A spatial array UV LED light source and an AI time domain control photoetching method

Abstract

The embodiment of the application discloses a kind of space array UV LED light source and AI time domain control photoetching method, it is related to photoetching technical field.The method includes: obtaining mask plate file, the mask plate file is decomposed, obtains multiple basic patterns;Based on the preset basic pattern algorithm database, the multiple UV LED lamp pearl control algorithms corresponding to the multiple basic patterns of the mask plate file are respectively acquired, and the initial space array UV LED light source control algorithm is formed;Based on the preset neural network, the initial space array UV LED light source control algorithm is optimized by the way of mechanical learning, and the artificial intelligence light source-mask joint optimization algorithm is obtained;The space array UV LED light source is controlled based on the artificial intelligence light source-mask joint optimization algorithm and is exposed to wafer through the mask plate.The application has the technical effects of small amount of calculation and high product yield compared with traditional photoetching method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photolithography, in particular to a spatial array UV LED light source and an AI time domain control photolithography method. BACKGROUND

[0002] The traditional photolithography machine light source structure is a whole light source, which adjusts the scattered light into parallel light through an energy regulator, multiple refractive mirrors and a lens group. Due to the diffraction of light, the light passing through the edge of the mask plate pattern is diffracted, which causes the edge pattern to be blurred. The traditional photolithography machine uses computational lithography (CL) to realize source-mask collaborative optimization (SMO) to guide the energy regulator to adjust the light source brightness to match different mask plates for multiple photolithography. Although this method can partially improve the edge definition of the mask plate pattern, the compensation method of computational lithography requires a large amount of experience data and multiple photolithography with different mask plates, which has a large amount of calculation and low product yield. SUMMARY

[0003] The embodiments of the present application provide a spatial array UV LED light source and an AI time domain control photolithography method, which aims to solve the problem of large amount of calculation and low product yield of the traditional photolithography method.

[0004] In a first aspect, the embodiments of the present application provide an AI time domain control photolithography method, and the photolithography equipment includes a spatial array UV LED light source and a mask plate. The method includes:

[0005] Obtaining a mask plate file, decomposing the mask plate file to obtain a plurality of basic patterns;

[0006] Based on a preset basic pattern algorithm database, a plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns of the mask plate file are obtained respectively to form an initial spatial array UV LED light source control algorithm;

[0007] Based on a preset neural network, the initial spatial array UV LED light source control algorithm is optimized by a mechanical learning method to obtain an artificial intelligence source-mask joint optimization algorithm;

[0008] Based on the artificial intelligence source-mask joint optimization algorithm, the spatial array UV LED light source is controlled to expose the wafer through the mask plate.

[0009] Further technical solutions are that based on the preset basic pattern algorithm database, the plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns include:

[0010] determining whether the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database;

[0011] If the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database, reading the UV LED lamp bead control algorithm corresponding to the basic pattern from the basic pattern algorithm database.

[0012] A further technical solution is that the preset basic pattern algorithm database is used to obtain a plurality of UV LED lamp bead control algorithms corresponding to a plurality of basic patterns, and the method further comprises:

[0013] If the UV LED lamp bead control algorithm corresponding to the basic pattern does not exist in the basic pattern algorithm database, the basic pattern is split into a plurality of sub-patterns, wherein the UV LED lamp bead control algorithm corresponding to the sub-pattern exists in the basic pattern algorithm database.

[0014] The UV LED lamp bead control algorithms corresponding to the plurality of sub-patterns are read from the basic pattern algorithm database to form the UV LED lamp bead control algorithm of the basic pattern.

[0015] A further technical solution is that the preset neural network is used to optimize the initial spatial array UV LED light source control algorithm through mechanical learning to obtain an artificial intelligence light source-mask joint optimization algorithm, and the method further comprises:

[0016] The preset neural network is used to repeatedly iterate the photolithography results of the initial spatial array UV LED light source control algorithm under a plurality of different light source parameter conditions through mechanical learning until the photolithography results converge, and the artificial intelligence light source-mask joint optimization algorithm is obtained.

