Method, device and storage medium for OPC modeling

Abstract

According to an example embodiment of the present disclosure, a method, device and storage medium for OPC modeling are provided. The method includes: obtaining multiple peak wavelengths of a target light source for lithography and multiple cumulative radiation power densities corresponding to the multiple peak wavelengths respectively; determining multiple optical signals corresponding to the multiple peak wavelengths respectively, and the optical signals in the multiple optical signals indicate optical properties related to the wavelength; based on the multiple cumulative radiation power densities and the multiple optical signals, determining the weighted average of the multiple optical signals; and constructing an optical proximity correction OPC model based on the weighted average of the multiple optical signals. In this way, an OPC model suitable for a broadband light source can be constructed, so that the optical distortion in the process of lithography using a broadband light source can be compensated, so that the final formed pattern is closer to the design requirements.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of integrated circuit technology, and more particularly, to a method, device, and storage medium for OPC modeling. Background Art

[0002] Photolithography is a key process in chip manufacturing, used to transfer circuit patterns onto silicon wafers. As chip manufacturing processes continue to shrink, the problem of image distortion in photolithography becomes more and more prominent. The Optical Proximity Correction (OPC) model came into being. It models and analyzes phenomena such as light propagation and diffraction during the photolithography process, and corrects and compensates for the original design graphics to improve the accuracy and quality of the photolithography graphics and ensure the yield rate of chip manufacturing. It is an indispensable and important part of advanced photolithography technology. Summary of the invention

[0003] In a first aspect of the present disclosure, a method for OPC modeling is provided. The method includes: obtaining multiple peak wavelengths of a target light source for photolithography and multiple accumulated radiation power densities corresponding to the multiple peak wavelengths, respectively; determining multiple optical signals corresponding to the multiple peak wavelengths, respectively, wherein the optical signals in the multiple optical signals indicate wavelength-related optical properties; determining a weighted average of the multiple optical signals based on the multiple accumulated radiation power densities and the multiple optical signals; and constructing an optical proximity correction (OPC) model based on the weighted average of the multiple optical signals.

[0004] In a second aspect of the present disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor. The memory has instructions stored therein, and when the instructions are executed by the processor, the electronic device executes the method according to the first aspect of the present disclosure.

[0005] In a third aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0006] It will be understood from the following description that according to an embodiment of the present disclosure, a plurality of peak wavelengths of a target light source for lithography and a plurality of cumulative radiation power densities corresponding to the plurality of peak wavelengths are first obtained. Further, a plurality of optical signals corresponding to the plurality of peak wavelengths are determined, wherein the optical signals in the plurality of optical signals indicate optical properties related to the wavelength. Further, based on the plurality of cumulative radiation power densities and the plurality of optical signals, a weighted average of the plurality of optical signals is determined. Finally, based on the weighted average of the plurality of optical signals, an optical proximity correction (OPC) model is constructed. In this way, an OPC model suitable for a broadband light source can be constructed, thereby compensating for optical distortion in the process of lithography using a broadband light source, so that the final formed pattern is closer to the design requirements.

[0007] It should be understood that the contents described in the summary of the present invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0009] Figure 1 A schematic diagram showing an example environment in which various embodiments of the present disclosure can be implemented;

[0010] Figure 2 A schematic diagram showing a spectrum of a target light source according to some embodiments of the present disclosure;

[0011] Figure 3A A schematic diagram showing a test layout for OPC model calibration according to some embodiments of the present disclosure is shown;

[0012] Figure 3B A schematic diagram showing another test layout for OPC modeling calibration according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A flowchart illustrating a process for OPC modeling according to some embodiments of the present disclosure; and

[0014] Figure 5 A block diagram of an electronic device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0017] Various example implementations of the scheme will be described in detail below with reference to the accompanying drawings.

[0018] See first Figure 1 , which shows a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. Figure 1 As shown, example environment 100 generally may include an electronic device 110 .

[0019] In some embodiments, the electronic device 110 may interact with other devices (not shown). For example, the electronic device 110 may receive input information from other devices and output feedback information to other devices. In some embodiments, the input message from other devices may be design layout data 120. The electronic device 110 may perform corresponding mathematical operations on the design layout data and output corresponding operation results 130 to other devices. In some embodiments, the operation results may be corrected layout data.

