Joint correction and inversion photolithography method, device and equipment based on digital micro-mirror gray modulation and medium

Abstract

This invention provides a joint correction inversion lithography method, apparatus, device, and medium based on digital micromirror grayscale modulation. The method includes: calling a pre-constructed forward model to process the grayscale array of a target pattern and a current DMD to obtain a simulated wafer surface exposure energy distribution and development profile, wherein the forward model consists of multiple sub-models; performing simulation based on the wafer surface exposure energy distribution and development profile output by the forward model to obtain a simulated exposure result including radial distortion and optical proximity effect; updating the grayscale array of the current DMD based on the difference between the simulated exposure result and the target pattern; repeating the above steps until a target grayscale array is obtained, wherein the difference between the simulated exposure result obtained based on the target grayscale array and the target pattern meets a preset requirement; and performing grayscale correction on the DMD based on the target grayscale array.

Description

Technical Field

[0001] This invention relates to the field of digital mask lithography technology, and in particular to a joint correction inversion lithography method, apparatus, equipment and medium based on digital micromirror grayscale modulation. Background Technology

[0002] Digital mask lithography (DMD) is an emerging technology derived from traditional optical lithography. Its main principle involves inputting the desired lithographic pattern into a DMD chip via software using a computer. The rotation angle and exposure time of the DMD chip's micromirrors are adjusted based on the distribution of grayscale pixels in the image. A collimated light source illuminates the DMD chip, forming a light image consistent with the desired pattern, which is then projected onto the substrate surface for imaging. Large-scale device fabrication can also be achieved by moving the workpiece stage. In maskless lithography based on DMD devices, the actual exposed pattern is simultaneously affected by optical proximity effect (OPE) and radial distortion of the projection system, leading to pattern distortion, including line tail shortening, corner rounding, line edge position offset, and overall geometric deformation over a large field of view. This results in a significant error between the final image and the target pattern. Existing correction methods generally involve adding or deleting pixels at edges and corners (which translates to switching on-off states on the DMD). These simple methods mostly only address localized OPE compensation and are difficult to achieve high-precision correction. The large distortion in regions far from the principal optical axis caused by global radial distortion in a large field of view is even more difficult to solve, making it difficult to achieve high-fidelity overall image transfer. Summary of the Invention

[0003] To address the aforementioned technical problems, embodiments of the present invention provide a joint correction and inversion lithography method based on digital micromirror grayscale modulation, comprising: The pre-built forward model is called to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. Based on the wafer surface exposure energy distribution and development profile output by the positive model, simulation is performed to obtain simulated exposure results including radial distortion and optical proximity effect; The grayscale array of the DMD is updated based on the difference between the simulated exposure result and the target image. Repeat the above steps until the target grayscale array is obtained. The difference between the simulated exposure result obtained based on the target grayscale array and the target image meets the preset requirements. The DMD is grayscale corrected based on the target grayscale array.

[0004] In one embodiment, the forward model is constructed as follows: The forward model is constructed based on the distortion model, imaging model, and photoresist model.

[0005] In one embodiment, the forward model includes a photoresist model, which is a differentiable model, and the photoresist model includes a Sigmoid model.

[0006] In one embodiment, updating the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image includes: The simulated exposure results are compared pixel by pixel with the target image to determine the differences; Based on the difference, the partial derivative of the gray value of each pixel in the DMD is calculated to obtain the gradient of each pixel. The grayscale array of the current DMD is updated in conjunction with the gradient.

[0007] In one embodiment, updating the grayscale array of the current DMD in conjunction with the gradient includes: The grayscale array of the DMD is updated based on the gradient and the Adam optimization algorithm.

[0008] In one embodiment, the Adam optimization algorithm includes:

[0009] in, (·) is the objective function. g k For the first k Variables during sub-optimization α k For the first k Step size or learning rate during the next optimization. For the Adam optimization algorithm in the th k The search or update direction given in the next iteration. The gradient of the objective function. Transpose symbol ，t It is an intermediate variable.

[0010] In one embodiment, it further includes:

[0011]

[0012]

[0013] in, g k+1 For the first k+ Variables during the first optimization, until gk+1 When the requirements are met, convert it into g output This is the final output grayscale vector. This indicates that each element is projected onto a reasonable range of 8-bit grayscale values. m k First-order momentum, v k It is a second-order momentum. x k It is the first k The parameter vector at the next iteration β 1, β 2 is the attenuation coefficient. To prevent small constants with a denominator of zero.

