Correction methods and data acquisition methods for optical proximity effect correction models
By classifying test patterns and combining different data acquisition methods, the complexity of data acquisition in optical proximity effect correction models when lithographic conditions change is solved, achieving an efficient and accurate modeling process and reducing costs and time.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NEXCHIP SEMICON CO LTD
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-03
AI Technical Summary
Existing optical proximity effect correction models require the recollection of a large amount of wafer data when lithography conditions change, resulting in complex, time-consuming, and costly data acquisition, which affects production efficiency and accuracy.
By screening test images, they are classified into two categories: low sensitivity and high sensitivity. The actual measured values of the key dimensions of low sensitivity images are determined by formula calculation, and the values of high sensitivity images are determined by actual exposure. Data is collected by combining formula calculation and actual exposure.
It simplifies the data acquisition process, reduces modeling costs and time, and improves the accuracy of data acquisition and the efficiency of modeling.
Smart Images
Figure CN122018227B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of semiconductor technology, and in particular to a correction method and data acquisition method for an optical proximity effect correction model. Background Technology
[0002] With the development of integrated circuits, semiconductor manufacturing technology is constantly evolving towards smaller dimensions. The feature size of semiconductor devices is even smaller than the wavelength of light used in photolithography. In this situation, due to the diffraction effect of light, the pattern on the mask will be deformed during transfer, a phenomenon known as the optical proximity effect. The optical proximity effect causes a significant difference between the actual pattern projected onto the wafer and the designed target pattern, thus affecting the photolithography quality of adjacent pattern areas on the mask, and consequently impacting circuit performance and production yield.
[0003] To eliminate the effects of optical proximity, optical proximity correction (OPC) is commonly used. This method uses computer software to correct the original pattern on the semiconductor substrate to be exposed on the silicon wafer, resulting in a target pattern that differs from the original. A photomask is then fabricated based on this target pattern. During photolithography, the pattern projected onto the semiconductor substrate using this photomask is almost identical to the original pattern, thus compensating for the problems caused by optical proximity.
[0004] Lithography conditions are an essential modeling parameter in OPC modeling. Changes in lithography conditions directly affect the reflectivity of the underlying layer of the light source, thus influencing the optical proximity effect. Currently, the common solution to the impact of lithography conditions on OPC modeling is to recollect the wafer data required for modeling whenever the light source changes. However, data collection and filtering are complex and time-consuming, which increases the accuracy of feedback data, prolongs correction time, and increases production costs. Summary of the Invention
[0005] One objective of this application is to provide a calibration method and data acquisition method for an optical proximity effect correction model, optimize and diversify the data acquisition methods during the modeling and calibration process of the optical proximity effect correction model, reduce the complexity of data acquisition, reduce the amount of data acquired, lower the modeling cost, and ensure the accuracy of the model.
[0006] Firstly, to achieve the above objectives, one embodiment of this application provides a correction method for an optical proximity effect correction model, comprising:
[0007] Under the initial information of photolithography conditions, the actual measured values of critical dimensions of the test pattern set after exposure on the wafer were determined, and an initial optical proximity effect correction model was established.
[0008] The initial information of the photolithography conditions is adjusted, and the first simulated value of the key dimension of the test pattern set under the initial information of the photolithography conditions and the second simulated value of the key dimension of the test pattern set under the adjusted information of the photolithography conditions are determined.
[0009] Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result; wherein, the first type of test graphics is the test graphics corresponding to the difference being within the threshold range, and the second type of test graphics is the test graphics corresponding to the difference not being within the threshold range.
[0010] Different data acquisition methods are used to re-collect the actual measurements of the critical dimensions of the first type of test pattern and the second type of test pattern to obtain collected data; wherein, the collected data are the actual measurements of the critical dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions.
[0011] Using the collected data, the initial optical proximity effect correction model is corrected to obtain the target optical proximity effect correction model.
[0012] Optionally, the photolithography conditions may include: the energy of the light, the focal point, or the light source.
[0013] Optionally, the information for adjusting the photolithography conditions may include: adjusting the shape information of the light source in the photolithography conditions.
[0014] Optionally, the threshold range may be less than a set value, which is 1.5 nm.
