Photolithography simulation method, simulation device and adjustment method for photoresist with micron-level thickness

Abstract

The application provides a photoetching simulation method, a simulation device and an adjusting method for a photoetching glue with a micron level thickness, and the photoetching simulation method comprises the following steps: obtaining photoetching simulation parameters, which comprise exposure process parameters, baking process parameters and developing process parameters; based on the exposure process parameters, a Dill model is used to simulate exposure to obtain the distribution of photosensitive mixtures in the photoetching glue after exposure, and the calculation process comprises discretizing the exposure dose according to the exposure time to calculate the distribution of the photosensitive mixtures at different thicknesses in the photoetching glue; based on the baking process parameters, the photoetching glue after exposure is simulated to bake to obtain the distribution of the photosensitive mixtures in the photoetching glue after baking; and based on the developing process parameters, the photoetching glue after baking is simulated to develop to obtain the size data of the photoetching glue after development. The application simulates the process from the whole photoetching process, can realize more accurate key size simulation prediction, and can also provide more debugging schemes.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and in particular to a photolithography simulation method, simulation device, and adjustment method for photoresist with a thickness of micrometers. Background Technology

[0002] Moore's Law is nearing its limits, with feature sizes approaching physical boundaries. 2.5D and 3D advanced packaging technologies continue to meet the demands for low power consumption, high performance, and high density in integrated circuits. Among these, multilayer redistribution (RDL) technology is a crucial high-density interconnect technology in advanced packaging, and thick-film photoresist (micron-level photoresist) lithography is the most important step in realizing multilayer RDLs. The more layers there are, the more complex the thick-film photoresist lithography becomes, and the more severe the impact of differences in mask patterns and process conditions on the final pattern.

[0003] Currently, simulations of micron-thick photoresists can mostly only predict the trend of pattern changes under the same process conditions, and cannot achieve accurate simulation prediction of key dimensions. In particular, it is difficult to simulate and predict the 3D morphology data of the photoresist, which makes thick photoresist lithography too reliant on on-site debugging, wasting a lot of time and cost of experimental exploration. Summary of the Invention

[0004] The purpose of this invention is to provide a photolithography simulation method, simulation device, and adjustment method for micron-thickness photoresist, which can be used to achieve more accurate simulation and prediction of key dimensions.

[0005] To solve the above-mentioned technical problems, the present invention provides a photolithography simulation method for micron-thickness photoresist, comprising:

[0006] Obtain the photolithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters;

[0007] Based on the exposure process parameters, the Dill model is used to simulate exposure and obtain the distribution of photosensitive mixture in the photoresist after exposure. The calculation process includes discretizing the exposure dose according to the exposure time to calculate the distribution of photosensitive mixture at different thicknesses in the photoresist.

[0008] Based on the baking process parameters, the exposed photoresist was simulated and baked to obtain the distribution of the photosensitive mixture in the baked photoresist.

[0009] Based on the aforementioned development process parameters, the baked photoresist is simulated and developed to obtain the morphology and size data of the developed photoresist.

[0010] Optionally, the thickness of the photoresist before exposure is greater than or equal to 1 micrometer.

[0011] Optionally, the exposure process parameters include exposure wavelength, exposure intensity, and exposure time.

[0012] Optionally, the process of simulating exposure using the Dill model includes:

[0013] Based on the light source intensity, the thickness of each film layer, and the initial extinction coefficient, the light field distribution I(z, 0) at time t=0 with different thicknesses is obtained. At this time, the distribution M0(z) of the photosensitive mixture in the photoresist is 1, where z is the thickness of the photoresist. The light field distribution I(z, t) is... n The result is obtained by calculating using the Rayleigh-Sommerfeld diffraction formula;

[0014] The exposure time is discretized into t0, t1, and so on up to t2. n The exposure dose is discretized as ΔDose(z) = I(z, t) i )*Δt, where Δt=t i -t i-1 t0 is the time t=0, and i is a positive integer less than or equal to n;

[0015] Calculate the exposure dose ΔDose(z) at time t=t1 and the distribution M of the photosensitive mixture in the photoresist. t1 (z), Δt=t1-t0, ΔDose(z)=I(z,0)*(t1-t0), ΔM(z)=(e -C* ΔDose(z) -1)*M t0 (z), M t1 (z) = M t0 (z)+ ΔM(z), where C is the photochemical reaction rate of the photoresist;