[0014] Another embodiment of the present invention also provides a joint correction inversion lithography apparatus based on digital micromirror grayscale modulation, comprising: The processing module is used to call a pre-built forward model to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. The simulation module is used to simulate the wafer surface exposure energy distribution and development profile based on the output of the forward model, and obtain the simulated exposure results including radial distortion and optical proximity effect. The update module is used to update the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image; An iterative module is used to repeat the above steps until a target grayscale array is obtained, and the difference between the simulated exposure result obtained based on the target grayscale array and the target image meets the preset requirements; A correction module is used to perform grayscale correction on the DMD based on the target grayscale array.

[0015] Another embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the joint correction inversion lithography method based on digital micromirror grayscale modulation as described in any one of the above descriptions.

[0016] Another embodiment of the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the joint correction inversion lithography method based on digital micromirror grayscale modulation as described above.

[0017] Based on the content disclosed in the above embodiments, the joint correction and inversion lithography method based on digital micromirror grayscale modulation in this application is applicable to DMD digital lithography systems and is used to achieve high-precision pattern inversion optimization. The method of this embodiment can predict and compensate for errors generated during the imaging process before exposure, thereby improving the fidelity of pattern transfer. Moreover, the method of this embodiment can invert and generate the corresponding grayscale pattern with the smallest difference between the exposure and development results and the target pattern, targeting local morphological deviations and global radial distortions caused by optical proximity effect and radial distortion of the projection system during DMD digital lithography. This facilitates pre-correction before actual exposure, reduces the time and cost consumption caused by repeated experiments, measurements and process debugging, reduces the input of human and material resources, and significantly improves the production efficiency and image fidelity of DMD digital lithography processing.

[0018] Other features and advantages of this application will be set forth in the following description. The objectives and other advantages of this application can be realized and obtained through the structures particularly pointed out in the written description and drawings.

[0019] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of the joint correction and inversion lithography method based on digital micromirror grayscale modulation in an embodiment of the present invention.

[0022] Figure 2 This is a schematic flowchart of the joint correction and inversion lithography method based on digital micromirror grayscale modulation in an application embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram showing the results in an application embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram comparing the simulation effect before and after optimization in an embodiment of the present invention.

[0025] Figure 5 This is a structural block diagram of the joint correction and inversion lithography apparatus based on digital micromirror grayscale modulation in an embodiment of the present invention. Detailed Implementation

[0026] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but these are not intended to limit the scope of the invention.

[0027] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope of this disclosure will be apparent to those skilled in the art.

[0028] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.

[0029] These and other features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0030] It should also be understood that although the invention has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention.

[0031] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0032] Specific embodiments of the present disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the present disclosure. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but are merely representative of the present disclosure and are intended to teach those skilled in the art to use the present disclosure in a variety of substantially any suitable detailed structures.

[0033] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.

[0034] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0035] like Figure 1 As shown, this embodiment of the invention provides a joint correction inversion lithography method based on digital micromirror grayscale modulation, comprising: S1: Call the pre-built forward model to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. S2: Based on the wafer surface exposure energy distribution and development profile output by the positive model, simulation is performed to obtain the simulated exposure results containing radial distortion and optical proximity effect; S3: Update the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image; S4: Repeat the above steps until the target grayscale array is obtained. The difference between the simulated exposure result obtained based on the target grayscale array and the target graphic meets the preset requirements. S5: Perform grayscale correction on the DMD based on the target grayscale array.

[0036] The forward model is constructed as follows: S6: Construct the forward model based on the distortion model, imaging model, and photoresist model.

[0037] For example, the distortion model considers the field-dependent geometric distortion introduced by a non-ideal projected optical system, and introduces a high-order radial distortion model in the object plane coordinate mapping. This distortion is expressed as an anisotropic polynomial function with respect to the radial distance relative to the optical axis, thereby allowing... x and y The distortion behaviors in two directions are independent of each other. Let ( , Let be the coordinates of an ideal object plane referenced to the optical axis, then the distorted coordinates ( , This can be represented as: ; .