[0015] Optionally, the step of re-collecting the actual measurements of key dimensions for the first type of test graphics and the second type of test graphics using different data acquisition methods includes:
[0016] Using a preset formula, the actual measured values of the critical dimensions of the first type of test pattern after exposure on the wafer are calculated under the adjustment information of the photolithography conditions.
[0017] Using the actual exposure method, the actual measured values of the critical dimensions of the second type of test pattern after exposure on the wafer are determined under the adjustment information of the photolithography conditions.
[0018] Optionally, the preset formula can be:
[0019] A' = (A / a) * a';
[0020] Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
[0021] Secondly, to achieve the above objectives, one embodiment of this application provides a data acquisition method for an optical proximity effect correction model, comprising:
[0022] Provide a test pattern set to determine the actual measured values of critical dimensions after exposure on the wafer, given the initial information of the photolithography conditions.
[0023] The information of the photolithography conditions is adjusted, and the first simulated value of the key dimension of the test pattern set under the initial information of the photolithography conditions and the second simulated value of the key dimension of the test pattern set under the adjusted information of the photolithography conditions are determined.
[0024] Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result; wherein, the first type of test graphics is the test graphics corresponding to the difference being within the threshold range, and the second type of test graphics is the test graphics corresponding to the difference not being within the threshold range.
[0025] Different data acquisition methods are used to re-collect the actual measurements of the critical dimensions of the first type of test pattern and the second type of test pattern to obtain collected data; wherein, the collected data are the actual measurements of the critical dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions.
[0026] Optionally, the step of re-collecting the actual measurements of key dimensions for the first type of test graphics and the second type of test graphics using different data acquisition methods includes:
[0027] Using a preset formula, the actual measured values of the critical dimensions of the first type of test pattern after exposure on the wafer are calculated under the adjustment information of the photolithography conditions.
[0028] Using the actual exposure method, the actual measured values of the critical dimensions of the second type of test pattern after exposure on the wafer are determined under the adjustment information of the photolithography conditions.
[0029] Optionally, the preset formula can be:
[0030] A' = (A / a) * a';
[0031] Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
[0032] Thirdly, the present invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor, when executing the program stored in the memory, implements the correction method steps or data acquisition method steps of the optical proximity effect correction model as described above.
[0033] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the correction method steps or data acquisition method steps of the optical proximity effect correction model as described above.
[0034] Compared with the prior art, the technical solution provided by the present invention has at least one of the following beneficial effects:
[0035] The correction method and data acquisition method for the optical proximity effect correction model provided in this application both include: determining the actual measured values of the critical dimensions of the test pattern set after exposure on the wafer under the initial information of the photolithography conditions; adjusting the information of the photolithography conditions and determining the first simulated value of the critical dimensions of the test pattern set under the initial information of the photolithography conditions, and the second simulated value of the critical dimensions of the test pattern set under the adjusted information of the photolithography conditions; determining whether the difference between the first simulated value and the second simulated value corresponding to each test pattern in the test pattern set is within a threshold range, and classifying multiple test patterns in the test pattern set based on the determination result; and then re-collecting the actual measured values of the critical dimensions for the classified test patterns using different data acquisition methods.
[0036] In this application, on the one hand, by comparing the first and second simulated values of the key dimensions of each test pattern in the test pattern set under the initial information and the adjusted information (the changed lithography conditions) of the lithography conditions, test patterns (the first type of test patterns) whose key dimensions change less or within an acceptable threshold range with the change of lithography conditions are selected. Then, the actual measured values of the key dimensions of the selected first type of test patterns after exposure on the wafer are determined using the calculation formula proposed in this application. An unexpected effect is that a new data acquisition method for the actual measured values of key dimensions is proposed, and the actual measured values are determined by using formula calculation instead of actual exposure on the wafer. This saves the cost of acquiring the actual data of the selected first type of test patterns, reduces the computational load of data acquisition, and reduces the modeling cost.
[0037] On the other hand, for test patterns (the second type of test patterns) where the key dimensions change significantly or exceed the acceptable threshold range due to changes in photolithography conditions, this application still adopts the method of actual exposure on the wafer to collect actual test values of the key dimensions. An unexpected effect is that by combining formula calculation and actual exposure, the accuracy of data acquisition during the modeling process of optical proximity effect correction is ensured, while the calculation of some actual data acquisition is simplified by formula calculation, thereby reducing labor costs and improving modeling efficiency. Attached Figure Description
[0038] The accompanying drawings provide a more in-depth understanding of embodiments of this application and are incorporated herein by reference as a whole. These drawings and descriptions are used to illustrate the principles of some embodiments. It should be noted that all drawings are schematic diagrams and are for illustrative and drawing convenience, and relative sizes and proportions have been adjusted. The same symbols represent corresponding or similar features in different embodiments.