[0020] In the example environment 100, the electronic device 110 can be any type of device with computing capabilities, including a terminal device or a server device. The terminal device can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. The server device can include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0021] It should be understood that the structure and function of the environment 100 are described for exemplary purposes only, and do not imply any limitation on the scope of the present disclosure. Example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings.

[0022] As briefly mentioned above, the OPC model is an indispensable and important part of advanced lithography technology. Lithography technology aims to break through the hardware limitations of the minimum exposure size by improving software technologies such as resolution while keeping the hardware environment of existing lithography equipment unchanged, which has greatly promoted the development of advanced semiconductor processes.

[0023] In the photolithography process, due to physical phenomena such as diffraction and interference of light, there will be differences between the graphics formed by actual exposure and the designed graphics on the mask. This difference will be more obvious, especially when the feature size continues to shrink. The OPC model accurately models the physical process of light propagation and imaging during the photolithography process, analyzes the law and degree of graphic distortion, and then corrects the graphics on the mask. For example, for some long and thin line graphics, problems such as uneven line edges and inconsistent widths may occur after photolithography. The OPC model can appropriately deform the line graphics on the mask or add auxiliary graphics based on the calculation results to compensate for the optical distortion in the photolithography process, so that the graphics finally formed on the silicon wafer are closer to the design requirements.

[0024] Usually, the OPC model is constructed based on a light source with a single peak wavelength. However, in some cases, a light source with multiple peak wavelengths (hereinafter referred to as a "broadband light source" or "target light source") is required for photolithography. This makes it impossible to use traditional modeling methods to build OPC models, and other methods with lower correction accuracy have to be used to correct the graphics on the mask, resulting in the final graphics being far from meeting the design requirements.

[0025] To this end, an embodiment of the present disclosure proposes a scheme for OPC modeling. According to an embodiment of the present disclosure, a plurality of peak wavelengths of a target light source for lithography and a plurality of cumulative radiation power densities respectively corresponding to the plurality of peak wavelengths are first obtained. Further, a plurality of optical signals respectively corresponding to the plurality of peak wavelengths are determined, wherein the optical signals in the plurality of optical signals indicate optical properties related to the wavelength. Further, based on the plurality of cumulative radiation power densities and the plurality of optical signals, a weighted average of the plurality of optical signals is determined. Finally, based on the weighted average of the plurality of optical signals, an optical proximity correction OPC model is constructed. According to an embodiment of the present disclosure, an OPC model suitable for a broadband light source can be constructed, thereby compensating for optical distortion in the process of lithography using a broadband light source, so that the final formed pattern is closer to the design requirements.

[0026] In the following description, the embodiments will be described with reference to constructing an OPC model for the photolithography process in the organic light-emitting diode (OLED) process. Of course, it can be understood that the embodiments of the present disclosure are not limited to this application scenario. The OPC model construction process can also be used in other scenarios using broadband light sources.

[0027] With the continuous development of OLED technology, the line width involved (usually refers to the width of the line formed on the surface of materials such as silicon wafers after the photolithography process in semiconductor chip manufacturing) has become smaller and smaller, until it is so small that the optical proximity effect must be considered. However, the OLED process uses a broadband light source, which makes it impossible to use the traditional OPC model modeling method. In some schemes, in order to correct the mask used in the photolithography process in the OLED process, a rule-based correction method is used. However, compared with the OPC model, the correction accuracy of this method is lower, which limits the OLED manufacturing process and also affects the performance of OLED-related devices.