[0038] K x Characterizing the distortion coefficient, r The radius is [0, 1].

[0039] The imaging model is a one-dimensional rectangular function, which can be expressed as the product of two one-dimensional rectangular functions. The reflection function of a single DMD micromirror can be accurately described by the difference between the two two-dimensional rectangular functions, as shown in equation (3). The Fourier transform of the rectangular function can be easily obtained, as shown in equation (4), where, L This indicates the side length of the outer edge of the microscope. l Indicates the side length of the internal hole. f x and f y They represent x direction and ySpatial frequency of the direction. Finally, the light intensity distribution on the image plane is calculated using the coherent imaging model shown in equation (5): (3) (4) (5) in m(x,y) It is the reflection function of the micromirror. F (·) represents the inverse Fourier transform of a certain function. F -1 (·) is the Fourier transform of a certain function. rect (·) is a rectangle function. sinc (·) is the sinc function. It is the pupil function in the frequency domain.

[0040] In this embodiment, the photoresist model is preferably a differentiable model, such as, but not limited to, a Sigmoid model.

[0041] The process of processing the target image and the grayscale array of the current DMD based on the aforementioned positive model is as follows: Figure 2 As shown, an initial digital mask is first generated based on the target graphic (target pattern), then the distortion mask is processed by the distortion model, and then processed by the optical imaging model to obtain the light intensity distribution describing the reflection function of the DMD micromirror and coherent imaging. Finally, the exposure energy distribution and development profile of the wafer surface are obtained by processing with the photoresist model.

[0042] Based on the above, this embodiment first pre-constructs a forward model including a distortion model, an imaging model, and a photoresist model. This model is capable of taking the target graphic and the initial grayscale array of the DMD as input, and outputting the simulated wafer surface exposure energy distribution and development profile. Next, the grayscale array of the target graphic and the current DMD, i.e., the grayscale value of each pixel, is determined and input into the forward model to obtain the wafer surface exposure energy distribution and development profile matching the current DMD pixel grayscale state. Then, based on the output of the forward model, the energy distribution and development result on the actual exposed wafer are calculated. For example, this could be a simulated exposure result including radial distortion and optical proximity effects, such as a development profile distortion map. Finally, based on the image error between the simulation result and the target graphic, the pixel grayscale values ​​of the current DMD are updated, i.e., the grayscale array of the current DMD is updated, rather than directly determining the pixel grayscale based on a simple binary switch. If the image error does not meet the requirements, the above steps (excluding the construction of the forward model) are repeated, or the above steps are repeated within a preset number of iterations, until a grayscale array whose image error meets the preset requirements is obtained, i.e., the target grayscale array. Finally, the grayscale values ​​of each pixel in the DMD can be corrected based on this target grayscale array. Based on the simulation results generated by the DMD processing after pixel grayscale correction, the error is minimized, and fast convergence can be achieved even under large field-of-view distortion and uneven local energy distribution, significantly reducing image error.

[0043] Based on the above, the joint correction and inversion lithography method based on digital micromirror grayscale modulation in this embodiment is applicable to DMD digital lithography systems and is used to achieve high-precision pattern inversion optimization. The method in this embodiment can predict and compensate for errors generated during the imaging process before exposure, thereby improving the fidelity of pattern transfer. Furthermore, the method in this embodiment can invert and generate a corresponding grayscale pattern with the smallest difference between the exposure and development results and the target pattern, addressing local topographic deviations and global radial distortions caused by optical proximity effects and radial distortion of the projection system during DMD digital lithography. This facilitates pre-correction before actual exposure, reducing the time and cost consumption caused by repeated experiments, measurements, and process adjustments, lowering manpower and material resources, and significantly improving the production efficiency and image fidelity of DMD digital lithography processing.

[0044] The radial distortion and optical proximity effect are respectively: Traditional Optical Proximity Correction (OPC) methods primarily compensate for optical proximity effects. The basic idea is to correct the boundaries of the target image, especially at corners, before exposure by adding or removing pixels.

[0045] Radial distortion correction method: The distortion of the projection system is calibrated, and the input image is pre-distorted according to the distortion mapping relationship. That is, the transformation relationship between the ideal coordinates and the actual imaging coordinates is first obtained through calibration, and then the image to be exposed is subjected to inverse geometric transformation at the DMD end so that it can be restored to a shape close to the ideal shape after passing through the projection system.

