Calibration method of optical proximity correction model, electronic device and storage medium

Abstract

Embodiments of the present disclosure relate to a method for calibrating an optical proximity correction (OPC) model, an electronic device, and a storage medium. The method includes determining a value of a cost function of an OPC model based on a simulation pattern of a predetermined pattern in a layout and a wafer image corresponding to the predetermined pattern, wherein a ruler arranged along a first direction of the predetermined pattern and a second direction non-perpendicular to the first direction is given a greater weight than a ruler of a remaining direction, the first direction being a horizontal or vertical direction; iteratively updating a model parameter of the OPC model based on the value of the cost function; and determining a corresponding OPC model as a calibrated OPC model in response to the iteration satisfying a predetermined condition. The technical solution of the present disclosure can significantly improve the phenomenon that the contour of the simulation pattern of the model appears square, thereby improving the simulation accuracy.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to integrated circuits, and more particularly, to a method of calibrating an optical proximity correction model, an electronic device, and a storage medium. BACKGROUND

[0002] The production of integrated circuit chips at advanced process nodes often relies on patterning technology. The core of the patterning technology is optical proximity correction (OPC). OPC is widely used in semiconductor manufacturing processes to reduce the difference between a wafer image and a target pattern. The core of OPC is to have an accurate OPC model to predict the shape of the photoresist pattern on the wafer corresponding to different mask patterns.

[0003] In a conventional OPC modeling process, for a pattern of a square hole arranged in a regular repeating manner on a layout, the result predicted by the OPC model can have a square hole (also referred to as a square) phenomenon. This is not consistent with the round hole phenomenon on a scanning electron microscope (SEM). SUMMARY

[0004] According to example embodiments of the present disclosure, a calibration scheme of an optical proximity correction model is provided to at least partially overcome the above or other potential drawbacks.

[0005] According to an aspect of the present disclosure, a method of calibrating an optical proximity correction model is provided. The method includes determining a value of a cost function of an optical proximity correction (OPC) model based on a simulated pattern of a predetermined pattern in a layout and a wafer image corresponding to the predetermined pattern, wherein a ruler set along a first direction of the predetermined pattern and a second direction non-perpendicular to the first direction is assigned a greater weight than a ruler of a remaining direction, the first direction being a horizontal or vertical direction; updating a model parameter of the OPC model based on the value of the cost function; and in response to the updating satisfying a predetermined condition, determining the OPC model whose difference between an error in the first direction and an error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is below a threshold value as a calibrated OPC model.

[0006] In a second aspect of the disclosure, an electronic device is provided. The electronic device includes a processor; and a memory coupled with the processor, the memory having stored therein instructions that, when executed by the processor, cause the device to perform acts comprising: determining a value of a cost function of an optical proximity correction (OPC) model based on a simulation pattern of a predetermined pattern in a layout and a wafer image corresponding to the predetermined pattern, wherein a scale bar disposed along a first direction of the predetermined pattern and a second direction non-perpendicular to the first direction is assigned a greater weight than scale bars of remaining directions, the first direction being a horizontal or vertical direction; performing an iteration on the OPC model based on the value of the cost function to update a model parameter; and in response to the iteration satisfying a predetermined condition, determining a corresponding OPC model as a calibrated OPC model.

[0007] In some embodiments, determining the value of the cost function of the OPC model based on the simulation pattern of the predetermined pattern in the layout and the wafer image corresponding to the predetermined pattern comprises: determining an error between a simulated CD of the predetermined pattern at a position indicated by each scale bar and a corresponding CD on the wafer image; and determining the value of the cost function based on each error and a weight of each of the scale bars.

[0008] In some embodiments, determining the value of the cost function based on each error and the weight of each of the scale bars comprises: determining a square value of each error, respectively; performing a weighted average on the square values based on the weight of each scale bar to determine a weighted average value, respectively; and determining the weighted average value as the value of the cost function.

[0009] In some embodiments, determining the value of the cost function based on each error and the weight of each of the scale bars comprises determining the value of the cost function based on the following formula:

[0010]

[0011] wherein cost_function represents the cost function; simulation_CDj represents a simulated critical dimension of the predetermined pattern; wafer_CDj represents a corresponding critical dimension of a lithographic pattern j of the predetermined pattern on the wafer; wt j represents a weight of a scale bar of each direction in each predetermined pattern, and n is a number of the predetermined patterns.

[0012] In some embodiments, in the iteration process, a scale bar disposed along a third direction of the predetermined pattern non-perpendicular to the first direction is also assigned a greater weight than scale bars of remaining directions.

[0013] In some embodiments, an angle between the second direction and the first direction is 40° to 50°.

[0014] In some embodiments, the angle between the second direction and the first direction is 45°.

[0015] In some embodiments, the third direction is perpendicular to the second direction.

[0016] In some embodiments, in response to the iteration satisfying the predetermined condition, the OPC model whose difference between the error in the first direction and the error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is lower than the threshold value is determined as the calibrated OPC model.

[0017] In some embodiments, the weight of the first direction, the second direction and the third direction is 50-300 times of the weight of the rest directions.