[0017] A further technical solution is that the generation process of the UV LED lamp bead control algorithm of the basic pattern comprises:

[0018] The preset neural network is used to repeatedly iterate the photolithography results of the initial UV LED lamp bead control algorithm of the basic pattern under a plurality of different light source parameter conditions through mechanical learning until the photolithography results converge, and the UV LED lamp bead control algorithm of the basic pattern is obtained.

[0019] A further technical solution is that the method further comprises:

[0020] The UV LED lamp bead control algorithm of the basic pattern is stored in a database to obtain the basic pattern algorithm database.

[0021] In a second aspect, the embodiments of the present application also provide a spatially arrayed UV LED light source applied to the method of the first aspect, the spatially arrayed UV LED light source comprising a control unit and a plurality of UV LED lamp beads arranged in an array, the control unit being composed of FTF thin film transistors, the control unit being capable of independently controlling each of the UV LED lamp beads, and the switching of the UV LED lamp beads being controlled by PWM signals.

[0022] In a third aspect, the embodiments of the present application also provide an AI time-domainized control photolithography device comprising a unit for executing the method described above.

[0023] In a fourth aspect, the embodiments of the present application also provide a computer device comprising a memory and a processor, the memory having a computer program stored thereon, and the processor implementing the method described above when executing the computer program.

[0024] In a fifth aspect, the embodiments of the present application also provide a computer readable storage medium, the storage medium storing a computer program, the computer program being capable of implementing the method described above when executed by a processor.

[0025] The embodiments of the present application provide a spatially arrayed UV LED light source, an AI time-domainized control photolithography method, device, equipment and medium. The method comprises: obtaining a mask file, decomposing the mask file to obtain a plurality of basic patterns; based on a preset basic pattern algorithm database, obtaining a plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns of the mask file respectively to form an initial spatially arrayed UV LED light source control algorithm; based on a preset neural network, optimizing the initial spatially arrayed UV LED light source control algorithm in a mechanical learning manner to obtain an artificial intelligence light source-mask joint optimization algorithm; and based on the artificial intelligence light source-mask joint optimization algorithm, controlling the spatially arrayed UV LED light source to expose a wafer through the mask. Compared with the traditional calculation photolithography method, the present application does not need to repeat calculation for different masks, greatly saving the device computing power, device storage space and algorithm formation time. In addition, unlike the traditional photolithography machine which can only use a fixed calculation photolithography compensation method and apply a large amount of experience data for correction in the calculation process, the present application can timely adjust the light source information through the iterative optimization of the light source-mask joint optimization algorithm based on artificial intelligence (AI), provide theoretical guidance for the selection of photolithography data, and improve the overall exposure effect of the photolithography pattern, so that the product yield is higher. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0027] Figure 1 A flowchart of an AI time-domain control photolithography method provided by the embodiment of the present application is shown in the figure.

[0028] Figure 2 A simplified diagram of the optical path principle of the photolithography machine when all the UV LED lamp beads of the spatial arrayed UV LED light source are turned on is shown in the figure.

[0029] Figure 3 A simplified diagram of the optical path principle of the photolithography machine when part of the UV LED lamp beads of the spatial arrayed UV LED light source are turned on is shown in the figure.

[0030] Figure 4 A structure diagram of a spatial arrayed UV LED light source provided by the embodiment of the present application is shown in the figure.

[0031] Figure 5 A schematic block diagram of an AI time-domain control photolithography device provided by the embodiment of the present application is shown in the figure.

[0032] Figure 6 A schematic block diagram of a computer device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] It should be understood that when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0035] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be further understood that the term "and / or" as used in the specification and in the claims, if any, means any combination of one or more of the associated listed items and all possible combinations and includes those combinations.

[0037] As used in the specification and in the claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to the determination" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0038] Referring to Figure 1 The embodiment of the present application provides an AI time-domain control photolithography method. Different from the prior art, in the embodiment of the present application, a photolithography device comprises a spatial arrayed UV LED light source and a mask plate. The spatial arrayed UV LED light source comprises a control unit and a plurality of UV LED lamp beads arranged in an array, the control unit is composed of FTF thin film transistors, the control unit can independently control each UV LED lamp bead, and the switching of the UV LED lamp bead is controlled through a PWM signal. As shown in the figure, Figure 1 The method comprises the following steps S1-S4.

