Method for improving DMD digital photoetching smoothness based on curvature dynamic fuzzy exposure technology

Abstract

The invention relates to the technical field of DMD device imaging exposure, in particular to a method for improving DMD digital photoetching smoothness based on a curvature dynamic fuzzy exposure technology, which comprises the following steps: selecting an exposure image to be processed; segmenting the exposure image to be processed to obtain a plurality of sub-images; calculating the slope of the sub-image, and designing a displacement path based on the slope; calculating local expansion of the sub-image based on the displacement path, and preprocessing the sub-image; and exposing the pre-processed sub-images to obtain a complete exposed image. According to the invention, a high-fidelity complex image can be effectively exposed.

Description

Technical Field

[0001] The invention relates to the technical field of DMD device imaging exposure, and in particular to a method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology. Background Art

[0002] As a core technology in the field of semiconductor processing, DMD digital lithography technology replaces traditional optical masks based on its low cost, high efficiency, and lightweight characteristics, and is widely used in various fields such as industrial printed circuit board (PCB) manufacturing, tissue engineering, biomedicine, and 3D printing. For example, DMD digital lithography technology is combined with magnetic materials to produce micro-medical robots that can enter the human body for micro-surgery through magnetic field control; the essence of DMD is composed of a square micro-mirror array. This microstructure causes the edges of the exposed microstructures in digital lithography to be jagged, which greatly reduces the image edge accuracy and cannot meet the high-precision manufacturing requirements of micro-devices.

[0003] In order to solve this problem, the conventional method reduces the magnification of the projection objective lens and improves the optical resolution, but greatly reduces the exposure area and is extremely inefficient. Gao Yiqing et al. reduced the DMD pixel error by designing the characteristic size of the mask to be an integer multiple of the DMD pixel, but this method is limited by the design and processing of the microstructure slice layer and is not very flexible in processing. The time-space modulation technology proposed by Liu Hua et al. improves the smoothness of the image edge by overlapping multiple sub-pixels, but a large number of sub-images need to be produced. Grayscale exposure imaging technology improves the smoothness of the image edge by controlling the line width, but requires a lot of calculations, has high requirements on equipment, and is costly. It can be seen that the existing technology for improving the smoothness of DMD digital lithography is difficult to meet the current needs in high-precision systems. Therefore, the present invention proposes a method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology. Summary of the invention

[0004] The purpose of the present invention is to provide a method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology in order to solve the above technical problems, so as to achieve high-quality optimization of the edge of the exposure pattern.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology, comprising:

[0007] Select the exposure image to be processed;

[0008] Segmenting the exposure image to be processed to obtain a plurality of sub-images;

[0009] Calculating the slope of the sub-image, and designing a displacement path based on the slope;

[0010] Calculating a local expansion of the sub-image based on the displacement path, and preprocessing the sub-image;

[0011] The preprocessed sub-image is exposed to obtain a complete exposure image.

[0012] Optionally, selecting the exposure image to be processed includes:

[0013] The DMD lithography system is debugged to a working state so that the ultraviolet light source can sequentially pass through the engineering scattering plate, lens group, DMD chip, reflector, and projection objective in the DMD lithography system to expose the target image on the photosensitive material on the three-dimensional nano-mobile platform to obtain the exposure image to be processed.

[0014] Optionally, segmenting the exposure image to be processed to obtain a plurality of sub-images includes:

[0015] The exposure image to be processed is segmented by an image curvature processing algorithm to obtain sub-images with different curvatures.

[0016] Optionally, calculating the slope of the sub-image includes:

[0017] The sub-image is fitted into a straight line by the least square method to obtain the slope.

[0018] Optionally, designing a displacement path based on the slope includes:

[0019] The moving path of the three-dimensional nano mobile platform is designed based on the slope a of the sub-image. When a is greater than 1 or less than -1, the displacement distance on the x-axis is 1 / 2 pixel distance, and the y-axis moves a / 2 pixel distance; when a is greater than -1 and less than 1, the displacement distance on the y-axis is 1 / 2 pixel distance, and the x-axis moves a / 2 pixel distance; when a is equal to 1 or -1, the displacement distances of the x-axis and y-axis are both 1 / 2 pixel distance.