[0018] In some embodiments, the iteration satisfying the predetermined condition comprises: the value of the cost function being lower than a predetermined threshold value; the number of iterations reaching a predetermined number; or the absolute value of the difference between the values of the cost function of the previous and the current iteration being less than a predetermined difference threshold value.

[0019] In some embodiments, determining the OPC model whose difference between the error in the first direction and the error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is lower than the threshold value as the calibrated OPC model comprises: in response to the number of iterations reaching the predetermined number, selecting the OPC model whose model error in the first direction and the second direction is lower than the second threshold value from the last predetermined number of iterations as the calibrated model.

[0020] In some embodiments, the predetermined pattern is an anchor point.

[0021] In a third aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon machine executable instructions which, when executed by a processor, implement the method according to the first aspect of the present disclosure.

[0022] It will be appreciated from the following description that the technical solution of the present disclosure can significantly improve the phenomenon that the contour of the simulated pattern of the model appears square, thereby improving the simulation accuracy.

[0023] The summary is provided to introduce some aspects of the concepts in a simplified form that are further described below in the detailed description. The summary is not intended to identify key or essential features of the disclosure, nor is it intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A schematic diagram showing the way of placing a ruler on a pattern in a layout in a conventional solution is shown;

[0025] Figure 2 A schematic diagram showing the simulated pattern generated in the case of the way of placing a ruler shown; Figure 1 A schematic diagram showing the simulated pattern generated in the case of the way of placing a ruler shown;

[0026] Figure 3 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented is shown;

[0027] Figure 4 A flowchart showing a calibration method of an optical proximity correction model according to some embodiments of the present disclosure is shown;

[0028] Figure 5 A schematic diagram showing placement of a ruler on a pattern of a layout according to some embodiments of the present disclosure is shown;

[0029] Figure 6 A schematic diagram showing a simulated pattern generated in the case of the shown manner of placement of the ruler is shown; Figure 5 A schematic diagram showing a simulated pattern generated in the case of the shown manner of placement of the ruler is shown;

[0030] Figure 7 A flowchart showing a calibration method of an optical proximity correction model according to some embodiments of the present disclosure is shown;

[0031] Figure 8 A block diagram of a computing device capable of implementing various embodiments of the present disclosure is shown.

[0032] In the various drawings, like or corresponding elements are denoted by like or corresponding reference numerals. DETAILED DESCRIPTION

[0033] The principles of the present disclosure will now be described, by way of example only, with reference to various example embodiments and with the aid of the accompanying drawings. It is to be understood that the description of these embodiments is merely intended to illustrate the general principles of the present disclosure. Therefore, the scope of the present disclosure should not be limited to these example embodiments. It should be noted that like or corresponding elements are denoted by like or corresponding reference numbers in the various drawings. Those skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein can be employed without departing from the principles of the application described herein.

[0034] As used herein, the term "includes" and its variants are intended to be open-ended to the extent that suggest a departure from exclusivity. The term "or" is intended to mean "and / or" unless specifically indicated otherwise. The term "based on" means "based, at least in part, on." The terms "one example embodiment" and "an example embodiment" mean "at least one example embodiment." The term "another embodiment" means "at least one additional embodiment." The terms "a first," "a second," etc. can refer to different or the same objects.

[0035] As mentioned before, for a pattern of square holes arranged in a regular manner on a layout, the OPC model prediction result can be square holes, which is not consistent with the round hole pattern observed in SEM. Specifically, the conventional OPC modeling mainly relies on the measurement data of the critical dimension (CD) in the orthogonal direction (0° or 90°) to calibrate the model. Since no data in other directions is introduced in the modeling process, the profile of the pattern simulated by the model presents square features, which is significantly different from the round hole pattern observed in SEM.

[0036] For a square pattern on a layout, the pattern on the wafer obtained by SEM scanning is actually round. However, due to the conventional gauge setting manner, the OPC prediction result can be square, which is inconsistent with that obtained by SEM. The reason is that many parameters need to be run in the model construction process, and these parameters in the model determine the performance of the model. Since no data in other directions is introduced, in the process of iteratively constructing the model, these parameters will be biased towards the model construction that is beneficial to accurately predict the horizontal and vertical directions, and will not be biased towards other directions, which leads to obvious errors in the model prediction in other directions, i.e., the generation of square hole phenomenon.

[0037] The spatial image profile generated based on the principle of optical imaging is also close to square. Due to the post exposure bake (PEB) development process, the change in photoacid concentration distribution causes the lithography result on the wafer to present a round shape.

[0038] It should be noted that if it is a non-repeated arrangement pattern, the surrounding environment of the pattern is irregular, and the influence of the contour morphology of the pattern at each position is also irregular, and the square hole phenomenon can not necessarily occur.

[0039] Figure 1 A schematic diagram of placing a gauge on a pattern in a conventional scheme is shown. As shown in Figure 1 , a pattern 100 in a layout is shown, in which a gauge 102 is only arranged in the horizontal direction. In this way, only the model error in the horizontal direction will be considered in the iteration process of the OPC model. If only the horizontal or vertical direction is considered, since the diagonal direction is not considered, after the model iteration is completed, square holes can occur in the simulated pattern.