[0016] Calculate the refractive index n of the photoresist at time t=t1. t1 (z) and extinction coefficient k t1 (z), n t1 (z) = n bleached +(n unexposed - n bleached )*M t1 (z), k t1 (z) = (AM t1 (z) + B), where n unexposed Let n be the refractive index before exposure. bleached Here, A represents the refractive index after exposure, and B represents the absorption parameter that is not related to exposure. ;

[0017] Based on the light source intensity, the thickness of each film layer, and the refractive index n at time t=t1 t1 (z) and extinction coefficient k t1 (z), to obtain the light field distribution I(z,t1) at time t=t1;

[0018] Similarly, calculate t=t i The exposure dose at each time point, the distribution of the photosensitive mixture in the photoresist, and the light field distribution are determined until t=t is obtained. n Distribution of photosensitive mixture in photoresist at time M tn (z), with M representing the distribution of the photosensitive mixture in the photoresist. tn (z) is the output result.

[0019] Optionally, the baking process parameters include baking temperature and baking time.

[0020] Optionally, methods for simulating baking of the exposed photoresist include:

[0021] Based on the Fick diffusion equation, the diffusion coefficient was modified by baking temperature and concentration, and combined with baking time, the distribution of photosensitive mixture in the photoresist after baking was calculated.

[0022] Optionally, the developing process parameters include developing time.

[0023] Optionally, the step of simulating development of the baked photoresist includes:

[0024] The photoresist development rate R[M] based on the distribution of the photosensitive mixture was obtained.

[0025] R[M] = R max +R min ,

[0026] Among them, R max It is the maximum developing rate, R min It is the minimum developing rate, n and Both represent the development constants for different concentrations. The development constant representing the notch. This represents the influence coefficient of the relative concentration of the photosensitive compound PAC. = ,

[0027] Based on the development rate of the photoresist with photosensitive mixture distribution and combined with the development time, the morphology of the developed photoresist is obtained.

[0028] Based on another aspect of the present invention, a photolithography simulation apparatus for micron-scale photoresist is also provided, comprising:

[0029] The parameter acquisition module is used to acquire lithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters.

[0030] The exposure simulation module is used to simulate exposure based on the exposure process parameters and the Dill model to obtain the distribution of photosensitive mixture in the photoresist after exposure. Its calculation process includes discretizing the exposure dose according to the exposure time to calculate the distribution of photosensitive mixture at different thicknesses in the photoresist.

[0031] The baking simulation module is used to simulate baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the photoresist after baking.

[0032] The development simulation module is used to simulate the development of the baked photoresist to obtain the morphology and size data of the developed photoresist.

[0033] Based on another aspect of the present invention, a photolithography adjustment method for micron-thickness photoresist is also provided. According to the photolithography simulation method described above, the morphology and size data of the developed photoresist are obtained, and the photolithography simulation parameters are adjusted until the morphology and size data of the developed photoresist meet the expectations.

[0034] In summary, this invention obtains photolithography simulation parameters, sequentially uses the exposure process parameters, and employs the Dill model to simulate exposure to obtain the distribution of the photosensitive mixture in the photoresist after exposure. Next, based on the baking process parameters, it simulates baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the baked photoresist. Finally, based on the developing process parameters, it simulates developing the baked photoresist to obtain the morphology and size data of the developed photoresist. Compared to on-site debugging in one or more processes of photolithography (e.g., exposure, baking, or developing), this embodiment uses the above-mentioned photolithography simulation method before actual photolithography to simulate and adjust the entire photolithography process from the perspectives of photoresist thickness, exposure process parameters, baking process parameters, and developing process parameters. This allows the simulation results to be closer to expectations, enabling more accurate prediction of critical dimensions. Furthermore, it provides more debugging options and helps in selecting process parameter schemes that take into account other technical effects, such as improving photolithography efficiency and reducing photolithography material costs. Attached Figure Description

[0035] Those skilled in the art will understand that the accompanying drawings are provided to better understand the invention and do not constitute any limitation on the scope of the invention.

[0036] Figure 1 A flowchart of the photolithography simulation method for micron-thickness photoresist provided in Example 1;

[0037] Figure 2 A schematic diagram simulating the iterative process of exposure;

[0038] Figure 3a This is a cross-sectional schematic diagram of the light field distribution in the photoresist;

[0039] Figure 3b for Figure 3a A schematic diagram of the distribution of the photosensitive mixture formed in the corresponding light field;

[0040] Figure 3c To and Figure 3b A schematic diagram showing the distribution of the photosensitive mixture in the photoresist after baking;

[0041] Figure 3d To and Figure 3c The corresponding schematic diagram of the photoresist morphology after development.