[0028] To this end, an embodiment of the present disclosure provides a solution for OPC modeling. Specifically, a plurality of peak wavelengths of a target light source used for photolithography of a target layout and a plurality of cumulative radiation power densities corresponding to the plurality of peak wavelengths are first obtained, wherein the cumulative radiation power density indicates the sum of the radiation power densities within a wavelength range related to the corresponding wavelength, and the wavelength range is determined based on the corresponding wavelength and a first predetermined threshold. For example, the target layout is a layout of an organic light emitting diode (OLED). Further, a plurality of optical signals corresponding to the plurality of peak wavelengths are determined, wherein the optical signals in the plurality of optical signals indicate optical properties related to the wavelength. Further, a weighted average of the plurality of optical signals is determined based on the plurality of cumulative radiation power densities and the plurality of optical signals. Finally, an OPC model is constructed based on the weighted average of the plurality of optical signals. In this way, the accuracy of mask correction in the photolithography process in the OLED process can be improved, thereby improving the performance of OLED-related devices.

[0029] The following is further combined with the attached Figure 2 Various example implementations of the scheme are described in detail. Figure 2 FIG. 2 is a schematic diagram showing a spectrum 200 of a target light source according to some embodiments of the present disclosure. In some embodiments, the above OPC modeling process can be performed as follows: Figure 1 The electronic device 110 shown in FIG. Figure 1 to explain in detail.

[0030] like Figure 2As shown in the spectrum 200, the abscissa represents the wavelength (nm) of the target light source, and the ordinate represents the radiation power density (W / nm) of the target light source. The peak wavelength refers to the wavelength corresponding to the maximum radiation power or relative radiation power in the spectral power distribution of the light source. It can be seen from the figure that the target light source has multiple peak wavelengths.

[0031] The peak wavelength can be obtained by calculation or measurement. For example, the peak wavelength can be obtained by correlation calculation based on the spectrum of the target light source. For another example, the peak wavelength can be obtained by measurement methods such as laser sheet scanning method, monochromator measurement method, spectral response characteristic measurement method based on optoelectronic devices, etc.

[0032] Taking the peak wavelength obtained by calculation as an example, in some embodiments, the spectrum 200 can be input into the electronic device 110. The electronic device 110 can determine the wavelength energy distribution function of the target light source according to the relevant data in the spectrum 200. For example, the wavelength energy distribution function can be expressed as follows:

[0033]

[0034] in, is the wavelength of the target light source. In this article, the wavelength energy distribution function P is sometimes also referred to as the "first correlation relationship".

[0035] After obtaining the wavelength energy distribution function, the electronic device 110 may determine a new function based on the first association relationship. For example, the electronic device 110 may perform a “local integration” on the wavelength energy distribution function to obtain a new function. For example, the new function may be expressed as follows:

[0036]

[0037] in, is the wavelength of the target light source, is a smaller number (for example, 1 nm, which can also be determined based on the measurement accuracy of function P). In this article, function Q is sometimes also referred to as the “second correlation”. Sometimes also referred to as the "first predetermined threshold".

[0038] It should be understood that the local integral refers to the function P at the wavelength passing through the target light source. and a first predetermined threshold The determined range Integrate on.

[0039] Thus, the value of the function Q obtained is the cumulative radiation power density, that is, the sum of the radiation power density within a wavelength range related to the corresponding wavelength, and the wavelength range is determined based on the wavelength and the first predetermined threshold.

[0040] After obtaining the function Q, the electronic device 110 can determine multiple candidate wavelengths and multiple accumulated radiation power densities corresponding to the multiple candidate wavelengths based on the new function. For example, the electronic device 110 can take the derivative of the function Q to obtain multiple wavelengths with maximum values. The value of and the corresponding value of the function Q. The multiple wavelengths obtained can be As multiple candidate wavelengths, respectively The corresponding values ​​of the multiple functions Q are used as multiple accumulated radiation power densities.

[0041] For example, multiple wavelengths The value of and the corresponding function Q can be expressed as follows:

[0042]

[0043] in, For multiple candidate wavelengths, is a number of accumulated radiation power densities.

[0044] Further, the electronic device 110 can select multiple cumulative radiation power densities greater than a second predetermined threshold from multiple cumulative radiation power densities corresponding to multiple candidate wavelengths, respectively, and determine multiple candidate wavelengths corresponding to the selected multiple cumulative radiation power densities greater than the second predetermined threshold as multiple peak wavelengths.