[0040] Referring to Figure 2 , Figure 2 A schematic diagram of a simulated pattern generated in the case of the gauge placement manner shown in Figure 1 is shown. As shown in Figure 2As shown, the profile of the lithography image 202 is a square hole shape. Because, in the iteration process of the OPC model, only the model error in the horizontal direction is considered, due to the fact that the diagonal direction is not considered, after the model iteration is completed, it is likely to cause the square hole as shown in the simulation pattern Figure 2 , which does not match the image on the wafer.

[0041] In theory, what the actual SEM pattern is, the prediction result of the OPC model should be. And the layout pattern is only the pattern on the mask, due to the optical proximity effect, there is a large difference with the actual SEM pattern. But the general evaluation standard of the accuracy of the OPC model only focuses on the accuracy of the horizontal or vertical direction. In the case of the square hole predicted by the model, the horizontal and vertical directions are also consistent with the size on the actual SEM picture. According to the evaluation standard of the traditional model, the prediction of this model is accurate. In fact, the entire model has obvious deviation in the non-orthogonal direction in each direction, that is, the predicted square is inconsistent with the circle on the wafer, so the prediction of the model is not accurate. This may be caused by some unreasonable parameters in the modeling process.

[0042] In view of this, the present disclosure provides an improved scheme.

[0043] Embodiments of the present disclosure provide an improved calibration method of an OPC model. The method comprises: determining a value of a cost function of an optical proximity correction OPC model based on a simulation pattern of a predetermined pattern in a layout and a wafer image corresponding to the predetermined pattern; iteratively updating a model parameter of the OPC model based on the value of the cost function, wherein a ruler arranged in a first direction of the predetermined pattern and a second direction non-perpendicular to the first direction is given a greater weight than a ruler of the remaining directions, the first direction being a horizontal or vertical direction; and in response to the iteration satisfying a predetermined condition, determining the OPC model whose difference between an error in the first direction and an error in the second direction between the simulation pattern of the predetermined pattern and the wafer image is lower than a threshold value as a calibrated OPC model.

[0044] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0045] Figure 3 A schematic diagram of an example environment 300 in which embodiments according to the present disclosure can be implemented is shown. As Figure 3 shown, the example environment 300 includes a computing device 310 and a client 320.

[0046] In some embodiments, the computing device 310 can interact with the client 320. For example, the computing device 310 can receive input messages from the client 320 and output feedback messages to the client 320. In some embodiments, the input messages from the client 320 can be design layout data, wafer data, etc. The computing device 310 can perform corresponding mathematical operations on the design layout data, wafer data, etc., and output the corresponding operation results to the client 320.

[0047] In some embodiments, the computing device 310 may include, but is not limited to, a personal computer, a server computer, a handheld or laptop device, a mobile device (such as a mobile phone, a personal digital assistant PDA, a media player, etc.), a consumer electronics product, a minicomputer, a mainframe computer, cloud computing resources, etc.

[0048] It should be understood that the description of the structure and functionality of example environment 300 for illustrative purposes only is not intended to limit the scope of the subject matter described herein. The subject matter described herein may be implemented in different structures and / or functionalities. This environment is merely illustrative and is not intended to limit the application environment of the embodiments of this disclosure.

[0049] To more clearly explain the principles of this disclosure, reference will be made below. Figure 4 Let me describe it in more detail.

[0050] Figure 4 A flowchart of a calibration method 400 for an optical proximity correction model according to some embodiments of the present disclosure is shown.

[0051] At box 402, the value of the cost function of the OPC model is determined based on the simulation graphic of the predetermined graphic in the layout and the wafer image corresponding to the predetermined graphic, wherein the scale set along a first direction of the predetermined graphic and a second direction that is not perpendicular to the first direction are given greater weight than the scales in the other directions, the first direction being either horizontal or vertical.

[0052] In some embodiments, a series of wafer CD data of the square hole pattern may be collected first for the calibration of the OPC model. The wafer CD data may include various data of different pattern sizes and different pattern spacings.

[0053] Typically, commercial OPC modeling tools provide an open data import interface. Users need to first organize the data into a model-readable .csv or .asd format, and then enter the path of the corresponding .csv or .asd file into the interface so that the OPC modeling tool can retrieve it.

[0054] Currently, cost functions are widely used in the calibration (or optimization) of models. The value of a cost function can indicate the difference between the simulation result of a pattern and the corresponding wafer image. The smaller the value of the cost function, the smaller the difference between the two. Therefore, the model can be calibrated based on the value of the cost function.

[0055] In some embodiments of the present disclosure, determining the value of the cost function of the OPC model based on the simulation pattern of the predetermined pattern in the layout and the corresponding wafer image can include: determining the error between the simulation CD of the predetermined pattern at the position indicated by each scale and the corresponding CD on the wafer image; and determining the value of the cost function based on each error and the weight of each scale.

[0056] In some embodiments, determining the value of the cost function based on each error can include: respectively determining the square value of each error; respectively weighting the square values based on the weight of each scale to determine a weighted average value; and determining the weighted average value as the value of the cost function.