[0042] In the attached image:

[0043] 11 - Middle area; 12 - Side areas. Detailed Implementation

[0044] To make the objectives, advantages, and features of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the drawings are all in a very simplified form and are not drawn to scale, and are only used to facilitate and clarify the explanation of the embodiments of this invention. Furthermore, the structures shown in the drawings are often part of the actual structures. In particular, different figures may emphasize different aspects and may sometimes use different scales.

[0045] As used in this invention, the singular forms “a,” “an,” and “the” include plural objects; the term “or” is generally used to mean “and / or”; the term “a number” is generally used to mean “at least one”; and the term “at least two” is generally used to mean “two or more”. Furthermore, the terms “first,” “second,” and “third” are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as “first,” “second,” or “third” may explicitly or implicitly include one or at least two of that feature, unless otherwise expressly indicated.

[0046] Example 1

[0047] Example 1 provides a photolithography simulation method for photoresist with a thickness of micrometers.

[0048] Figure 1 The flowchart shows the photolithography simulation method for micron-thickness photoresist provided in Example 1.

[0049] like Figure 1 As shown, the photolithography simulation method for micron-thickness photoresist provided in this embodiment includes:

[0050] S01: Obtain lithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters;

[0051] S02: Based on the exposure process parameters, the Dill model is used to simulate exposure to obtain the distribution of photosensitive mixture in the photoresist after exposure. The calculation process includes discretizing the exposure dose according to the exposure time.

[0052] S03: Based on the baking process parameters, simulate baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the baked photoresist;

[0053] S04: Based on the development process parameters, simulate development is performed on the baked photoresist to obtain the morphology and size data of the developed photoresist.

[0054] First, step S01 is executed to obtain the photolithography simulation parameters, which include exposure process parameters, baking process parameters and development process parameters.

[0055] In this embodiment, a negative photoresist is used as an example to make it suitable for the lift-off process of multilayer rewiring technology in packaging. Furthermore, the thickness of this photoresist can be greater than or equal to 1 micrometer. It is understood that compared to conventional patterning processes, the lift-off process can omit the etching step, but it places higher demands on the morphology of the photoresist.

[0056] The exposure process parameters may include exposure wavelength, exposure intensity, and exposure time, and the product of exposure intensity and exposure time may be the exposure dose; the baking process parameters after exposure may include baking temperature and baking time; the developing process parameters may include developing time.

[0057] Next, step S02 is executed, based on the exposure process parameters, the Dill model is used to simulate exposure to obtain the distribution of photosensitive mixture in the photoresist after exposure. The calculation process includes discretizing the exposure dose according to the exposure time.

[0058] This embodiment considers a relatively thick photoresist and employs a layered iterative approach to address the uneven distribution of materials during exposure. Specifically, it uses scalar diffraction theory to discretize the exposure time and thickness, calculating the distribution of the photosensitive mixture within the photoresist. Each calculation of the light field distribution is based on Rayleigh-Sommerfeld diffraction theory within scalar diffraction, fully considering the diffraction effect in the near field and its reflection from the upper (air) and lower (substrate) interfaces. Furthermore, when using the Dill model for simulation, the exposure time and thickness are discretized, and the exposure coefficient, extinction coefficient, and redistribution of the photosensitive compound concentration are iteratively calculated using the Dill exposure equation. This fully considers the interaction between the light field distribution and the distribution of the photosensitive mixture in the photoresist, making the process closer to the real exposure process. When using scalar diffraction theory to calculate the light field, an unpolarized light source is used. The calculation results from an unpolarized light source are more rigorous, significantly reducing simulation time and computational resources. It is understandable that compared to thin-layer photoresist (whose thickness is, for example, less than 1 micrometer), the thicker photoresist in this embodiment has more obvious interference and reflection. If a more detailed and comprehensive simulation is performed based on the non-Dill model, it is difficult to obtain an accurate distribution of the photosensitive mixture, and it is also impossible to obtain a more accurate key dimension simulation prediction for more details of subsequent baking and development.