[0046] Furthermore, after obtaining the output of the forward model, it is calculated and simulated. The simulation results can be used as a reference. Figure 3 As shown, Figure 3 (a) is the original DMD micromirror array. Figure 3 (b) shows the energy distribution on the imaging plane. Figure 3 (c) shows the development result on the target imaging surface of the wafer, where obvious distortion can be seen.

[0047] After obtaining the simulation results, in one embodiment, updating the grayscale array of the current DMD based on the difference between the simulated exposure results and the target image includes: S301: Compare the simulated exposure result with the target image pixel by pixel to determine the difference; S302: Based on the difference, calculate the partial derivative of the gray value of each pixel in the DMD to obtain the gradient of each pixel; S303: Update the grayscale array of the current DMD in conjunction with the gradient.

[0048] The step of updating the grayscale array of the current DMD in conjunction with the gradient includes: S304: Update the grayscale array of the current DMD based on the gradient and the Adam optimization algorithm.

[0049] For example, the simulated outline is compared pixel by pixel with the target image to calculate the difference between them. This difference can be calculated using methods such as, but not limited to, mean squared error (MSE) or cross-entropy loss functions. Next, using the chain rule, the partial derivative of the image error with respect to the grayscale value of each DMD pixel is calculated to obtain the gradient for each pixel in the DMD. This gradient indicates how the grayscale value of each pixel should be adjusted (increased or decreased) to reduce the error between the final image and the target. Then, the Adam optimization algorithm, as described above, is used to update the grayscale value of each pixel, truly achieving sub-pixel-level error compensation. The Adam algorithm described in this embodiment combines momentum (accelerating convergence) and RMSProp (adaptive learning rate), making it particularly suitable for high-dimensional, non-convex optimization problems, such as the grayscale distribution of a DMD with tens of thousands of pixels. Furthermore, when applying Adam optimization, it can simultaneously apply to all pixels, i.e., optimize all pixels synchronously, thus enabling the joint handling of global radial distortion (large field-of-view geometric deformation) and local optical proximity effects (edge ​​rounding, line tail shortening, etc.). After the update is complete, the updated grayscale values ​​are reorganized into a two-dimensional array with the same size as the DMD micromirror array. Ensure the updated grayscale values ​​remain within the valid range. Comparison images before and after optimization are provided, such as... Figure 4 As shown, Figure 4 The left column shows the energy distribution and development results of the unoptimized original micromirror array after exposure, as well as a comparison between the developed result and the target pattern (image error). The right column shows the energy distribution, development results, and image error of the micromirror grayscale array obtained after inversion optimization. According to this figure, the image error is significantly reduced.

[0050] Specifically, in actual calculations, the following are included: , G Grayscale pattern representing DMD micromirror array; Using a differentiable sigmoid development model, the normalized intensity is... Mapped to soft development results, such as , D It is a soft-developed pattern, where each pixel takes a value in (0,1). It refers to steepness.

[0051] To obtain a differentiable development approximation, this embodiment uses the Sigmoid function: ; Let the error err = D Then the objective function can be written as:

[0052] in, min Represents the minimum value. For loss function, Number of pixels Indicates the number of micromirrors within the window. , is a grayscale matrix The vectorized form, For regularization weights, It is a norm. The Adam optimization algorithm in this embodiment applies the backpropagation mechanism, which provides an efficient adjoint derivative mechanism for nonlinear forward models, making nonlinear optimization possible in high-dimensional parameter spaces, such as 8-bit DMD grayscale matrices. The loss function affects the developing output. The gradient is: .

[0053] Loss gradient with respect to the complex field of the imaging surface g image It can be written as:

[0054] Here, ⊙ represents element-wise multiplication, such as the Hadamard product. I =∣ E image | 2 For strength, It is the loss versus strength I The gradient. E image It is the complex electric field / complex amplitude field on the imaging surface.

[0055] Furthermore, This makes the imaging operator Then we can get:

[0056]

[0057] in It is the conjugate transpose. W This refers to the distortion / resampling operator. g distortion It is the gradient of the loss with respect to the distorted reconstructed field. g object It is the gradient of the loss with respect to the complex field of the ideal object. U object It is the ideal object re-enactment. U distortion It is a re-field after distortion. U image It is like a multi-field image.