[0057] In some embodiments, when the OPC model is calibrated using the cost function, the following equation can be used:

[0058]

[0059] wherein cost_function represents the cost function; simulation_CDj represents the simulation result of the test pattern (the predetermined pattern), specifically, the simulation critical dimension of the predetermined pattern; wafer_CDj represents the measurement result of the lithography pattern j of the test pattern on the wafer, which can be referred to as the wafer CD or the CD of the wafer pattern; and n is the number of test patterns.

[0060] In some embodiments, a scale can be set on the predetermined pattern. The predetermined pattern can be any pattern. In some embodiments, the scale is set on an anchor.

[0061] An anchor is an important concept in the lithography process. The anchor can be a certain pattern selected from a series of different size patterns, which usually corresponds to the minimum design rule of the process level. The anchor is used to calibrate the energy of the wafer at the corresponding level in the process (or referred to as resolution), so as to calibrate the signal threshold in the OPC model. For each corresponding process level, there is only one anchor for determining the exposure energy. In other words, the anchor is a pattern used to determine the energy required for lithography, and the CD and space of the anchor are generally the minimum CD and space of the layer when designing. Many patterns are collected for modeling, and each pattern has its corresponding scale. Not only the scale is set at the anchor, but also the scale can be set on other patterns. The scale can be understood as the measurement position. Many data are needed for modeling, so many scales of different patterns are needed.

[0062] It is not practical to add scales in different directions for each pattern in the actual modeling, which wastes the model iteration time. Generally, since the anchor points are the most important, in some embodiments, only the anchor points are added with scales in different directions to correct the direction of the model iteration in the parameters. Since the anchor points are dense array patterns, the scale CDs in different directions are more similar to each other than in non-dense array patterns, and the improvement in the corner effect is very obvious. Here, the dense array pattern is distinguished from the non-dense array pattern. The dense array pattern means that the pattern is dense, that is, there are patterns in the up, down, left and right directions and the 45° and 135° directions of a square hole, and the horizontal and vertical distances between each pattern are the same. The profile of such a model is generally circular, and each scale may have a difference of at most a few nanometers between 0° and 45°. The non-dense array pattern, for example, has only one row of patterns, which are densely arranged in the left and right directions, but there are no patterns in the horizontal and vertical directions, that is, the environment is isolated. Therefore, the model CD in the horizontal and vertical directions of the pattern is likely to have a difference, and the profile is not necessarily circular. Therefore, the equal scale CDs in different directions emphasize the dense array pattern relative to the non-dense array pattern. In fact, for the dense array pattern, there may be some model CD differences in the horizontal and vertical directions and the 45° direction environment. However, according to industry experience, under a specific light source, the difference is not large, and there is no obvious square hole behavior. If the obvious square hole behavior is found, which is different from the actual SEM result, human intervention is needed.

[0063] As mentioned above, the anchor point pattern is the most important, and it is more appropriate to set the scale on the anchor point, which can simplify the iteration process and improve the profile accuracy of the model prediction.

[0064] As mentioned above, the anchor point pattern is the most important, and it is more appropriate to set the scale on the anchor point, which can simplify the iteration process and improve the profile accuracy of the model prediction.

[0065] As mentioned above, since only the horizontal or vertical direction is considered in the conventional method without considering the inclined direction, the profile of the pattern simulated by the model often presents a square feature, which is significantly different from the circular hole morphology of the actual SEM imaging.

[0066] In some embodiments, the second direction forms an angle of 40° to 50° with the first direction. In some embodiments, the second direction forms an angle of 45° with the first direction. In some embodiments, the third direction is perpendicular to the second direction.

[0067] In some embodiments of the present disclosure, by increasing the weight of the scale of the other direction, the direction of the cost function reduction can be tilted towards the direction of the scale increase during the model iteration process. In this way, the OPC model can be made to move in the direction that makes the error at the scale set by the first and second directions smaller during the iteration process, thereby avoiding the occurrence of square holes.

[0068] In some embodiments, the weight of the first and second directions is 50-300 times the weight of the remaining directions. The weight of the other directions is generally 1, and a large distinction between the two can be made.

[0069] Generally, when building an OPC model, most of the data required is in the form of "array", that is, the horizontal and vertical two-directional pattern environment is exactly the same, so only one direction, i.e. the horizontal direction, is required. The wafer CD in the vertical direction is the same as that in the horizontal direction. Some non- "array" patterns can also be collected, which have different horizontal and vertical two-directional pattern environments, and it is possible to collect some vertical wafer CDs to ensure the accuracy of the vertical direction. However, the wafer CD data of this vertical pattern is not necessarily collected. Since the time for building the model is generally limited, according to industry experience, in order to save time, only data in one direction is considered to build the model.

[0070] As known in the art, the pattern on the layout is exposed by the lithography machine, and the wafer image is formed on the wafer. The SEM pattern is obtained by scanning electron microscopy, and the wafer CD is obtained by measuring the machine.