[0059] Figure 2 This is a schematic diagram simulating the iterative process of exposure. For details, please refer to... Figure 2 Step ① is executed, based on the light source intensity, the thickness of each film layer, and the initial extinction coefficient, to obtain the light field distribution I(z, 0) at time t=0 (t0) for different thicknesses. At this time, the distribution of the photosensitive mixture in the photoresist is M0(z)=1, where z is the photoresist thickness. Next, step ② is executed, to calculate the exposure dose ΔDose(z) at time t=t1, i.e., after exposure Δt, where Δt=t1-t0, and ΔDose(z)=I(z, 0)*(t1-t0). Next, step ③ is executed, using a quasi-static approximation, to calculate the change in the photosensitive mixture in the photoresist after time t1, ΔM(z)=(e... -C* ΔDose(z) -1)*M t0 (z), then the distribution M of the photosensitive mixture in the photoresist at this time t1 (z) =M t0 (z) + ΔM(z), where C is the photochemical reaction rate of the photoresist. Next, step ④ is performed to calculate the complex refractive index of the photoresist after time t=t1, i.e., the refractive index n. t1 (z) and extinction coefficient k t1 (z), n t1 (z) = n bleached + (nunexposed - n bleached )*M t1 (z), k t1 (z) = (AM t1 (z) +B), where n unexposed Let n be the refractive index before exposure. bleached Here, A represents the refractive index after exposure, and B represents the absorption parameter that is not related to exposure. The exposure wavelength is set. Next, step ⑤ is executed, based on the light source intensity, thickness of each film layer, and refractive index n at time t=t1. t1 (z) and extinction coefficient k t1 (z), to obtain the light field distribution I(z,t1) at time t=t1. Then, using the light field distribution I(z,t1) at time t=t1 and M... t1 (z), refractive index n t1 (z) and extinction coefficient k t1 (z) According to t=t2, t3...t n The t=t calculation is obtained by iterative calculation. n Distribution of photosensitive mixture in photoresist at time M tn (z), where t0, t1, ..., t n This is a discretization of the total exposure time.

[0060] In a specific example, Figure 3a This is a cross-sectional schematic diagram of the light field distribution in a local area of ​​the photoresist. The central region 11 is the dark area in the photoresist, and the two side regions 12 are the light-transmitting areas in the photoresist. Figure 3b for Figure 3a This diagram illustrates the distribution of the photosensitive mixture formed in the corresponding light field. The two side regions 12 represent the photosensitive mixture regions in the photoresist, while the middle region 11 represents the unreacted area in the photoresist. From... Figure 3a The diffraction phenomenon and the interference and standing wave effect formed by substrate reflection in regions 12 on both sides can be seen. Figure 3b As can be seen, diffraction phenomena and interference formed by substrate reflection in the middle region 11 are observed. The aforementioned interference and standing wave effect are more pronounced near the boundary of the lithographic image (latent image), making the key dimensions and sidewall morphology of the latent image formed by exposure difficult to define. Moreover, in the two side regions 12, the exposure degree (reactivity degree) of the photoresist near the upper layer is greater than that of the photoresist near the lower layer, thus giving the latent image a morphology that is wider at the top and narrower at the bottom.

[0061] Next, step S03 is performed, based on the baking process parameters, to simulate baking the exposed photoresist and obtain the distribution of the photosensitive mixture in the baked photoresist.

[0062] This embodiment utilizes the Fick diffusion equation, considering diffusion coefficients corrected for temperature and concentration, to calculate the distribution of the photosensitive mixture in the photoresist after baking. Specifically, the Fick diffusion equation in the baking process is:

[0063] = D PAC * ▽ 2 M PAC ,

[0064] Among them, M PAC D represents the normalized concentration of the photosensitive compound PAC. PAC This represents the diffusion coefficient of PAC.

[0065] D PAC (T) = D0exp( - ) *exp( ),

[0066] Where T represents the post-baking temperature, α and β are adaptation parameters, D0 is the diffusion constant, E0 is the diffusion activation energy, and R is the molar gas constant.

[0067] D PAC Substituting into the Fick diffusion equation, the diffusion length of the PAC (including the exposed and unexposed areas) at different locations and depths can be obtained. :

[0068] ,

[0069] Among them, t PEB This indicates the time for post-baking.