[0020] Optionally, a motion blur kernel function is used to calculate the local dilation of the sub-image based on the displacement path.

[0021] Optionally, preprocessing the sub-image includes:

[0022] The Python image processing algorithm is used to perform edge pixel elimination processing on the sub-image.

[0023] Optionally, exposing the preprocessed sub-image includes:

[0024] The preprocessed sub-image is exposed using a simulation exposure model, wherein the simulation exposure model is a Hopkins exposure model.

[0025] Optionally, exposing the preprocessed sub-image further includes:

[0026] The preprocessed sub-images are exposed in sequence according to the curvature, and the preprocessed sub-images with the same curvature are exposed at the same time.

[0027] The beneficial effects of the present invention are:

[0028] The present invention analyzes and segments the image edges through curve fitting, calculates the motion trajectory of the three-dimensional nano mobile platform; uses the dynamic fuzzy kernel function to calculate the local expansion size and process the exposure of complex images; and by making full use of the flexibility of the DMD mode and the high precision of the three-dimensional nano mobile platform, not only the smoothness of the edges of the complex images is optimized, but also the line width is guaranteed, so that high-fidelity complex images can be effectively exposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A flow chart of a method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of an experimental device according to an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of image curvature segmentation according to an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of sub-image preprocessing according to an embodiment of the present invention;

[0034] Figure 5 A schematic diagram of the displacement of a three-dimensional nano mobile platform according to an embodiment of the present invention;

[0035] Figure 6 An exposure energy simulation diagram of an embodiment of the present invention;

[0036] Figure 7 This is a Python simulation comparison diagram before and after optimization of an embodiment of the present invention;

[0037] Figure 8It is a comparison diagram of experimental lithography effects before and after optimization of an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] This embodiment provides a method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology, such as Figure 1 As shown, including:

[0041] Selecting an exposure image to be processed; segmenting the exposure image to be processed to obtain a plurality of sub-images; calculating the slope of the sub-image, and designing a displacement path based on the slope; calculating the local expansion of the sub-image based on the displacement path, and preprocessing the sub-image; exposing the preprocessed sub-image to obtain a complete exposure image.

[0042] Further, selecting the exposure image to be processed includes:

[0043] The DMD lithography system is debugged to a working state so that the ultraviolet light source can sequentially pass through the engineering scattering plate, lens group, DMD chip, reflector, and projection objective in the DMD lithography system to expose the target image on the photosensitive material on the three-dimensional nano-mobile platform to obtain the exposure image to be processed.

[0044] Specifically, Figure 2As shown, after the ultraviolet light source (LUMINUS UV-LED, CBT-39-UV-C32-FB400-22) is turned on, it is projected onto the DMD through the lens group with a resolution of 1920×1080 (pixel size is 7.56μm). The typical peak wavelength of this light source is 405nm, but the beam divergence angle is large and the light intensity is Gaussian distribution. When the light source reaches the subsequent part of the DMD lithography system, a large amount of light energy will be lost. In order to make the DMD lithography system have a higher light source utilization rate, it is usually necessary to add a better lens group to reduce the loss of light energy. The lens group of the DMD lithography system of this embodiment adopts an engineering scatterer (ED1-C20-MD, Thorlabs, USA) and a lens (1 plano-convex lens and 3 biconvex lenses are placed in sequence). The DMD controller model is DLP6500 (Texas Instruments, Texas, USA). The beam from the photomask was precisely exposed to the photosensitive resin (W200 water-washable resin, eSUN, Shenzhen, China) on a three-dimensional nanopositioning stage (cormorrow, XYZ100, E70. D3S closed-loop piezoelectric controller).