[0071] The role of the OPC model is to accurately predict the pattern presented on the wafer after the mask is exposed by the lithography machine. For the hole layer, the pattern on the wafer is generally only concerned about the horizontal and vertical directions. The collected data is basically based on the horizontal scale (for array patterns) or with individual vertical scales (non-array patterns). Traditional OPC modeling often only focuses on the model errors in the horizontal and vertical directions, and the model errors in other directions have little effect on the performance of the hole layer. Therefore, from a technical point of view, after the square hole effect occurs, relevant technical personnel often do not pay enough attention to this phenomenon or do not recognize the root cause of the phenomenon. It may be considered that only the horizontal and vertical directions are accurate. The present application innovatively adds a diagonal scale to the OPC model, successfully solves the square hole effect, and further improves the accuracy of the OPC model.

[0072] The model calibration calculation process is that the model predicts the measurement size (measurement CD, also known as critical dimension), updates the model parameters according to the error of the model to the measurement CD, and iteratively obtains the optimal model.

[0073] In some embodiments, according to the importance of different test positions (different direction scales), the difference between simulation_CD (simulation result) and wafer_CD (wafer measurement result) for each test pattern can be set to different weights (wt) for different scales, as shown in the following formula:

[0074]

[0075] wt j The weight of each direction scale in each test pattern. The remaining parameters are the same as the formula of the cost function above. It should be noted that the form of the above formula is only illustrative, and specific adjustments can be made according to the actual selected predetermined pattern and the set scale direction.

[0076] In the known patent application (publication number CN119358486A), a cost function is mentioned. In the process of determining the cost function, the weights corresponding to different depth positions are involved. Specifically, different weights are set for different planes (depth positions) as needed, which is different from setting different weights for different directions of the scale in the present application. The application also mentions that the weights can not be set, i.e., it can be considered that each plane has the same weight. It can be seen from this that the weights in the application are for the plane position. In the application, the simulation result is the simulation value of the profile of the plane at different depths, which is different from the CD of the predetermined pattern (or test pattern) in different directions in the present application.

[0077] In another known patent application (publication number CN118627441A), a cost function is also mentioned. In this application, in the process of determining the cost function, weights are set according to the importance of different test patterns and the difference between the simulation results and the wafer measurement results of each test pattern. That is, in this application, the weights are set for test patterns, which is different from setting different weights for different directions of the ruler in the present application. In addition, in this application, the simulation results are the simulation profiles of the test patterns, and do not involve the simulation CDs at the rulers in different directions of the test patterns.

[0078] In some embodiments, in the iteration process, the rulers set in a third direction of the pattern which is non-perpendicular to the first direction are also given greater weights than the rulers in the remaining directions.

[0079] In some embodiments, the weights of the first direction, the second direction and the third direction are 50-300 times the weights of the remaining directions.

[0080] The following will be described in conjunction with Figure 5 . Figure 5 A schematic diagram of placing rulers on a predetermined pattern of a layout according to some embodiments of the present disclosure is shown. As Figure 5 shown, a first ruler 502 in a horizontal direction (which can be referred to as a first direction) is set. In addition, a second ruler 504 is set in a 45° direction (which can be referred to as a second direction), and a third ruler 506 is set in a 135° direction (which can be referred to as a third direction). In this diagram, only two directions of non-vertical horizontal directions are selected as an example, and embodiments of the present disclosure are not limited thereto, but any other non-horizontal or non-vertical direction can be selected.

[0081] The conventional ruler setting method cannot accurately capture the changes in the 45° / 135° direction. In some embodiments of the present disclosure, by setting rulers in non-perpendicular directions, such as 45° / 135° directions, the square hole problem in the conventional scheme can be avoided.

[0082] At block 404, the OPC model is iterated based on the value of the cost function to update the model parameters.

[0083] As mentioned earlier, writing the path of the corresponding.csv or.asd at the interface can be grabbed by the OPC modeling tool. After the OPC modeling tool grabs the above content, it will focus on the internal iteration of the OPC model parameters according to the weight of each ruler, and the iteration direction will proceed in the direction of smaller model error of the ruler with greater weight. After the parameter iteration is completed, the accurate model can be obtained.

[0084] The OPC model is in the process of iterating parameters in the direction of reducing the cost function, and an important component of the cost function is the variance of the model error of all scales (rms, or root mean square), the smaller the rms, the more accurate the model prediction of each scale. The model prediction is biased, and it is impossible to accurately predict all scales, which involves weights. By increasing / decreasing the weights of different scales, the model can be more accurate in predicting the direction of the scale with larger weights in the iteration process. Therefore, by increasing the weights of the three scales, the OPC model can be guided to more accurately predict the three scales in the iteration process. Once the model prediction of the three scales is accurate, the hole effect will no longer exist.