[0070] In a specific example, Figure 3c To and Figure 3b The diagram shows the distribution of the photosensitive mixture in the photoresist after baking. Regions 12 on both sides represent the photosensitive mixture areas in the photoresist, while region 11 in the middle represents the unreacted area in the photoresist. From... Figure 3c and Figure 3b The comparison shows that baking the exposed photoresist results in a more complete and balanced reaction in the two side regions 12, eliminating the influence of the internal standing wave effect on the concentration of photosensitive compounds in the photoresist. Furthermore, the boundary morphology of the two side regions 12 and the middle region 11 also changes somewhat due to baking.

[0071] Next, step S04 is executed, based on the development process parameters, the baked photoresist is simulated to obtain the morphology and size data of the developed photoresist.

[0072] This embodiment uses the Notch development formula to calculate the morphology of the photoresist after development based on the development time. In this embodiment, the photoresist is negatively charged, and the development process can be negative development, i.e., the linewidth W = 1 - M. Therefore, the development rate R[M] of the photoresist based on the (reverse) distribution of the photosensitive mixture is:

[0073] R[M] = R max +R min

[0074] Among them, R max It is the maximum developing rate (full exposure), R min It is the minimum development rate (unexposed), n and Both indicate the selectivity of development to concentration (development constant). Indicates the intensity of the notch (development constant). This represents the influence coefficient of the relative concentration of the photosensitive compound PAC.

[0075] a =

[0076] in, This represents the threshold indicating the relative concentration of the photosensitive compound PAC. Of course, R... max R min , These factors are also related to the developer formulation and the developer processing temperature. After obtaining the development rate of the photoresist based on the (reverse) distribution of the photosensitive mixture, the final developed photoresist morphology can be obtained by combining it with the development time.

[0077] In a specific example, Figure 3d To and Figure 3c The corresponding schematic diagram of the photoresist morphology after development shows that the photoresist is retained in the two side regions 12, while the middle region 11 is the gap (opening) formed by the removal of the unexposed photoresist. Taking the formation of a top-wide and bottom-narrow opening on a silicon substrate using PSPI (photosensitive polyimide) photoresist with a thickness of 8.4 micrometers as an example, the process parameters include: exposure wavelength of 436nm, exposure intensity of 22.5mW, and exposure time of 14s (i.e., exposure dose of 315mJ / cm). 2 After exposure, the baking temperature was 45℃ and the time was 60s, followed by a development time of 30s. Simulations and experiments were conducted under different masks (masks with different line widths), and the data are shown in the table below:

[0078]

[0079] Here, Top corresponds to the width of the top of the opening, and Bottom corresponds to the width of the bottom of the opening. It is easy to see that in the simulation results and experimental results, the bottom and top of the opening of the developed photoresist have very similar widths (less than 5%), and the two have similar trends (sidewall tilt angle). That is, the two have similar 3D morphologies. In other words, the photolithography simulation method of this embodiment can accurately simulate and predict actual photolithography, thereby reducing the dependence on on-site debugging through early simulation debugging, thus saving the time and cost of the experiment.

[0080] In addition, in other examples of this embodiment, the photoresist can also be positive, wherein the calculation method for the distribution of light field and PAC concentration can be similar to the calculation method for negative photoresist, but the corresponding development process is positive development, that is, the exposed area is removed by development, so the linewidth W=M.

[0081] Example 2

[0082] Implementation 2 provides a photolithography simulation device for photoresist with a thickness of micrometers.

[0083] The photolithography simulation apparatus for micron-thick photoresist provided in this embodiment includes a parameter acquisition module, an exposure simulation module, a baking simulation module, and a development simulation module. The parameter acquisition module acquires photolithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters. The exposure simulation module uses the Dill model to simulate exposure based on the exposure process parameters to obtain the distribution of the photosensitive mixture in the exposed photoresist. Its calculation process includes discretizing the exposure dose according to the exposure time to calculate the distribution of the photosensitive mixture at different thicknesses in the photoresist. The baking simulation module simulates baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the baked photoresist. The development simulation module simulates developing the baked photoresist to obtain the morphology and size data of the developed photoresist. Specific details of the above photolithography simulation can be found in Embodiment 1, and will not be repeated here.

[0084] Example 3

[0085] Implementation 3 provides a method for adjusting the photolithography of photoresist with a thickness of micrometers.