[0039] S1, a mask plate file is acquired, and the mask plate file is decomposed to obtain a plurality of basic patterns.

[0040] In a specific implementation, the mask plate file can be specifically uploaded by a user. After receiving the mask plate file, feature extraction is performed on mask patterns in the mask plate file, data in a basic pattern database (the basic pattern database stores a plurality of basic patterns) is compared, and the mask patterns are divided into a plurality of different basic patterns, that is, a basic pattern group is obtained. The basic patterns include a circle, a rectangle, a triangle, a trapezoid, a square, a diamond, a star, a quadrilateral, a pentagon and a polygon, and the present application is not specifically limited.

[0041] Taking a circle as an example, when the mask pattern comprises a circle, since the circle is a pattern in the basic pattern database, the circle in the mask pattern is directly extracted as a basic pattern thereof.

[0042] Taking a five-pointed star as an example, when the mask pattern comprises a five-pointed star, since the five-pointed star is not a pattern in the basic pattern database, but an isosceles triangle and a pentagon are both patterns in the basic pattern database. By extracting the five-pointed star, the five-pointed star is divided into five isosceles triangles and one pentagon as basic patterns thereof by comparing the data in the basic pattern database.

[0043] S2, based on a preset basic pattern algorithm database, respectively acquiring a plurality of UV LED lamp bead control algorithms corresponding to a plurality of basic patterns of the mask file, and composing an initial spatial array UV LED light source control algorithm.

[0044] In a specific implementation, the basic pattern algorithm database stores UV LED lamp bead control algorithms corresponding to all basic patterns.

[0045] Understandably, in the present application, UV LED lamp bead control algorithms of basic patterns are acquired in advance, and a basic pattern algorithm database is constructed based on the UV LED lamp bead control algorithms of basic patterns.

[0046] The generation process of the UV LED lamp bead control algorithm of the basic pattern includes: based on a preset neural network, repeatedly iterating the lithography results of the initial UV LED lamp bead control algorithm preset for the basic pattern under a plurality of different light source parameter conditions through mechanical learning until the lithography results converge, to obtain the UV LED lamp bead control algorithm of the basic pattern. The initial UV LED lamp bead control algorithm includes a source-mask collaborative optimization algorithm, an optical proximity effect correction algorithm, and a computational lithography algorithm.

[0047] The neural network can be specifically a BP neural network, and the generation process of the UV LED lamp bead control algorithm of the basic pattern specifically includes:

[0048] First, upload the digital file of the basic pattern of the mask plate to the device;

[0049] Collect the lithography results optimized through the lithography algorithms such as the “source-mask collaborative optimization algorithm”, the “optical proximity effect correction algorithm”, and the “computational lithography algorithm” under the set different light source parameter conditions; based on the artificial intelligence model (AI) constructed through mechanical learning of the BP neural network, repeatedly iteratively calculate the modeling of the lithography results and the light source parameters, to obtain the light source parameter data when the lithography results are best and the corresponding relationship between the lithography results and the light source parameters; and form the UV LED lamp bead control algorithm of the basic pattern.

[0050] Taking a circular basic pattern as an example, upload the digital file of the circular mask plate to the device, and collect the lithography results optimized through the lithography algorithms such as the “source-mask collaborative optimization algorithm”, the “optical proximity effect correction”, and the “computational lithography” under the preset different light source parameter conditions. Based on the artificial intelligence model (AI) constructed through mechanical learning of the BP neural network, repeatedly iteratively calculate the modeling of the lithography results and the light source parameters. Obtain the light source parameter data when the lithography results are best and the corresponding relationship between the lithography results and the light source parameters, to form the UV LED lamp bead control algorithm of the circular mask pattern.

[0051] Further, the UV LED lamp bead control algorithm of the basic pattern is stored into a database to obtain the basic pattern algorithm database. Specifically, all the UV LED lamp bead control algorithms of the basic patterns are stored into the database to obtain the basic pattern algorithm database.

[0052] Further, the UV LED lamp bead control algorithms corresponding to the basic patterns are respectively acquired from the basic pattern algorithm database to form the initial spatial array UV LED light source control algorithm.