[0085] As mentioned earlier, the OPC model relies on parameter iteration to achieve accurate model results, and the parameters must have a direction or "guide" in the iteration process to let the model know which direction to proceed in the next iteration. The cost function plays the role of a guide, and the most important component of the cost function is the model error of each scale. If there are 300 scales, the variance of the model error of each of the 300 scales is introduced into the cost function, so the model iteration direction will be based on the direction of reducing the prediction error (error) of each scale. In some embodiments of the present disclosure, in order to solve the hole effect, in addition to the traditional 300 horizontal or vertical scales, two oblique scales are additionally introduced, so there are 302 scales. However, the two scales have no advantage in quantity compared to the 300 scales, so the contribution of the two scales is small when calculating the variance of the model error, and the model cannot take into account the model error of the two scales in the iteration process. Therefore, the weights of the two scales need to be increased, for example, if the model error of the two scales is 1 nm, the weight is increased to 100, then the model error becomes 100 nm, and the variance of the model error of all scales and the cost function will become very large. If you want to reduce the cost function, you must reduce the model error of the two scales with large weights to approach the horizontal direction.

[0086] Back to Figure 4 Continue to describe. At block 406, in response to the iteration satisfying the predetermined condition, the corresponding OPC model is determined as a calibrated OPC model. In practice, there can be more than one model that satisfies the predetermined condition at this time, and these models can all be calibrated models. In some embodiments, the optimal model can be selected based on other factors as needed.

[0087] In some embodiments, the OPC model whose difference between the error in the first direction and the error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is below the threshold value is determined as the calibrated OPC model in response to the iteration satisfying the predetermined condition.

[0088] In some embodiments, the iteration satisfying the predetermined condition can include: the value of the cost function being below a predetermined threshold value; the absolute value of the difference between the values of the cost function of the previous and the current iteration being less than a predetermined difference threshold value; the ratio of the values of the cost function of the previous and the current iteration being greater than a predetermined ratio threshold value; or the number of iterations reaching a predetermined number of times.

[0089] In some embodiments, the OPC model whose difference between the error in the first direction, the error in the second direction and the error in the third direction between the simulated pattern of the predetermined pattern and the wafer image are all below the threshold value is determined as the calibrated OPC model in response to the iteration satisfying the predetermined condition.

[0090] In some embodiments, the OPC model whose difference between the error in the first direction and the error in the second direction between the simulated pattern of the pattern and the wafer image is below the threshold value is determined as the calibrated OPC model can include: in response to the number of iterations reaching a predetermined number of times, selecting the OPC model whose model error in the first direction and the second direction is below a second threshold value from the last predetermined number of iterations as the calibrated model.

[0091] In some embodiments, assuming the model error in the horizontal direction is originally only 0.01 nm, if the model error of the two rulers is also reduced to 0.01 nm, then after the weight is increased to 100, the model error is only 1 nm. In this way, the model errors of the horizontal and oblique rulers are very close, and the prediction result of the model is a circle. If the difference is very large, it is very likely to be a square.

[0092] There are many other factors in addition to the cost function to evaluate the quality of the model. For example, the threshold value is a clear numerical value from the perspective of evaluating the accuracy of the model in predicting the wafer data. Other evaluation methods need to be considered, such as whether the signal of the model is healthy, whether the profile of the model is abnormal, whether the prediction of the model for the same pattern at different positions is consistent, etc. These evaluation indexes may not be a certain numerical value but a behavior.

[0093] Therefore, using the cost function to determine the accuracy of a model is just one of many methods, and the embodiments of this disclosure are not limited to this method. In some embodiments, the number of iteration rounds (times) can be set for iteration. Even if the value of the cost function falls below a predetermined threshold in a certain round, the model iteration continues, with the aim of generating as many usable models as possible to provide more model choices for subsequent performance evaluations.

[0094] In practice, multiple models are generated during the modeling process, and in some embodiments, one model is selected. For this model, the CD in the horizontal direction is closest to the CD in the 45° / 135° direction.

[0095] In some embodiments, models CD that are close in the horizontal and 45° / 135° directions at the anchor point are selected. Since a square hole prediction is not desired, a large difference between the model error in the diagonal direction and the model error in the horizontal direction indicates that it is definitely not a round hole. Only when the model errors of these three scales are close will the model result be a round hole. During the selection process, the model error can be used as a criterion. After the model iteration is completed, models for all iterations will be generated. These models will generate .csv / .asd files showing the model error for all scales. At this point, the model with the closest model error for these three scales can be selected.

[0096] In some embodiments, the weights of the three scales can be increased, and then a reasonable threshold can be set. When the model is below this threshold, it means that even with the three scales having large additional weights, the model's RMS can still be very small. This indicates that the model prediction size of the three scales is close (waferCD is the same, so theoretically the model prediction size should be consistent), and the square hole phenomenon can be resolved.

[0097] Some embodiments of this disclosure aim to address the square hole effect. The threshold is used to evaluate model accuracy; it simply artificially increases the weight of the three scales, resulting in models with smaller cost functions having smaller and closer model errors across the three scales. As long as the cost function gradually decreases during model iteration, it indirectly indicates that the model errors across the three scales are gradually converging, eliminating the need for the cost function value to fall below a certain threshold to select a model with similar errors in all three directions.

[0098] See Figure 6 , Figure 6 It shows in Figure 5 This is a schematic diagram of the simulation graphics generated when the ruler is placed as shown. Figure 6 As shown, the simulated pattern 602 is circular, consistent with the wafer image. This avoids the phenomenon of the simulated pattern having a square hole, which is common in traditional solutions.