[0086] The photolithography adjustment method for micron-thick photoresist provided in this embodiment uses the aforementioned photolithography simulation method based on provided photolithography simulation parameters to obtain the morphology and size data of the photoresist after development. The photolithography simulation parameters are then adjusted until the morphology and size data of the photoresist after development meet expectations. Compared to on-site debugging in one or several processes of photolithography (e.g., exposure, baking, or development), this embodiment uses the aforementioned photolithography simulation method before actual photolithography to perform multi-process (full photolithography process) multi-parameter process adjustment simulations of photoresist thickness, exposure process parameters, baking process parameters, and development process parameters. This allows the simulation results to be closer to expectations, enabling more accurate prediction of critical dimensions. Furthermore, it provides more debugging options and helps in selecting process parameter schemes that take into account other technical effects, such as improving photolithography efficiency and reducing photolithography material costs.

[0087] In summary, this invention obtains photolithography simulation parameters, sequentially uses the exposure process parameters, and employs the Dill model to simulate exposure to obtain the distribution of the photosensitive mixture in the photoresist after exposure. Next, based on the baking process parameters, it simulates baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the baked photoresist. Finally, based on the developing process parameters, it simulates developing the baked photoresist to obtain the morphology and size data of the developed photoresist. Compared to on-site debugging in one or more processes of photolithography (e.g., exposure, baking, or developing), this embodiment uses the above-mentioned photolithography simulation method before actual photolithography to simulate and adjust the entire photolithography process from the perspectives of photoresist thickness, exposure process parameters, baking process parameters, and developing process parameters. This allows the simulation results to be closer to expectations, enabling more accurate prediction of critical dimensions. Furthermore, it provides more debugging options and helps in selecting process parameter schemes that take into account other technical effects, such as improving photolithography efficiency and reducing photolithography material costs.

[0088] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A photolithography simulation method for a photoresist with a thickness of micrometers, characterized in that, include: Obtain the photolithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters; Based on the exposure process parameters, the Dill model is used to simulate exposure and obtain the distribution of photosensitive mixture in the photoresist after exposure. The calculation process includes discretizing the exposure dose according to the exposure time to calculate the distribution of photosensitive mixture at different thicknesses in the photoresist. The exposure process parameters include exposure wavelength, exposure intensity, and exposure time; The process of simulating exposure using the Dill model includes: Based on the light source intensity, the thickness of each film layer, and the initial extinction coefficient, the light field distribution I(z, 0) at time t=0 with different thicknesses is obtained. At this time, the distribution M0(z) of the photosensitive mixture in the photoresist is 1, where z is the thickness of the photoresist. The light field distribution I(z, t) is... n The result is obtained by calculating using the Rayleigh-Sommerfeld diffraction formula; The exposure time is discretized into t0, t1, and so on up to t2. n The exposure dose is discretized as ΔDose(z) = I(z, t) i )*Δt, where Δt=t i -t i-1 t0 is the time t=0, and i is a positive integer less than or equal to n; Calculate the exposure dose ΔDose(z) at time t=t1 and the distribution M of the photosensitive mixture in the photoresist. t1 (z), Δt=t1-t0, ΔDose(z)=I(z,0)*(t1-t0), ΔM(z)=(e -C* ΔDose(z) -1)*M t0 (z), M t1 (z) = M t0 (z)+ ΔM(z), where C is the photochemical reaction rate of the photoresist; Calculate the refractive index n of the photoresist at time t=t1. t1 (z) and extinction coefficient k t1 (z), n t1 (z) = n bleached +(n unexposed - n bleached )*M t1 (z), k t1 (z) = (AM t1 (z) + B), where n unexposed Let n be the refractive index before exposure. bleached Here, A represents the refractive index after exposure, and B represents the absorption parameter that is not related to exposure. ; Based on the light source intensity, the thickness of each film layer, and the refractive index n at time t=t1 t1 (z) and extinction coefficient k t1 (z), to obtain the light field distribution I(z,t1) at time t=t1; Similarly, calculate t=t i The exposure dose at each time point, the distribution of the photosensitive mixture in the photoresist, and the light field distribution are determined until t=t is obtained. n Distribution of photosensitive mixture in photoresist at time M tn (z), with M representing the distribution of the photosensitive mixture in the photoresist. tn (z) is the output result; Based on the baking process parameters, the exposed photoresist was simulated and baked to obtain the distribution of the photosensitive mixture in the baked photoresist. Based on the aforementioned development process parameters, the baked photoresist is simulated and developed to obtain the morphology and size data of the developed photoresist.