[0045] The second predetermined threshold value may be determined based on the maximum cumulative radiation power density of the target light source, where the maximum cumulative radiation power density indicates the sum of all radiation power densities within the wavelength range corresponding to the target light source. For example, the second predetermined threshold value may be a certain multiple of the maximum cumulative radiation power of the target light source. Alternatively or additionally, the multiple may be 0.5 times, and is usually not less than 0.2 times.

[0046] It should be understood that the above process of calculating the peak wavelength is only exemplary, and the peak wavelength may be calculated in any other suitable manner, which is not limited to the embodiments of the present disclosure.

[0047] The simulation signal of the traditional OPC model can be expressed by the following formula:

[0048]

[0049] Among them, simulation_signal represents the simulation signal, optical represents the optical signal, and the optical part corresponds to only one peak wavelength, Kernel represents the kernel function, C represents the parameter, and layout represents the corresponding image signal of each figure in the mask. represents the product symbol and @ represents the convolution symbol.

[0050] In order to construct an OPC model suitable for a broadband light source, the kernel function part can be removed and only the optical signal part can be retained. Specifically, the electronic device 110 can first determine a plurality of optical signals corresponding to a plurality of peak wavelengths. For example, the plurality of peak wavelengths determined from the plurality of candidate wavelengths are , then the optical signals corresponding to multiple peak wavelengths can be expressed as .

[0051] An optical signal among the plurality of optical signals indicates a wavelength-dependent optical characteristic.

[0052] For example, the optical signal corresponding to the peak wavelength can be used to describe various optical phenomena in the photolithography process at the peak wavelength, such as diffraction, interference, and scattering of light.

[0053] For example, in the case of shorter wavelengths, the diffraction of light is relatively weak, and it may be easier to achieve higher resolution in lithography; in the case of longer wavelengths, the diffraction phenomenon will be more obvious, which may cause more blurring and deformation at the edges of the lithographic patterns. Optical signals can also be functions used to quantify and analyze these phenomena.

[0054] For example, in a photolithography system, light intensity distribution is one of the key factors that determine the photoresist exposure effect. The optical signal corresponding to the peak wavelength can be used to calculate the light intensity distribution on the photoresist surface after passing through the mask, optical system, etc. at the peak wavelength.

[0055] For example, at different wavelengths, the aberrations and chromatic aberrations of the optical system will be different. The optical signal can combine these factors to more accurately describe the optical behavior in the lithography process.

[0056] It should be understood that the above examples related to optical signals with peak wavelengths, including descriptions of phenomena such as diffraction, interference, and scattering of light, as well as calculations of light intensity distribution and the influence of wavelength on optical models, can be obtained through a variety of methods, such as theoretical deduction, experimental research, numerical calculation, and simulation.

[0057] Furthermore, the electronic device 110 determines a plurality of weights corresponding to the plurality of optical signals respectively. The weights corresponding to the optical signals may be determined based on the wavelengths corresponding to the optical signals and the accumulated radiation powers corresponding to the wavelengths.

[0058] In some embodiments, the electronic device 110 obtains multiple candidate wavelengths of the target light source and multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths, and determines multiple cumulative radiation power densities corresponding to multiple peak wavelengths, and finally uses the ratio of the cumulative radiation power density corresponding to each of the multiple peak wavelengths to the sum of the multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths as multiple weights.

[0059] For example, the peak wavelength The corresponding optical signal weights can be expressed as follows:

[0060]

[0061] in, represents the cumulative radiation power density corresponding to the jth peak wavelength, represents the candidate wavelength, Represents the cumulative radiation power density corresponding to the i-th candidate wavelength.

[0062] It should be understood that the above process of calculating the weight of the optical signal is only exemplary, and any other suitable calculation method may also be used, and the embodiments of the present disclosure are not limited thereto.

[0063] Further, the electronic device 110 may determine a weighted average of the multiple optical signals based on the multiple optical signals and the multiple weights, and construct an OPC model based on the weighted average of the multiple optical signals.

[0064] For example, the constructed OPC model can be expressed as follows:

[0065]

[0066] in, For the OPC model, is the i-th candidate wavelength, is the cumulative radiation power density corresponding to the i-th candidate wavelength, is the jth peak wavelength, is the optical signal corresponding to the jth peak wavelength, is the cumulative radiation power density corresponding to the jth peak wavelength.