[0099] Figure 7A flowchart of a calibration method of an optical proximity correction model according to some embodiments of the present disclosure is shown.

[0100] At block 702, collect CDs of various density of square hole patterns on wafer and SEM images in horizontal direction. Specifically, collect CDs (critical dimensions) of various density of square hole patterns on wafer and SEM images.

[0101] At block 704, add two more scales in 45° and 135° directions of the anchor point, and increase their weights.

[0102] At block 706, during the iteration of the OPC model, give greater weights to the three scales of the anchor point, so as to guide the OPC model to approach the direction with smaller model error of the three scales during the iteration.

[0103] At block 708, after the iteration of the model is completed, select the model with the closest CD in the horizontal direction and 45° and 135° directions of the anchor point, and check whether the pattern profile in the model is circular. If not, further iteration is needed until the predetermined requirement is met.

[0104] Through the continuous iteration of the model, the model cost function will approach Figure 4 the direction with smaller model error of the 0°, 45° and 135° models. At this time, the model error of the 0° scale in the horizontal direction is close to the model error of the non-horizontal scales, and the profile of the model is circular. Compared with the horizontal scale direction used in the traditional OPC modeling, the phenomenon of square profile of the model can be significantly improved by adding the 45° and 135° scales.

[0105] As mentioned above, the scales are set at the anchor point. It should be understood that embodiments of the present disclosure are not limited thereto. Instead, the scales can be set on other patterns. In addition, it should be understood that embodiments of the present disclosure are not limited to the weights of the 45° and 135° scales.

[0106] In a certain sense, some embodiments of the present disclosure essentially provide a method of affecting the iteration of the model, so as to guide the model to approach the direction with close model error in the oblique direction and the horizontal direction during the iteration. Finally, after the iteration is completed, the model meeting the predetermined condition can be selected as the calibrated OPC model. For example, the model with close model error of the three scales can be selected from the iteration results with the minimum cost function in the last few rounds as the OPC model, so as to solve the problem of square hole effect.

[0107] Some embodiments of the present disclosure provide a calibration method of an optical proximity correction model. It should be noted that the examples in the above embodiments are only for illustrating the schemes of the embodiments of the present disclosure, and are not used to limit the schemes of the present disclosure.

[0108] By means of the present application, the OPC prediction result is a circle by additionally setting the scales of other directions. This is consistent with the pattern on the wafer obtained by SEM. In some embodiments of the present application, the error direction in the parameter iteration of the correction model is corrected by introducing the scales of different directions, so that the model can take into account the pattern error in each direction, thereby achieving the purpose of realizing the accuracy of the model.

[0109] In some embodiments of the present application, by introducing wafer CD data in non-horizontal / vertical directions and increasing the weight, the iteration of the model parameters takes into account more directions of the model prediction error, thereby avoiding the generation of the model prediction square hole behavior, and obtaining an accurate prediction wafer pattern model.

[0110] It should be understood that the embodiments shown in the drawings are only for illustrative purposes to show the schemes of some embodiments of the present application, and are not intended to limit the present application. The embodiments of the present application can also have various other forms.

[0111] In some embodiments of the present application, an electronic device is also disclosed. The electronic device includes a processor and a memory coupled to the processor, the memory having stored therein instructions which, when executed by the processor, cause the device to perform actions including: determining a value of a cost function of an optical proximity correction (OPC) model based on a simulated pattern of a predetermined pattern in a layout and a wafer image corresponding to the predetermined pattern, wherein scales set along a first direction of the predetermined pattern and a second direction non-perpendicular to the first direction are given a greater weight than scales of remaining directions, the first direction being a horizontal or vertical direction; iteratively updating the OPC model based on the value of the cost function to update model parameters; and in response to the iteration satisfying a predetermined condition, determining the OPC model whose difference between an error in the first direction and an error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is below a threshold value as a calibrated OPC model.

[0112] In some embodiments of the present application, a computer readable storage medium having stored thereon machine executable instructions which, when executed by a processor, implement a calibration method of an optical proximity correction model according to embodiments of the present application.

[0113] Figure 8Schematic block diagrams of electronic devices according to some exemplary embodiments of the present disclosure are shown. The electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0114] like Figure 8 As shown, device 800 includes a CPU 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. CPU 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0115] Multiple components in device 800 are connected to I / O interface 805. These components include: input unit 806, such as a keyboard and mouse; output unit 807, such as various types of displays and speakers; storage unit 808, such as a disk and optical disk; and communication unit 809, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] The various processes and procedures described above, such as method 400, can be executed by CPU 801. For example, in some embodiments, method 400 can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by CPU 801, one or more steps of method 400 described above can be performed.

[0117] The schemes according to embodiments of the present disclosure can be a method, an apparatus, a system, and / or a computer program product. A computer program product can include a computer readable storage medium, having instructions, executable by one or more processors, stored thereon. The computer readable storage medium can be a tangible device that can retain and store instructions for execution by the one or more processors. The computer readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, or a combination of one or more of them. The computer readable program instructions can be downloaded to one or more computing / processing devices from a computer readable storage medium or to external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network.