[0045] Place a clean glass slide on a coating machine, use a micropipette to drop an appropriate amount of photoresist resin on the silicon substrate, set the speed and time of the coating machine, and start coating.

[0046] In this embodiment, the DMD lithography system is debugged to a working state, so that the ultraviolet light source can accurately expose the required exposure image on the photosensitive material on the three-dimensional nano-mobile platform through the engineered scattering plate and lens group, DMD chip, reflector, and projection objective lens in the DMD system.

[0047] As an optional embodiment, the three-dimensional nano mobile platform is a piezoelectric nano ceramic mobile platform.

[0048] Furthermore, segmenting the exposure image to be processed to obtain a plurality of sub-images includes:

[0049] The exposure image to be processed is segmented by an image curvature processing algorithm to obtain sub-images with different curvatures.

[0050] Further, calculating the slope of the sub-image includes:

[0051] The sub-image is fitted into a straight line by the least square method to obtain the slope.

[0052] Further, designing a displacement path based on the slope includes:

[0053] The moving path of the three-dimensional nano mobile platform is designed based on the slope a of the sub-image. When a is greater than 1 or less than -1, the displacement distance on the x-axis is 1 / 2 pixel distance, and the y-axis moves a / 2 pixel distance; when a is greater than -1 and less than 1, the displacement distance on the y-axis is 1 / 2 pixel distance, and the x-axis moves a / 2 pixel distance.

[0054] In this embodiment, the required exposure image is divided into multiple sub-images by an image curvature processing algorithm, and a straight line is fitted according to the least square method to obtain its slope, and the relative movement path between the three-dimensional nano-mobile platform and the exposure objective lens is designed, which specifically includes:

[0055] (1) Segment the exposure image using the image curvature processing algorithm to obtain sub-images with different curvatures, such as Figure 3 As shown;

[0056] (2) Fit each sub-image by the least squares method to obtain its slope. The least squares method is expressed as:

[0057]

[0058] The optimal estimates of parameters a and b are obtained by differentiation:

[0059]

[0060] (3) Determine the moving path of the three-dimensional nano mobile platform according to the slope a of the sub-image. When a is greater than 1 or less than -1, the displacement distance on the x-axis is 1 / 2 pixel distance, and the y-axis moves a / 2 pixel distance; when a is greater than -1 and less than 1, the displacement distance on the y-axis is 1 / 2 pixel distance, and the x-axis moves a / 2 pixel distance. When a is equal to 1 or -1, the displacement distances on the x- and y-axes are both 1 / 2 pixel distances. Figure 5 shown.

[0061] As an optional embodiment, the distance of one pixel is 1.89 microns.

[0062] Furthermore, a motion blur kernel function is used to calculate the local expansion of the sub-image based on the displacement path.

[0063] Further, preprocessing the sub-image includes:

[0064] The Python image processing algorithm is used to perform edge pixel elimination processing on the sub-image.

[0065] This embodiment uses a motion blur kernel function to calculate local expansion, and then uses an image processing algorithm to pre-process the sub-image to eliminate edge pixels, specifically including:

[0066] (1) Determine the local expansion size, and calculate the local expansion size based on the motion trajectory and the motion blur kernel function;

[0067] The direction and distance of the blur can be expressed as a two-dimensional vector (x, y) and can be calculated as follows:

[0068] length = sqrt(x 2 +y 2 );

[0069] Construct the matrix [kernel_size, kernel_size] and set the cells within the length range around the center of the matrix;

[0070] Each element M[i,j] in the matrix is ​​located at the center of the matrix and is less than or equal to the length from the center. The mathematical formula can be expressed as follows:

[0071]

[0072] M[i,j]=0;

[0073] The normalized matrix can be expressed as:

[0074]

[0075] Where M is the blur kernel matrix, i and j are the indices of the elements in the matrix, length is the length of the motion blur, and sum(M) is the sum of all elements in the matrix.