[0067] Therefore, the constructed OPC model can compensate for the optical distortion in the process of photolithography using a broadband light source, making the final pattern closer to the design requirements.

[0068] After the OPC model is constructed, the OPC model needs to be calibrated. In some embodiments, the electronic device 110 can obtain signals of each test pattern in the test layout, and calibrate the OPC model based on the signals of each test pattern in the test layout.

[0069] For example, the OPC model can be calibrated by performing a convolution operation on the OPC model and the image signal of the test layout to obtain a simulation signal, and the OPC model can be calibrated by the simulation signal.

[0070] For example, the simulation signal of the OPC model can be expressed as:

[0071] simulation_signal=[new_optical]@layout.

[0072] During the calibration process, due to the particularity of the OLED process, there are not a large number of graphics that can be used to calibrate the constructed OPC model. Since the constructed OPC model contains the optical signals corresponding to each peak wavelength in the target light source, and the proportion of the optical signal corresponding to each peak wavelength in the constructed OPC model can be determined by the weight of each optical signal, the constructed OPC model can reflect the physical quantities of multiple peak wavelengths of the target light source, so that the constructed OPC model can be closer to the physical essence of the target light source, so that the constructed OPC model has better "interpolation" and "extrapolation" capabilities.

[0073] Figure 3A FIG. 4 is a schematic diagram showing a test layout 300A for OPC model calibration according to some embodiments of the present disclosure. Figure 3B A schematic diagram of another test layout 300B for OPC modeling calibration according to some embodiments of the present disclosure is shown.

[0074] like Figure 3A and Figure 3B As shown, the test layout 300A includes multiple test patterns 310 evenly distributed, and the distance between two adjacent test patterns 310 is d1. The test layout 300B also includes multiple test patterns 320 evenly distributed, and the distance between two adjacent test patterns 320 is d2. It can be seen from the figure that d1 is smaller than d2.

[0075] Since the constructed OPC model can be closer to the physical essence of the target light source, after calibration through the test layout 300A and the test layout 300B, the constructed OPC model can be applied to the mask whose distance between two adjacent test patterns is between d1 and d2, and this capability is called interpolation capability. In addition, the constructed OPC model can also be applied to the mask whose distance between two adjacent test patterns is less than d1 or greater than d2, and this capability is called extrapolation capability.

[0076] In some embodiments, the electronic device 110 may obtain corresponding image signals of each graphic in the target layout, the image signals indicating the shapes of each graphic in the target layout, and determine the signal simulation_signal=[new_optical]@layout of the OPC model based on the OPC model and the signals of each graphic in the target layout. Finally, the target layout is processed (e.g., corrected) based on the signal of the OPC model.

[0077] In summary, the OPC model for wide-screen light sources constructed in the above manner is closer to the physical essence, has better interpolation and extrapolation capabilities, and can more easily obtain better fitting results, thereby greatly improving the correction accuracy of OPC under OLED technology.

[0078] Figure 4 FIG. 4 is a flow chart showing a process 400 for OPC modeling according to some embodiments of the present disclosure. In some embodiments, the process 400 may be performed by: Figure 1 It should be understood that process 400 may also include additional blocks not shown and / or may omit one (or some) of the blocks shown, and the scope of the present disclosure is not limited in this respect. Figure 1 The process 400 is described in detail.

[0079] like Figure 4 As shown, in box 410, the electronic device 110 obtains multiple peak wavelengths of a target light source for lithography and multiple accumulated radiation power densities corresponding to the multiple peak wavelengths respectively. In box 420, the electronic device 110 determines multiple optical signals corresponding to the multiple peak wavelengths respectively, and the optical signals in the multiple optical signals indicate optical characteristics related to the wavelength. In box 430, the electronic device 110 determines a weighted average of the multiple optical signals based on the multiple accumulated radiation power densities and the multiple optical signals. In box 440, the electronic device 110 constructs an optical proximity correction OPC model based on the weighted average of the multiple optical signals.