[0118] The above has described the embodiments of the present disclosure, the above description is exemplary, only optional embodiments of the present disclosure, is not exhaustive, and is not used to limit the present disclosure. Although the claims in the present application have been made against the specific combination of features, it should be understood that the scope of the present disclosure also includes any novel features or any novel combination of features disclosed herein, whether it is related to the same scheme in any of the presently claimed claims or not. It should be understood that new claims can be made in the examination process of the present application or any further application derived therefrom as these features and / or combinations of these features.

[0119] The choice of words used herein is intended to best explain the principles of the embodiments, practical application, or technical improvement in the art, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein. The present disclosure can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A calibration method for an optical proximity correction model, comprising: The cost function of the optical proximity correction (OPC) model is determined based on the simulated pattern of a predetermined pattern in the layout and the wafer image corresponding to the predetermined pattern, wherein scales set along a first direction of the predetermined pattern and a second direction that is not perpendicular to the first direction are given greater weight than scales in other directions, the first direction being either horizontal or vertical. The OPC model is iterated based on the value of the cost function to update the model parameters; as well as In response to the iteration satisfying predetermined conditions, the corresponding OPC model is determined as a calibrated OPC model.

2. The method of claim 1, wherein determining the value of the cost function of the optical proximity correction (OPC) model based on a simulated graphic of a predetermined pattern in the layout and a wafer image corresponding to the predetermined pattern comprises: Determine the error between the simulated critical dimension of the predetermined pattern at the position indicated by each scale and the corresponding critical dimension on the wafer image; as well as The value of the cost function is determined based on the weights of each of the errors and each of the scales.

3. The method of claim 2, wherein determining the value of the cost function based on the weights of each of the errors and each of the scales comprises: Determine the square value of each of the aforementioned errors; The squared values ​​are weighted and averaged based on the weights of each scale to determine the weighted average. as well as The weighted average value is determined as the value of the cost function.

4. The method of claim 3, wherein determining the value of the cost function based on the weights of each of the errors and each of the scales comprises determining the value of the cost function based on the following formula: ； Where cost_function_wt represents the cost function; simulation_CDj represents the simulation critical dimension of the predetermined pattern; wafer_CDj represents the corresponding critical dimension of the lithographic pattern j on the wafer, wt j This represents the weight of the scale in each direction within each predetermined graphic, where n is the number of predetermined graphics.

5. The method according to claim 1, wherein: During the iteration process, the scale set along the predetermined pattern in a third direction that is not perpendicular to the first direction is also given a greater weight than the scales in the other directions besides the first and second directions.

6. The method according to claim 5, wherein: The angle between the second direction and the first direction is 40° to 50°.

7. The method according to claim 6, wherein: The angle between the second direction and the first direction is 45°.

8. The method according to claim 6, wherein: The third direction is perpendicular to the second direction.

9. The method of claim 5, wherein determining the corresponding OPC model as a calibrated OPC model in response to the iteration satisfying a predetermined condition comprises: In response to the iteration satisfying a predetermined condition, an OPC model whose difference between the error in the first direction and the error in the second direction between the simulated graphic of the predetermined pattern and the wafer image is less than a threshold is determined as a calibrated OPC model.

10. The method according to claim 9, wherein: In response to the iteration satisfying a predetermined condition, an OPC model in which the difference between the simulated graphic of the predetermined pattern and the wafer image in the first direction, the second direction, and the third direction is all lower than the threshold is determined as a calibrated OPC model.

11. The method according to any one of claims 5 to 10, wherein: The weights of the first direction, the second direction, and the third direction are 50-300 times the weights of the other directions.

12. The method of claim 9, wherein the iteration satisfies a predetermined condition including: The value of the cost function is lower than a predetermined threshold; The number of iterations has reached the predetermined number; or The absolute value of the difference between the cost function values ​​of the two iterations is less than a predetermined difference threshold.

13. The method of claim 12, wherein determining an OPC model whose difference between the error in the first direction and the error in the second direction between the simulated pattern of the predetermined pattern and the wafer image is less than the threshold comprises: In response to the number of iterations reaching the predetermined number, an OPC model with model errors below a second threshold in the first and second directions is selected from the last predetermined number of iterations as the calibrated OPC model.

14. The method according to any one of claims 1 to 10, wherein the predetermined pattern is an anchor point.

15. An electronic device comprising: processor; as well as A memory coupled to a processor, containing instructions stored therein, which, when executed by the processor, cause the device to perform actions, including: The cost function of the optical proximity correction (OPC) model is determined based on the simulated pattern of a predetermined pattern in the layout and the wafer image corresponding to the predetermined pattern, wherein scales set along a first direction of the predetermined pattern and a second direction that is not perpendicular to the first direction are given greater weight than scales in other directions, the first direction being either horizontal or vertical. The OPC model is iterated based on the value of the cost function to update the model parameters; and In response to the iteration satisfying predetermined conditions, the corresponding OPC model is determined as a calibrated OPC model.

16. A computer-readable storage medium storing machine-executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 14.

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