quantities as input and features at multiple future time points as output, by learning based on training data in which question data is used as question data and feature quantities at multiple future time points.

[0107] The input layer of the feature learning model 925 has one or more neurons that receive multiple features in a time series, and passes each of the input features to the intermediate layer. The intermediate layer includes an autoregressive layer that includes multiple neurons. The autoregressive layer is implemented as, for example, a Long Short Term Memory (LSTM) model, and a neural network including such an autoregressive layer is called an RNN (recurrent neural network). The intermediate layer outputs the amount of change due to each of the multiple features input sequentially along the time series. The output layer has one or more neurons for the features at multiple time points in the future, and outputs the features at multiple time points in the future based on the amount of change due to each of the multiple features output from the intermediate layer. Learning for such an RNN is performed using, for example, a BPTT (Backpropagation Through Time) algorithm.

[0108] The training data may be stored in an array format. When the training data is in an array format, for example, the values ​​of each of the elements from 0 to 4 (t-4 to t) of the array number may be used as question data, and the values ​​of each of the elements from 5 to 7 (t+1 to t+3) of the array number may be used as answer data. The time-series question data (t-2, t-1, t) input from the input layer is sequentially passed to the LSTM (autoregressive layer), and the LSTM (autoregressive layer) outputs the output value to the output layer and its own layer, thereby processing sequence information including temporal changes and order.

[0109] In this embodiment, the input and output of the feature learning model 925 are features obtained by principal component analysis of the vibration frequency, etc., but this is not limited to this, and the values ​​of the vibration frequency, temperature, time to reach vacuum, and accumulated operating time may be input to the feature learning model 925, etc.

[0110] 11 is a diagram for explaining an example of a management screen for movable parts. The control unit 91 of the control device 9 acquires feature quantities at multiple future time points output from the feature quantity learning model 925, and generates a management screen (screen data) shown as an example in this embodiment using the acquired feature quantities, and outputs it to, for example, the display device 941.

[0111] The movable part management screen includes a list display area that shows the physical quantities, etc. of each movable part in list form, a feature trend display area that displays the trends in feature quantities in graph form, and a maintenance work advice display area that displays advice regarding maintenance work.

[0112] The list display area displays the current physical quantities of the target moving parts (first vacuum pump 101, second vacuum pump 102, etc.), such as vibration (frequency data) and temperature (temperature data), time to vacuum, accumulated operating time, feature value, time until failure, planned downtime, and spare parts inventory. The physical quantities of the vibration (frequency data) and temperature (temperature data), time to vacuum, and accumulated operating time are displayed based on the detection results from the detection unit such as the temperature sensor 111, and items stored in the storage unit 92 of the control device 9, as in the first embodiment. The feature value is a value of a feature derived by, for example, principal component analysis or dimensional compression of the vibration frequency, temperature, time to vacuum, and accumulated operating time.

[0113] The time to failure is calculated as the difference between the elapsed time until a threshold value (the value of the feature leading to failure) is reached among the features at multiple future time points output from the feature learning model 925, and the elapsed time until the present time. The planned downtime is a period during which the target moving part is scheduled to be stopped from operating based on the operation plan and maintenance and inspection plan of the laser annealing apparatus 1. The time of failure is the time obtained by adding the time to failure and the planned downtime to the current date and time, and is the time when the target moving part is expected to break down.

[0114] In the feature transition display area, the transition of the feature for each target moving part is displayed in a graph format. The vertical axis of the graph indicates the value of the feature. The horizontal axis indicates the elapsed time, that is, the operating time of the moving part such as the first vacuum pump 101. As the operating time (accumulated operating time / number of times vacuuming is performed) increases, the value of the feature tends to increase, and when the value of the feature reaches a threshold value (the value of the feature that leads to a failure), it is predicted that the moving part is highly likely to fail. The points (indicated by circles) shown in the diagram indicate the derived feature. The length between two adjacent points (indicated by circles) indicates the operating time of one operation of the moving part such as the first vacuum pump 101. The elapsed time indicated by the points is the accumulated operating time when the feature is derived.

[0115] As described above, the control unit 91 of the control device 9 derives the feature quantity by, for example, principal component analysis or dimensional compression of the vibration frequency, temperature, time to reach vacuum, and integrated operation time. By inputting a plurality of feature quantities in a time series from the past to the present into the feature quantity learning model 925, it is possible to output one or more feature quantities (future feature quantities) that are next to the feature quantity at the present time, i.e., the last feature quantity among the feature quantities in the input time series. Furthermore, by recursively inputting a plurality of feature quantities in a time series including the output feature quantities after the next feature quantity to the feature quantity learning model 925, it is possible to output (predict) the feature quantities after that. As a result, it is possible to grasp the increasing trend of the feature quantity and calculate the time from the present time until the preset threshold value (value of the feature quantity leading to a failure) is reached, thereby deriving the time until a failure.