[0080] In some embodiments, a weighted average of multiple optical signals is determined based on multiple cumulative radiation power densities and multiple optical signals, including: determining multiple weights corresponding to the multiple optical signals respectively based on the multiple cumulative radiation power densities; and determining the weighted average of the multiple optical signals based on the multiple optical signals and the multiple weights.

[0081] In some embodiments, determining multiple weights includes: obtaining multiple candidate wavelengths of the target light source and multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths respectively; and taking the ratio of the cumulative radiation power density corresponding to each of the multiple peak wavelengths to the sum of the multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths as multiple weights.

[0082] In some embodiments, obtaining multiple candidate wavelengths and multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths respectively includes: obtaining first association information of the target light source, the first association information indicating the relationship between the wavelength and the radiation power density of the target light source; based on the first association information, determining second association information, the second association information indicating the relationship between the wavelength and the cumulative radiation power density of the target light source; and based on the second association relationship, determining multiple candidate wavelengths and multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths respectively.

[0083] In some embodiments, obtaining multiple peak wavelengths and multiple cumulative radiation power densities corresponding to the multiple peak wavelengths respectively includes: selecting multiple cumulative radiation power densities greater than a second predetermined threshold from the multiple cumulative radiation power densities corresponding to the multiple candidate wavelengths respectively; determining the multiple candidate wavelengths corresponding to the selected multiple cumulative radiation power densities greater than the second predetermined threshold as multiple peak wavelengths; and determining the selected multiple cumulative radiation power densities greater than the second predetermined threshold as multiple cumulative radiation power densities corresponding to the multiple peak wavelengths respectively.

[0084] In some embodiments, the second predetermined threshold is determined based on a maximum cumulative radiation power density of the target light source, where the maximum cumulative radiation power density indicates the sum of all radiation power densities within a wavelength range corresponding to the target light source.

[0085] In some embodiments, process 400 also includes: acquiring corresponding image signals of each graphic in the target layout, the image signals indicating the shapes of each graphic in the target layout; determining the signal of the OPC model based on the OPC model and the signals of each graphic in the target layout; and processing the target layout based on the signal of the OPC model.

[0086] In some embodiments, the process 400 further includes: acquiring signals of each test pattern in the test layout; and calibrating the OPC model based on the signals of each test pattern in the test layout.

[0087] In some embodiments, the target light source is used in a photolithography process of an organic light emitting diode (OLED).

[0088] In some embodiments, constructing an optical proximity correction (OPC) model includes:

[0089]

[0090] in, For the OPC model, is the i-th candidate wavelength, is the cumulative radiation power density corresponding to the i-th candidate wavelength, is the jth peak wavelength, is the optical signal corresponding to the jth peak wavelength, is the cumulative radiation power density corresponding to the jth peak wavelength.

[0091] Figure 5 1 is a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. The electronic device 500 may be used to implement, for example, Figure 1 The electronic device 110 shown. It should be understood that Figure 5 The electronic device 500 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein.

[0092] like Figure 5 As shown, the electronic device 500 is in the form of a general electronic device. The components of the electronic device 500 may include, but are not limited to, one or more processors 510 or processing units, a memory 520, a storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing unit may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 520. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0093] The electronic device 500 typically includes a plurality of computer storage media. Such media may be any available media accessible to the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 may be a volatile memory (e.g., a register, a cache, a random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 may be a removable or non-removable medium, and may include a machine-readable medium, such as a flash drive, a disk, or any other medium, which may be capable of being used to store information and / or data (e.g., training data for training) and may be accessed within the electronic device 500.

[0094] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. The memory 520 may include a computer program product 525 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0095] The communication unit 540 enables communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0096] The input device 550 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 560 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 may also communicate with one or more external devices (not shown) through the communication unit 540 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 500, or communicate with any device (e.g., a network card, a modem, etc.) that allows the electronic device 500 to communicate with one or more other electronic devices. Such communication may be performed via an input / output (I / O) interface (not shown).

[0097] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the method described above.