[0058] Based on the above, we obtain the Taylor expansion of the Adam method written using the ordinary gradient descent method:

[0059] in, (·) is the objective function. g k For the first k Variables during sub-optimization α k For the first k Step size or learning rate during the next optimization. For the Adam optimization algorithm in the th k The search or update direction given in the next iteration. The gradient of the objective function. Transpose symbol ，t This is an intermediate variable, representing a positional parameter located between the current iteration point and the update point.

[0060] Also includes:

[0061]

[0062]

[0063] in, g k+1 For the first k+ Variables during the first optimization, until g k+1 When the requirements are met, convert it into g output This is the final output grayscale vector. This indicates that each element is projected onto a reasonable range of 8-bit grayscale values. m k First-order momentum, v k It is a second-order momentum. x k It is the first k The parameter vector at the next iteration β 1, β 2 is the attenuation coefficient. To prevent small constants with a denominator of zero.

[0064] Multiple digital experiments revealed that even for edge regions far from the main optical axis, good convergence can be achieved after 100-120 iterations of optimization, and image errors can be reduced by more than 90%. On an AMD EPYC 9654 processor (2.4GHz, 192 cores) and 768 GB RAM computing platform, the inversion optimization correction of an entire DMD chip range can be completed in less than an hour. The inversion optimization algorithm proposed in this embodiment can also quickly identify and skip positions with small distortions, further saving time.

[0065] After multiple iterations, such as 50-100 iterations of optimization, the error is basically stable and can achieve good convergence. The optimization results show that they can be stably maintained at a relatively small image error rate level. At this point, the optimal target grayscale array can be obtained.

[0066] Therefore, the method proposed in this embodiment takes into account both global radial distortion and local optical proximity effect distortion, has high accuracy and good robustness, and is acceptable in terms of computation time. It is a reliable and practical inversion lithography algorithm for DMD grayscale lithography.

[0067] like Figure 5 As shown, another embodiment of the present invention also provides a joint correction and inversion lithography apparatus based on digital micromirror grayscale modulation, comprising: The processing module is used to call a pre-built forward model to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. The simulation module is used to simulate the wafer surface exposure energy distribution and development profile based on the output of the forward model, and obtain the simulated exposure results including radial distortion and optical proximity effect. The update module is used to update the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image; An iterative module is used to repeat the above steps until a target grayscale array is obtained, and the difference between the simulated exposure result obtained based on the target grayscale array and the target image meets the preset requirements; A correction module is used to perform grayscale correction on the DMD based on the target grayscale array.

[0068] In one embodiment, the forward model is constructed as follows: The forward model is constructed based on the distortion model, imaging model, and photoresist model.

[0069] In one embodiment, the forward model includes a photoresist model, which is a differentiable model, and the photoresist model includes a Sigmoid model.

[0070] In one embodiment, updating the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image includes: The simulated exposure results are compared pixel by pixel with the target image to determine the differences; Based on the difference, the partial derivative of the gray value of each pixel in the DMD is calculated to obtain the gradient of each pixel. The grayscale array of the current DMD is updated in conjunction with the gradient.

[0071] In one embodiment, updating the grayscale array of the current DMD in conjunction with the gradient includes: The grayscale array of the DMD is updated based on the gradient and the Adam optimization algorithm.

[0072] In one embodiment, the Adam optimization algorithm includes:

[0073] in, (·) is the objective function. g k For the first k Variables during sub-optimization α k For the first k Step size or learning rate during the next optimization. For the Adam optimization algorithm in the th k The search or update direction given in the next iteration. The gradient of the objective function. Transpose symbol ，t It is an intermediate variable.

[0074] In one embodiment, it further includes:

[0075]

[0076]

[0077] in, g k+1 For the first k+ Variables during the first optimization, until g k+1 When the requirements are met, convert it into g output This is the final output grayscale vector. This indicates that each element is projected onto a reasonable range of 8-bit grayscale values. m k First-order momentum,v k It is a second-order momentum. x k It is the first k The parameter vector at the next iteration β 1, β 2 is the attenuation coefficient. To prevent small constants with a denominator of zero.