[0053] In an embodiment, the above step of "acquiring the UV LED lamp bead control algorithms corresponding to the basic patterns of the mask file based on the preset basic pattern algorithm database" specifically includes the following steps S21-S24:

[0054] S21, judging whether the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database.

[0055] In a specific implementation, firstly, it is judged whether the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database. If the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database, the algorithm is directly called. Normally, the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database, but the case where the algorithm does not exist cannot be excluded. If the UV LED lamp bead control algorithm corresponding to the basic pattern does not exist in the basic pattern algorithm database, the basic pattern needs to be split.

[0056] S22, if the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database, reading the UV LED lamp bead control algorithm corresponding to the basic pattern from the basic pattern algorithm database.

[0057] In a specific implementation, if the UV LED lamp bead control algorithm corresponding to the basic pattern exists in the basic pattern algorithm database, the UV LED lamp bead control algorithm corresponding to the basic pattern is directly read from the basic pattern algorithm database.

[0058] S23, if the UV LED lamp bead control algorithm corresponding to the basic pattern does not exist in the basic pattern algorithm database, splitting the basic pattern into a plurality of sub-patterns, wherein the UV LED lamp bead control algorithm corresponding to the sub-pattern exists in the basic pattern algorithm database.

[0059] In a specific implementation, if the UV LED lamp bead control algorithm corresponding to the basic pattern does not exist in the basic pattern algorithm database, the basic pattern is split into a plurality of sub-patterns, wherein the UV LED lamp bead control algorithm corresponding to the sub-pattern exists in the basic pattern algorithm database; that is, the principle of splitting the sub-pattern is that the UV LED lamp bead control algorithm corresponding to the sub-pattern must exist in the basic pattern algorithm database.

[0060] S24, read a plurality of UV LED lamp bead control algorithms corresponding to a plurality of sub-patterns from the basic pattern algorithm database to constitute the UV LED lamp bead control algorithm of the basic pattern.

[0061] In a specific implementation, after the basic pattern is split into a plurality of sub-patterns, a plurality of UV LED lamp bead control algorithms corresponding to the plurality of sub-patterns are read from the basic pattern algorithm database to constitute the UV LED lamp bead control algorithm of the basic pattern.

[0062] S3, based on a preset neural network, the initial spatial arrayed UV LED light source control algorithm is optimized through mechanical learning to obtain an artificial intelligence light source-mask joint optimization algorithm.

[0063] In a specific implementation, based on a preset neural network, the initial spatial arrayed UV LED light source control algorithm is repeatedly iterated under a plurality of different light source parameter conditions through mechanical learning until the lithography result converges, and the artificial intelligence light source-mask joint optimization algorithm is obtained.

[0064] The neural network can be a BP neural network, and the above step S3 specifically includes: based on the BP neural network mechanical learning, the lithography results under different light source parameters such as light intensity and incident angle are collected, and the relationship between the light source parameters and the lithography results is established, and the light source parameters when the best lithography result is obtained are obtained through repeated cyclic operation. Iterative operation refers to the calculation process of obtaining the light source parameters when the best lithography result is obtained through repeated cyclic operation. Due to the characteristics of the BP neural network in the process of constructing an artificial intelligence model (AI) through mechanical learning, the entire process cannot be trained through one calculation, and the final convergence result needs to be obtained through repeated calculation between the lithography result and the lithography data, so the iterative operation process needs to be repeated. Taking a five-point star-shaped mask pattern as an example, after obtaining the initial spatial arrayed UV LED light source control algorithm of the mask pattern with a five-point star pattern, based on the BP neural network, the lithography results under different light source parameters such as light intensity and incident angle are collected, sorted, iteratively operated, and the light source parameters when the optimal solution of the lithography result is determined, and finally the artificial intelligence (AI) light source-mask joint optimization algorithm is determined.

[0065] It can be understood that the BP neural network is only an example of a neural network provided by the present application, and other types of neural networks can also be used by those skilled in the art to implement the technical solutions of the present application, which will not exceed the protection scope of the present application.

[0066] S4, controlling the spatial arrayed UV LED light source to expose the wafer through the mask plate based on the artificial intelligence light source-mask joint optimization algorithm.