[0098] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0099] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0100] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0101] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0102] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the marketplace, or to enable other persons of ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. A method for OPC modeling, characterized in that: include: Acquire a plurality of peak wavelengths of a target light source for photolithography and a plurality of accumulated radiation power densities respectively corresponding to the plurality of peak wavelengths; determining a plurality of optical signals corresponding to the plurality of peak wavelengths, respectively, an optical signal in the plurality of optical signals indicating an optical characteristic that is dependent on wavelength; determining a weighted average of the plurality of optical signals based on the plurality of accumulated radiation power densities and the plurality of optical signals; as well as An optical proximity correction (OPC) model is constructed based on a weighted average of the plurality of optical signals.

2. The method for OPC modeling according to claim 1, characterized in that: Determining a weighted average of the plurality of optical signals based on the plurality of accumulated radiation power densities and the plurality of optical signals comprises: Based on the multiple accumulated radiation power densities, determining multiple weights corresponding to the multiple optical signals respectively; Based on the plurality of optical signals and the plurality of weights, a weighted average of the plurality of optical signals is determined.

3. The method for OPC modeling according to claim 2, characterized in that: Determining the plurality of weights comprises: Acquire multiple candidate wavelengths of the target light source and multiple accumulated radiation power densities respectively corresponding to the multiple candidate wavelengths; and The ratios of the accumulated radiation power densities corresponding to the multiple peak wavelengths and the sums of the accumulated radiation power densities corresponding to the multiple candidate wavelengths are respectively used as the multiple weights.

4. The method for OPC modeling according to claim 3, characterized in that: Acquiring the multiple candidate wavelengths and multiple accumulated radiation power densities respectively corresponding to the multiple candidate wavelengths includes: Acquire first association information of the target light source, where the first association information indicates a relationship between a wavelength and a radiation power density of the target light source; Based on the first association information, determining second association information, the second association information indicating a relationship between a wavelength of the target light source and a cumulative radiation power density; and Based on the second association relationship, the plurality of candidate wavelengths and a plurality of accumulated radiation power densities respectively corresponding to the plurality of candidate wavelengths are determined.

5. The method for OPC modeling according to claim 3, characterized in that: Acquiring the multiple peak wavelengths and multiple accumulated radiation power densities respectively corresponding to the multiple peak wavelengths includes: Selecting a plurality of accumulated radiation power densities greater than a second predetermined threshold from a plurality of accumulated radiation power densities respectively corresponding to the plurality of candidate wavelengths; Determine a plurality of candidate wavelengths corresponding to the selected plurality of accumulated radiation power densities greater than the second predetermined threshold as the plurality of peak wavelengths; and The selected multiple accumulated radiation power densities greater than the second predetermined threshold are determined as multiple accumulated radiation power densities corresponding to the multiple peak wavelengths respectively.

6. The method for OPC modeling according to claim 5, characterized in that: The second predetermined threshold is determined based on a maximum cumulative radiation power density of the target light source, where the maximum cumulative radiation power density indicates the sum of all radiation power densities within a wavelength range corresponding to the target light source.

7. The method for OPC modeling according to claim 1, characterized in that: The method further comprises: Acquire corresponding image signals of each graphic in the target layout, where the image signals indicate the shapes of each graphic in the target layout; Determining a signal of the OPC model based on the OPC model and signals of each graphic in the target layout; and The target layout is processed based on the signal of the OPC model.

8. The method for OPC modeling according to claim 1, characterized in that: The method further comprises: Acquiring signals of each test pattern in the test layout; and The OPC model is calibrated based on the signals of each test pattern in the test layout.

9. The method for OPC modeling according to claim 1, characterized in that: The target light source is used in the photolithography process of the organic light emitting diode (OLED).

10. The method for OPC modeling according to claim 1, characterized in that: Building an optical proximity correction (OPC) model includes: in, For the OPC model, is the i-th candidate wavelength, is the cumulative radiation power density corresponding to the i-th candidate wavelength, is the jth peak wavelength, is the optical signal corresponding to the jth peak wavelength, is the cumulative radiation power density corresponding to the jth peak wavelength.

11. An electronic device, characterized in that: include: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 10 when executed by the at least one processing unit.

12. A computer-readable storage medium, characterized in that: A computer program is stored thereon, characterized in that the computer program can be executed by a processor to implement the method according to any one of claims 1 to 10.

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