2. The photolithography simulation method according to claim 1, characterized in that, The thickness of the photoresist before exposure is greater than or equal to 1 micrometer.

3. The photolithography simulation method according to claim 1, characterized in that, The baking process parameters include baking temperature and baking time.

4. The photolithography simulation method according to claim 3, characterized in that, Methods for simulating baking of exposed photoresist include: Based on the Fick diffusion equation, the diffusion coefficient was modified by baking temperature and concentration, and combined with baking time, the distribution of photosensitive mixture in the photoresist after baking was calculated.

5. The photolithography simulation method according to claim 1, characterized in that, The developing process parameters include developing time.

6. The photolithography simulation method according to claim 5, characterized in that, The step of simulating development of the baked photoresist includes: The photoresist development rate R[M] based on the distribution of the photosensitive mixture was obtained. R[M] = R max +R min , Among them, R max It is the maximum developing rate, R min It is the minimum developing rate, n and Both represent the development constants for different concentrations. The development constant representing the notch. This represents the influence coefficient of the relative concentration of the photosensitive compound PAC. = Mth represents the threshold of the relative concentration of the photosensitive compound PAC; Based on the development rate of the photoresist with photosensitive mixture distribution and combined with the development time, the morphology of the developed photoresist is obtained.

7. A photolithography simulation device for micron-scale photoresist, characterized in that, include: The parameter acquisition module is used to acquire photolithography simulation parameters, including exposure process parameters, baking process parameters, and development process parameters; wherein, the exposure process parameters include exposure wavelength, exposure intensity, and exposure time; the process of simulating exposure using the Dill model includes: Based on the light source intensity, the thickness of each film layer, and the initial extinction coefficient, the light field distribution I(z, 0) at time t=0 with different thicknesses is obtained. At this time, the distribution M0(z) of the photosensitive mixture in the photoresist is 1, where z is the thickness of the photoresist. The light field distribution I(z, t) is... n The result is obtained by calculating using the Rayleigh-Sommerfeld diffraction formula; The exposure time is discretized into t0, t1, and so on up to t2. n The exposure dose is discretized as ΔDose(z) = I(z, t) i )*Δt, where Δt=t i -t i-1 t0 is the time t=0, and i is a positive integer less than or equal to n; Calculate the exposure dose ΔDose(z) at time t=t1 and the distribution M of the photosensitive mixture in the photoresist. t1 (z), Δt=t1-t0, ΔDose(z)=I(z,0)*(t1-t0), ΔM(z)=(e -C* ΔDose(z) -1)*M t0 (z), M t1 (z) = M t0 (z)+ ΔM(z), where C is the photochemical reaction rate of the photoresist; Calculate the refractive index n of the photoresist at time t=t1. t1 (z) and extinction coefficient k t1 (z), n t1 (z) = n bleached +(n unexposed - n bleached )*M t1 (z), k t1 (z) = (AM t1 (z) + B), where n unexposed Let n be the refractive index before exposure. bleached Here, A represents the refractive index after exposure, and B represents the absorption parameter that is not related to exposure. ; Based on the light source intensity, the thickness of each film layer, and the refractive index n at time t=t1 t1 (z) and extinction coefficient k t1 (z), to obtain the light field distribution I(z,t1) at time t=t1; Similarly, calculate t=t i The exposure dose at each time point, the distribution of the photosensitive mixture in the photoresist, and the light field distribution are determined until t=t is obtained. n Distribution of photosensitive mixture in photoresist at time M tn (z), with M representing the distribution of the photosensitive mixture in the photoresist. tn (z) is the output result; The exposure simulation module is used to simulate exposure based on the exposure process parameters and the Dill model to obtain the distribution of photosensitive mixture in the photoresist after exposure. Its calculation process includes discretizing the exposure dose according to the exposure time to calculate the distribution of photosensitive mixture at different thicknesses in the photoresist. The baking simulation module is used to simulate baking the exposed photoresist to obtain the distribution of the photosensitive mixture in the photoresist after baking. The development simulation module is used to simulate the development of the baked photoresist to obtain the morphology and size data of the developed photoresist.

8. A method for adjusting the photolithography of a photoresist with a thickness of micrometers, characterized in that, According to the photolithography simulation method as described in any one of claims 1 to 6, the morphology and size data of the developed photoresist are obtained, and the photolithography simulation parameters are adjusted until the morphology and size data of the developed photoresist meet the expectations.

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