[0067] In a specific implementation, the spatial arrayed UV LED light source is controlled to expose the wafer through the mask plate by the artificial intelligence light source-mask joint optimization algorithm.

[0068] Specifically, the spatial arrayed UV LED light source is controlled by an artificial intelligence (AI) light source-mask joint optimization algorithm to achieve spatial regulation and time domain regulation.

[0069] The spatial regulation is specifically manifested in that the UV LED lamp beads at the exposure position are in an open state to perform photolithography on the device, and the UV LED lamp beads at the non-exposure position are in an off state to avoid photolithography on the device. For example, referring to Figures 2-3 , Figure 2 FIG. 1 is a simplified diagram of the light path principle of a photolithography machine when all UV LED lamp beads of the spatial arrayed UV LED light source are turned on, Figure 3 FIG. 2 is a simplified diagram of the light path principle of a photolithography machine when part of the UV LED lamp beads of the spatial arrayed UV LED light source are turned on. In this figure, Figures 2-3 In the figure, 10 refers to the spatial arrayed UV LED light source, 11 refers to the UV LED lamp bead, 20 refers to the first column of compound eye arrays, 30 refers to the second column of compound eye arrays, 40 refers to the mask plate, 50 refers to the focusing mirror, and 60 refers to the wafer.

[0070] The time domain regulation is specifically manifested in that the UV LED lamp beads adopt a pulse width modulation (PWM) mode, the light-on period of the UV LED is set to a high-frequency flashing state, and the light and dark of each UV LED lamp bead are regulated in each pulse interval. The UV LED lamp beads at the exposure position and the edge position of the exposure pattern inject a large current into the UV LED lamp beads in the open state through the PWM control mode, so that the UV LED lamp beads are in the highest brightness and power interval when working. The UV LED lamp beads at the non-exposure position are in the off state through the PWM control mode, and the UV LED lamp beads do not work.

[0071] The technical scheme of the embodiment of the present application is as follows: a mask plate file is acquired, the mask plate file is decomposed to obtain a plurality of basic patterns, a plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns of the mask plate file are respectively acquired based on a preset basic pattern algorithm database to form an initial spatial arrayed UV LED light source control algorithm, the initial spatial arrayed UV LED light source control algorithm is optimized in a mechanical learning manner based on a preset neural network to obtain an artificial intelligence light source-mask joint optimization algorithm, and the spatial arrayed UV LED light source is controlled to expose a wafer through the mask plate based on the artificial intelligence light source-mask joint optimization algorithm. Compared with traditional computing lithography, the present application does not need to repeat calculation for different mask plates, greatly saving device computing power, device storage space and algorithm formation time. In addition, unlike the traditional lithography machine which can only use a fixed computing lithography compensation method and apply a large amount of experience data for correction in the calculation process, the present application can timely adjust the light source information through the iterative optimization of the light source-mask joint optimization algorithm based on artificial intelligence (AI), provide theoretical guidance for the selection of lithography data, and improve the overall exposure effect of the lithography pattern, so that the product yield is higher.

[0072] Referring to Figure 4 The embodiment of the present application provides a spatial arrayed UV LED light source, which is applied to the AI time-domain control lithography method provided in any of the above embodiments. The spatial arrayed UV LED light source 10 includes a control unit and a plurality of UV LED lamp beads 11 arranged in an array. The control unit is composed of an FTF thin film transistor. The control unit can independently control each UV LED lamp bead 11. The switching of the UV LED lamp bead 11 is controlled by a PWM signal.

[0073] Referring to Figure 5 , Figure 5 is a schematic block diagram of an AI time-domain control lithography device provided by the embodiment of the present application. Corresponding to the above AI time-domain control lithography method, the present application further provides an AI time-domain control lithography device 200. The AI time-domain control lithography device 200 includes units for executing the above AI time-domain control lithography method. The AI time-domain control lithography device 200 can be configured in a desktop computer, a tablet computer, a laptop computer, or the like terminal. Specifically, the AI time-domain control lithography device 200 includes:

[0074] A first acquisition unit 210 is configured to acquire a mask plate file, decompose the mask plate file to obtain a plurality of basic patterns.