[0076] The locality of the blur effect indicates the local degree of the blur effect, that is, the ratio of the blur kernel size to the blur length multiplied by the blur kernel range in the image. The larger the value, the more local the blur effect. The local effect produced in the actual exposure can be expressed as:

[0077]

[0078] A = rf;

[0079] Among them, r represents the ratio of image magnification, A represents the amount of image magnification, v represents the speed of target or object movement, T represents the exposure time, and f represents the focal length of the object.

[0080] (2) Use Python image processing algorithm to remove edge pixels from the sub-image to obtain a pre-processed sub-image, such as Figure 4 shown.

[0081] Further, exposing the preprocessed sub-image includes:

[0082] The preprocessed sub-image is exposed using a simulation exposure model, wherein the simulation exposure model is a Hopkins exposure model.

[0083] Furthermore, exposing the preprocessed sub-image further comprises:

[0084] The preprocessed sub-images are exposed in sequence according to the curvature, and the preprocessed sub-images with the same curvature are exposed at the same time.

[0085] Specifically, this embodiment exposes the preprocessed sub-images in sequence to obtain a complete exposure image, which can effectively optimize the edge of the exposure image with high quality. The processing process includes the following contents:

[0086] Create Python code through the dynamic blur kernel function, import the preprocessed sub-image, set the displacement data, and obtain the exposure energy simulation image after the dynamic blur effect, such as Figure 6 As shown;

[0087] The simulation exposure model is the Hopkins exposure model. The Hopkins exposure model is a mathematical model based on Fourier transform and can be expressed as:

[0088] When performing lithography simulation, Fourier transform is used to analyze the frequency response of the mask and light source. Given a function, its one-dimensional continuous Fourier transform is defined as:

[0089]

[0090] Where f(x) is the function in the spatial domain; F(f) is the function in the frequency domain; x is the spatial variable; f is the frequency variable.

[0091] For a two-dimensional image, such as the mask image in DMD digital lithography simulation, its two-dimensional Fourier transform is defined as:

[0092]

[0093] Where f(x,y) is the image in the two-dimensional space domain; F(f x +f y ) is a two-dimensional frequency domain image; (x, y) is a two-dimensional spatial coordinate; (f x ,f y ) is the two-dimensional frequency coordinate.

[0094] The transfer function describes the changes in light after it passes through the mask. For Hopkins simulation, the transfer function is:

[0095]

[0096] λ is the wavelength of light; z is the distance from the light source to the object; f x ,f y are frequency coordinates; n is the refractive index; j is the imaginary unit.

[0097] The Hopkins equation describes the image intensity during the photolithography process and, for a simplified scenario, can be expressed as:

[0098] I(x,y)=F -1 {O(u,v)H(u,v)} 2 ;

[0099] Where I(x, y) is the image intensity on the wafer plane; O(u, v) is the Fourier transform of the target function on the mask plane; H(u, v) is the transfer function; F -1 is the inverse Fourier transform operation; (x, y) is the two-dimensional space coordinate; (u, v) is the two-dimensional frequency coordinate.

[0100] Import the energy simulation subgraph into the Hopkins Python exposure model, obtain the optimized exposure simulation results, and compare them with the exposure simulation results before optimization, see Figure 7 ,Through the results of simulation experiments, we can know that the curvature blur dynamic ,exposure technology has an excellent optimization effect on the edges of ,complex graphics exposure.