[0078] Another embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the joint correction inversion lithography method based on digital micromirror grayscale modulation as described in any one of the above descriptions.

[0079] Furthermore, one embodiment of the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the joint correction inversion lithography method based on digital micromirror grayscale modulation as described above. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above-described method embodiments, and will not be repeated here.

[0080] Furthermore, embodiments of the present invention also provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions that, when executed, cause at least one processor to perform a joint correction inversion lithography method based on digital micromirror grayscale modulation, as described in the embodiments above.

[0081] It should be noted that the computer storage medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.

[0082] Furthermore, those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

Claims

1. A combined correction inverse photolithography method based on digital micro-mirror gray scale modulation, characterized in that, include: The pre-built forward model is called to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. Based on the wafer surface exposure energy distribution and development profile output by the positive model, simulation is performed to obtain simulated exposure results including radial distortion and optical proximity effect; The grayscale array of the DMD is updated based on the difference between the simulated exposure result and the target image. Repeat the above steps until the target grayscale array is obtained. The difference between the simulated exposure result obtained based on the target grayscale array and the target image meets the preset requirements. The DMD is grayscale corrected based on the target grayscale array.

2. The joint correction and inversion lithography method based on digital micromirror grayscale modulation according to claim 1, characterized in that, Construct the aforementioned forward model: The forward model is constructed based on the distortion model, imaging model, and photoresist model.

3. The combined correction and reverse photolithography method based on digital micro-mirror gray modulation according to claim 1, characterized in that, The forward model includes a photoresist model, which is a differentiable model, and the photoresist model includes a Sigmoid model.

4. The combined correction and reverse photolithography method based on digital micro-mirror gray modulation according to claim 3, characterized in that, The step of updating the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image includes: The simulated exposure results are compared pixel by pixel with the target image to determine the differences; Based on the difference, the partial derivative of the gray value of each pixel in the DMD is calculated to obtain the gradient of each pixel. The grayscale array of the current DMD is updated in conjunction with the gradient.

5. The combined correction and inverse photolithography method based on digital micro-mirror gray modulation according to claim 4, characterized in that, The step of updating the grayscale array of the current DMD in conjunction with the gradient includes: The grayscale array of the DMD is updated based on the gradient and the Adam optimization algorithm.

6. The combined correction and inverse photolithography method based on digital micro-mirror gray modulation according to claim 5, characterized in that, The Adam optimization algorithm includes: in, (·) is the objective function. g k For the first k Variables during sub-optimization α k For the first k Step size or learning rate during the next optimization. For the Adam optimization algorithm in the th k The search or update direction given in the next iteration. The gradient of the objective function. Transpose symbol ，t It is an intermediate variable.

7. The combined correction and inverse photolithography method based on digital micro-mirror gray modulation according to claim 6, characterized in that, Also includes: in, g k+1 For the first k+ Variables during the first optimization, until g k+1 When the requirements are met, convert it into g output This is the final output grayscale vector. This indicates that each element is projected onto a reasonable range of 8-bit grayscale values. m k First-order momentum, v k It is a second-order momentum. x k It is the first k The parameter vector at the next iteration β 1, β 2 is the attenuation coefficient. To prevent small constants with a denominator of zero.

8. A combined correction inverse photolithography device based on digital micro-mirror gray scale modulation, characterized in that, include: The processing module is used to call a pre-built forward model to process the target image and the grayscale array of the current DMD to obtain the simulated wafer surface exposure energy distribution and development profile. The forward model consists of multiple sub-models. The simulation module is used to simulate the wafer surface exposure energy distribution and development profile based on the output of the forward model, and obtain the simulated exposure results including radial distortion and optical proximity effect. The update module is used to update the grayscale array of the current DMD based on the difference between the simulated exposure result and the target image; An iterative module is used to repeat the above steps until a target grayscale array is obtained, and the difference between the simulated exposure result obtained based on the target grayscale array and the target image meets the preset requirements; A correction module is used to perform grayscale correction on the DMD based on the target grayscale array.

9. An electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the joint correction inversion lithography method based on digital micromirror grayscale modulation as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, which, when executed by a processor, implements the joint correction inversion lithography method based on digital micromirror grayscale modulation as described in any one of claims 1-7.

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