[0075] The second acquisition unit 220 is configured to acquire, based on a preset basic pattern algorithm database, a plurality of UV LED lamp bead control algorithms corresponding to a plurality of basic patterns in the mask file respectively, to form an initial spatial array UV LED light source control algorithm.

[0076] The optimization unit 230 is configured to optimize the initial spatial array UV LED light source control algorithm based on a preset neural network by means of mechanical learning, to obtain an artificial intelligence light source-mask joint optimization algorithm.

[0077] The exposure unit 240 is configured to control the spatial array UV LED light source to expose a wafer through the mask based on the artificial intelligence light source-mask joint optimization algorithm.

[0078] In an embodiment, the acquiring, based on the preset basic pattern algorithm database, of the plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns comprises:

[0079] determining whether the basic pattern algorithm database contains a UV LED lamp bead control algorithm corresponding to the basic pattern;

[0080] if the basic pattern algorithm database contains the UV LED lamp bead control algorithm corresponding to the basic pattern, reading the UV LED lamp bead control algorithm corresponding to the basic pattern from the basic pattern algorithm database.

[0081] In an embodiment, the acquiring, based on the preset basic pattern algorithm database, of the plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns further comprises:

[0082] if the basic pattern algorithm database does not contain the UV LED lamp bead control algorithm corresponding to the basic pattern, splitting the basic pattern into a plurality of sub-patterns, wherein the basic pattern algorithm database contains a UV LED lamp bead control algorithm corresponding to the sub-pattern;

[0083] reading a plurality of UV LED lamp bead control algorithms corresponding to the plurality of sub-patterns from the basic pattern algorithm database to form the UV LED lamp bead control algorithm of the basic pattern.

[0084] In an embodiment, the optimizing, based on the preset neural network, of the initial spatial array UV LED light source control algorithm by means of mechanical learning to obtain the artificial intelligence light source-mask joint optimization algorithm comprises:

[0085] Based on the preset neural network, the lithography results of the initial spatial array UV LED light source control algorithm under a plurality of different light source parameter conditions are repeatedly iterated in a mechanical learning manner until the lithography results converge, and the artificial intelligence light source-mask joint optimization algorithm is obtained.

[0086] In an embodiment, the generation process of the UV LED lamp bead control algorithm of the basic pattern includes:

[0087] Based on the preset neural network, the lithography results of the initial UV LED lamp bead control algorithm of the basic pattern preset are repeatedly iterated in a mechanical learning manner under a plurality of different light source parameter conditions until the lithography results converge, and the UV LED lamp bead control algorithm of the basic pattern is obtained.

[0088] In an embodiment, the AI time-domain control lithography device 200 further includes:

[0089] The construction unit is configured to store the UV LED lamp bead control algorithm of the basic pattern into a database to obtain the basic pattern algorithm database.

[0090] It should be noted that the specific implementation process of the AI time-domain control lithography device 200 and each unit can be clearly understood by those skilled in the art, and the corresponding description in the foregoing method embodiments can be referred to. In order to describe conveniently and concisely, it will not be repeated here.

[0091] The AI time-domain control lithography device 200 can be realized in the form of a computer program, which can run on a computer device as shown in the computer device. Figure 6 The computer device can be a terminal or a server.

[0092] Please refer to Figure 6 , Figure 6 is a schematic block diagram of a computer device provided by an embodiment of the present application. The computer device 500 can be a terminal or a server, wherein the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, a wearable device, and an electronic device with a communication function. The server can be a stand-alone server or a server cluster composed of multiple servers.

[0093] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, wherein the memory can include a non-volatile storage medium 503 and an internal memory 504.

[0094] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, can cause the processor 502 to perform an AI time-domain control lithography method.

[0095] The processor 502 is configured to provide computing and control capabilities to support the operation of the entire computer device 500.

[0096] The non-volatile storage medium 503 provides an environment for the computer program 5032 stored therein to run. The computer program 5032, when executed by the processor 502, can cause the processor 502 to perform an AI time-domain control lithography method.

[0097] The network interface 505 is configured to perform network communication with other devices. Those skilled in the art can understand that the above structure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device 500 to which the scheme of the present application is applied. The specific computer device 500 can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] The processor 502 is configured to run the computer program 5032 stored in the memory to implement the steps of the AI time-domain control lithography method provided in any of the above method embodiments.