[0116] The threshold value may be dynamically changeable by, for example, an operation by an administrator or the like of the laser annealing apparatus 1. The control unit 91 of the control device 9 accepts a change in the threshold value by the administrator or the like, and when the threshold value is changed, recalculates the time until failure based on the changed threshold value. The control unit 91 of the control device 9 calculates the time until failure by adding the time until failure to the current date and time. Alternatively, the control unit 91 of the control device 9 may calculate the time until failure by adding the time until failure and the planned downtime to the current date and time.

[0117] The maintenance work advice display area displays advice text generated based on the time until failure or the time of failure of the target moving part, etc. The control unit 91 of the control device 9 may generate advice on the current time until failure of the moving part, etc., by referring to a table in which states (time until failure or the time of failure, etc.) stored in the storage unit 92 are associated with actions (advice), for example.

[0118] (Other embodiments) 12, 13, 14, 15, and 16 are cross-sectional views showing steps in a method for manufacturing a semiconductor device according to another embodiment (method for manufacturing a semiconductor device). As another embodiment, a method for manufacturing a semiconductor device using the laser annealing apparatus 1 according to the above embodiment will be described. In the method for manufacturing a semiconductor device described below, in the step of crystallizing an amorphous semiconductor film, an annealing process is performed using the laser annealing apparatus 1 according to any one of the first to fourth embodiments.

[0119] The semiconductor device is a semiconductor device including a TFT (Thin Film Transistor), and in this case, the amorphous silicon film 84 can be crystallized by irradiating it with laser light to form a polysilicon film 85. The polysilicon film 85 is used as a semiconductor layer having a source region, a channel region, and a drain region of the TFT.

[0120] The laser annealing apparatus 1 according to the embodiment described above is suitable for manufacturing a TFT array substrate. A method for manufacturing a semiconductor device having a TFT will now be described.

[0121] First, as shown in FIG. 12, a gate electrode 82 is formed on a glass substrate 81 (substrate 8). The gate electrode 82 may be, for example, a thin metal film containing aluminum or the like. Next, as shown in FIG. 13, a gate insulating film 83 is formed on the gate electrode 82. The gate insulating film 83 is formed so as to cover the gate electrode 82. Thereafter, as shown in FIG. 14, an amorphous silicon film 84 is formed on the gate insulating film 83. The amorphous silicon film 84 is disposed so as to overlap the gate electrode 82 with the gate insulating film 83 interposed therebetween.

[0122] The gate insulating film 83 is a silicon nitride film (SiNx), a silicon oxide film (SiO2 film), or a laminated film thereof. Specifically, the gate insulating film 83 and the amorphous silicon film 84 are continuously formed by a CVD (Chemical Vapor Deposition) method. The glass substrate 81 with the amorphous silicon film 84 becomes a semiconductor film in the laser annealing apparatus 1 (laser irradiation apparatus).

[0123] 15, the amorphous silicon film 84 is irradiated with laser light L3 using the laser annealing apparatus 1 described above to crystallize the amorphous silicon film 84, thereby forming a polysilicon film 85. As a result, a polysilicon film 85 in which silicon is crystallized is formed on the gate insulating film 83.

[0124] 16, an interlayer insulating film 86, a source electrode 87a, and a drain electrode 87b are formed on the polysilicon film 85. The interlayer insulating film 86, the source electrode 87a, and the drain electrode 87b can be formed by using a general photolithography method or a film formation method. The manufacturing process thereafter differs depending on the device to be finally manufactured, so a description thereof will be omitted.

[0125] By using the above-described semiconductor device manufacturing method, a semiconductor device having a TFT including a polycrystalline semiconductor film can be manufactured. Such a semiconductor device is suitable for controlling a high-definition display such as an organic EL (Electro Luminescence) display. By suppressing unevenness in the polysilicon film 85 as described above, a display device with excellent display characteristics can be manufactured with high productivity.

[0126] In carrying out this series of processing steps, the time of failure of movable parts such as a vacuum pump included in the laser annealing apparatus 1 is predicted by the process disclosed in the first embodiment etc. based on physical quantities etc. acquired when the movable parts are operated. Depending on the predicted time of failure, maintenance work such as inspection or replacement of these movable parts can be performed, thereby reducing the downtime of the laser annealing apparatus 1 and improving the operation rate of the laser annealing apparatus 1.