[0101] The photosensitive resin and substrate are placed on a moving platform by the immersion method. Each sub-image is imported into the DMD controller and the time is set. The displacement data obtained according to each sub-image is imported into the moving platform, the time is set, and the sub-image is consistent with the data sequence. When the light source is activated, the light beam shines on the photosensitive resin, the platform starts to move according to the imported data, and the sub-images are exposed in sequence. When the light intensity is 8.11mw / cm2, the exposure time of the sub-image is about 6-7 seconds. After the exposure of each sub-image, the DMD and the moving platform will pause for a short time to continue the process when the next sub-image and displacement data are ready. After all the sub-images are exposed, the optimized exposure experimental image is obtained and compared with the exposure experimental image before optimization, see Figure 8In order to evaluate the optimization effect of this method, three different image exposure points were selected in the experiment to measure the line width. The line width measurements before optimization were recorded as 57.75μm, 37.00μm and 30.25μm. After optimization, these numbers were refined to 57.62μm, 36.90μm and 30.24μm, respectively. This resulted in a line width error rate of only ±0.27%, proving the accuracy of the curvature blurred dynamic exposure technology. In addition, in order to quantify the jagged defects, three measurement positions were selected again. Before optimization, the jagged defects were very obvious, at 1.63μm, 1.43μm and 1.46μm, respectively, with an average of 1.5μm. After optimization by applying the curvature blurred dynamic exposure technology, these numbers were significantly reduced to 0.22μm, 0.28μm and 0.14μm, with an average defect size of only 0.21μm. Comparative analysis of images and data shows that the proposed method not only effectively alleviates edge defects in complex graphics, but also significantly reduces the impact of dynamic blur on line width accuracy. This progress proves that the proposed method can improve the fidelity and quality of the lithography process.

[0102] Specifically, this embodiment analyzes and segments the image edges through curve fitting, calculates the motion trajectory of the three-dimensional nano-mobile platform; uses the dynamic fuzzy kernel function to calculate the local expansion size and process the exposure of complex images; and by making full use of the flexibility of the DMD mode and the high precision of the three-dimensional nano-mobile platform, not only the smoothness of the edges of the complex image is optimized, but also the line width is guaranteed, so that high-fidelity complex images can be effectively exposed.

[0103] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology, characterized in that: include: Select the exposure image to be processed; Segmenting the exposure image to be processed to obtain a plurality of sub-images; Calculating the slope of the sub-image, and designing a displacement path based on the slope; Calculating a local expansion of the sub-image based on the displacement path, and preprocessing the sub-image; The preprocessed sub-image is exposed to obtain a complete exposure image.

2. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Selecting the exposure image to be processed includes: The DMD lithography system is debugged to a working state so that the ultraviolet light source can sequentially pass through the engineering scattering plate, lens group, DMD chip, reflector, and projection objective in the DMD lithography system to expose the target image on the photosensitive material on the three-dimensional nano-mobile platform to obtain the exposure image to be processed.

3. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Segmenting the exposure image to be processed to obtain a plurality of sub-images includes: The exposure image to be processed is segmented by an image curvature processing algorithm to obtain sub-images with different curvatures.

4. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Calculating the slope of the sub-image includes: The sub-image is fitted into a straight line by the least square method to obtain the slope.

5. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Designing a displacement path based on the slope includes: The moving path of the three-dimensional nano mobile platform is designed based on the slope a of the sub-image. When a is greater than 1 or less than -1, the displacement distance on the x-axis is 1 / 2 pixel distance, and the y-axis moves a / 2 pixel distance; when a is greater than -1 and less than 1, the displacement distance on the y-axis is 1 / 2 pixel distance, and the x-axis moves a / 2 pixel distance; when a is equal to 1 or -1, the displacement distances of the x-axis and y-axis are both 1 / 2 pixel distance.

6. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: The local dilation of the sub-image is calculated based on the displacement path using a motion blur kernel function.

7. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Preprocessing the sub-image includes: The Python image processing algorithm is used to perform edge pixel elimination processing on the sub-image.

8. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 1, characterized in that: Exposing the preprocessed sub-image includes: The preprocessed sub-image is exposed using a simulation exposure model, wherein the simulation exposure model is a Hopkins exposure model.

9. The method for improving the smoothness of DMD digital lithography based on curvature dynamic blur exposure technology according to claim 8, characterized in that: Exposing the preprocessed sub-image further includes: The preprocessed sub-images are exposed in sequence according to the curvature, and the preprocessed sub-images with the same curvature are exposed at the same time.

Images