[0099] It should be understood that, in the embodiments of the present application, the processor 502 can be a central processing unit (CPU), and the processor 502 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0100] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the above-described embodiments.

[0101] Therefore, the present application further provides a storage medium. The storage medium can be a computer-readable storage medium. The storage medium stores a computer program. The computer program is executed by a processor to enable the processor to perform the steps of the AI time-domain control photolithography method provided by any of the method embodiments.

[0102] The storage medium is a physical, non-transient storage medium, for example, can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various physical storage media that can store program codes. The computer-readable storage medium can be non-volatile or volatile.

[0103] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0104] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed.

[0105] The steps in the method embodiments of the present application can be adjusted, combined and reduced in sequence according to actual needs. The units in the device embodiments of the present application can be combined, divided and reduced according to actual needs. In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0106] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art that contributes to the present application, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application.

[0107] In the above-described embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0108] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, these modifications and variations of the present application also belong to the scope of the claims of the present application and its equivalent technologies, and the present application also intends to include these modifications and variations.

[0109] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An AI temporalization control photolithography method, characterized in that, The photolithography device includes a spatially arrayed UV LED light source and a mask plate, and the method includes: obtaining a mask plate file, decomposing the mask plate file to obtain a plurality of basic patterns; based on a preset basic pattern algorithm database, obtaining a plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns of the mask plate file respectively to form an initial spatially arrayed UV LED light source control algorithm; based on a preset neural network, optimizing the initial spatially arrayed UV LED light source control algorithm through mechanical learning to obtain an artificial intelligence light source-mask joint optimization algorithm; based on the artificial intelligence light source-mask joint optimization algorithm, controlling the spatially arrayed UV LED light source to expose a wafer through the mask plate; wherein, based on the preset basic pattern algorithm database, obtaining a plurality of UV LED lamp bead control algorithms corresponding to the plurality of basic patterns respectively includes: determining whether there is a UV LED lamp bead control algorithm corresponding to the basic pattern in the basic pattern algorithm database: if there is a UV LED lamp bead control algorithm corresponding to the basic pattern in the basic pattern algorithm database, reading the UV LED lamp bead control algorithm corresponding to the basic pattern from the basic pattern algorithm database; if there is no UV LED lamp bead control algorithm corresponding to the basic pattern in the basic pattern algorithm database, splitting the basic pattern into a plurality of sub-patterns, wherein there is a UV LED lamp bead control algorithm corresponding to the sub-pattern in the basic pattern algorithm database; reading a plurality of UV LED lamp bead control algorithms corresponding to the plurality of sub-patterns from the basic pattern algorithm database to form the UV LED lamp bead control algorithm of the basic pattern.

2. The AI time-domained control lithography method of claim 1, wherein, based on the preset neural network, the initial spatially arrayed UV LED light source control algorithm is optimized through mechanical learning to obtain an artificial intelligence light source-mask joint optimization algorithm, which includes: based on the preset neural network, the photolithography results of the initial spatially arrayed UV LED light source control algorithm under a plurality of different light source parameter conditions are repeatedly iterated through mechanical learning until the photolithography results converge, and the artificial intelligence light source-mask joint optimization algorithm is obtained.

3. The method of claim 1, wherein, The generation process of the UV LED lamp bead control algorithm of the basic pattern includes: based on the preset neural network, the initial UV LED lamp bead control algorithm of the basic pattern under a plurality of different light source parameter conditions is repeatedly iterated through mechanical learning until the photolithography results converge, and the UV LED lamp bead control algorithm of the basic pattern is obtained.

4. The method of claim 3, wherein, The method further includes: storing the UV LED lamp bead control algorithm of the basic pattern into a database to obtain the basic pattern algorithm database.

5. An AI time-domained control lithographic apparatus, characterized in that, includes units for performing the method as claimed in any one of claims 1-4.

6. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1-4 when executing the computer program.

7. A computer readable storage medium characterized in that, The storage medium stores a computer program, and the computer program can implement the method according to any one of claims 1-4 when being executed by a processor.

Images