[0127] The present disclosure is not limited to the above-mentioned embodiment, and can be appropriately modified within the scope of the present disclosure. For example, in is not limited to the example of forming the polysilicon film 85 by irradiating the amorphous silicon film 84 with a laser beam, but a microcrystalline silicon film may be formed by irradiating the amorphous silicon film 84 with a laser beam. Also, a crystallized film may be formed by irradiating an amorphous film other than a silicon film with a laser beam.

[0128] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. [Explanation of symbols]

[0129] 1. Laser annealing device (laser irradiation device) 11 Annealing optical system 2 Laser light source 21 First laser light source 22 Second laser light source 31 1st attenuator 32 2nd attenuator 41 First polarization ratio control unit 42 Second polarization ratio control unit 5 Synthetic optical system 6 Beam shaping optics 61 Reflection mirror 65 Projection Lens 7 Laser irradiation chamber Stage 71 72 Base 8 Substrate 9 Control device 91 Control section (failure prediction section) 92 Memory section 920 Recording media P Program (Program Product) 921 Vacuum Pump Learning Model 922 Coldhead Learning Model Learning model for 923 He compressor 924 Diaphragm Pump Learning Model 925 Feature Learning Model 93 Communications Department 94 Input / Output Interface 941 Display device 101 First vacuum pump 102 Second Vacuum Pump 103 Cold Head 104He Compressor 105 Diaphragm Pump 111 Temperature Sensor 112 Vibration Sensor 113 Pressure Sensor 81 Glass Substrate 82 Gate electrode 83 Gate insulating film 84 Amorphous silicon film 85 Polysilicon Film 86 Interlayer insulating film 87a Source electrode 87b Drain electrode

Claims

1. A laser irradiation device including a laser light source, a failure prediction unit that predicts a failure of a movable part used in processing a substrate by the laser light source, The failure prediction unit is Acquire a physical quantity when the movable part is moved, Deriving a failure time of the moving part based on the acquired physical quantity; The moving parts include at least one of a diaphragm pump, a helium compressor, and a cold head. Laser irradiation device.

2. the movable part is provided with a detection unit that detects a physical quantity when the movable part is moved, The detection unit includes at least one of a temperature sensor and a vibration sensor, The failure prediction unit obtains a physical quantity when the movable part is moved from the detection unit.

2. The laser irradiation device according to claim 1.

3. The failure prediction unit is The time to failure of the moving part is derived by inputting the acquired physical quantity into a learning model that outputs the time until the moving part fails when the physical quantity at the time when the moving part is moved is input.

3. The laser irradiation device according to claim 1 or 2.

4. The failure prediction unit is Acquire an integrated operating time of the movable part; Derive the time when the moving part will fail based on the acquired cumulative operating time and physical quantity. The laser irradiation device according to any one of claims 1 to 3.

5. the movable part is a vacuum pump that sucks in gas sealed in a chamber of the laser light source; The failure prediction unit is acquiring a vacuum attainment time from when the vacuum pump starts to suck in gas until a vacuum state is reached in the chamber; The time when the vacuum pump will fail is calculated based on the acquired physical quantity and the time to reach vacuum. The laser irradiation device according to any one of claims 1 to 4.

6. the laser irradiation device includes a plurality of the laser light sources and a plurality of the vacuum pumps corresponding to the plurality of the laser light sources, The failure prediction unit is Acquire a physical quantity and a vacuum attainment time for each of the plurality of vacuum pumps; Deriving a failure time for each of the plurality of vacuum pumps based on the acquired physical quantities and the vacuum attainment times.

6. The laser irradiation device according to claim 5.

7. A computer that performs failure prediction for a movable part used in processing a substrate by a laser light source provided in a laser irradiation device, (A) acquiring a physical quantity when the movable part is moved; (B) deriving a failure time of the moving part based on the acquired physical quantity; The moving parts include at least one of a diaphragm pump, a helium compressor, and a cold head. An information processing method for executing a process.

8. A computer that performs failure prediction for a movable part used in processing a substrate by a laser light source provided in a laser irradiation device, (A) acquiring a physical quantity when the movable part is moved; (B) deriving a failure time of the moving part based on the acquired physical quantity; The moving parts include at least one of a diaphragm pump, a helium compressor, and a cold head. A program that executes a process.

Images