[0039] Figure 1 The diagram illustrates a flowchart of the correction method for the optical proximity effect correction model in an embodiment of this application.
[0040] Figure 2 illustrates the use of Figure 1 The correction method of the optical proximity effect correction model shown in the figure yields a comparison curve of the critical dimensions (proposed structure) of the first type of test pattern and the critical dimensions of the first type of test pattern determined by the actual exposure method of the existing technology.
[0041] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0042] To make the technical solutions and advantages of the embodiments of this application clearer, the technical solutions of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Although exemplary implementation methods of this application are shown in the accompanying drawings, it should be understood that this application can be implemented in various forms and should not be limited to the implementation methods described herein. Rather, these implementation methods are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.
[0043] The present application is described in more detail below by way of example with reference to the accompanying drawings. The advantages and features of the present application will become clearer from the following description and claims. It should be noted that the drawings are in a very simplified form and use non-precise proportions, intended only to facilitate and clarify the illustration of the embodiments of the present application. It is understood that the terms "on," "above," and "over" in this application should be interpreted in the broadest sense, such that "on" means not only "on" something without any intervening feature or layer (i.e., directly on something), but also includes "on" something with an intervening feature or layer.
[0044] Furthermore, for ease of description, regional relative terms such as “on,” “above,” “above,” “upper,” “above,” “upper,” etc., may be used herein to describe the relationship between one element or feature and another element or feature as shown in the figures. In addition to the orientations depicted in the figures, regional relative terms are intended to cover different orientations of the device in use or operation. The device may be oriented in other ways (rotated 90 degrees or in other orientations) and the regional relative descriptive terms used herein may be interpreted accordingly.
[0045] In the embodiments of this application, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be noted that the technical solutions described in the embodiments of this application can be arbitrarily combined without conflict.
[0046] In the process of manufacturing semiconductor chips, photomasks are used to form patterns on semiconductors using photolithography. To replicate these patterns onto a wafer, an integrated circuit photolithography machine is used to photo-etch the projected circuitry. The manufacturing process generally includes exposure, development, removal of photoresist, and photolithography. The general process of photolithography is as follows: first, a specific pattern structure is obtained on a photomask; then, the pattern on the photomask is copied onto the silicon wafer using photolithography equipment. However, the process of creating patterns through photolithography introduces some degree of distortion, especially as linewidths decrease, the distortion becomes increasingly severe. Typical examples include rounded corners or shortened line ends. These phenomena are caused by the optical proximity effect (OPE), which is caused by nonlinear filtering in the optical imaging system. The industry addresses this problem using optical proximity correction (OPC) technology. In OPC, the pattern on the integrated circuit photomask is pre-corrected to compensate for the distortion caused by the photolithography process, ensuring that the corrected pattern, after photolithography, achieves the pre-designed pattern structure.
[0047] The existing optical proximity correction model (hereinafter referred to as the OPC model) requires data collection during its establishment process. The model is then adjusted and optimized based on the collected data before the mask can be repaired. The most time-consuming part of the modeling process is data collection and processing. Currently, data collection primarily utilizes measurement instruments and measurement tools from Applied Materials to identify the measurement positions and boundaries of the test pattern. This is followed by the offline establishment of CD-SEM measurement standards, which are then used to measure the actual wafer dimensions online (i.e., the actual measured values of key dimensions after the test pattern is exposed on the wafer), thus obtaining the collected data. In the calibration process of existing optical proximity effect correction models, if the lithography conditions change, the wafer data required for modeling needs to be collected again. As the integration density of integrated circuits increases, the amount of wafer data required for the modeling and calibration of optical proximity effect correction models is also increasing. For example, the number of wafer data required for modeling the layout corresponding to semiconductor devices at the 28-nanometer node can currently reach tens of thousands. This makes the collection and screening of data during modeling and calibration complex and time-consuming, which invisibly increases the accuracy of feedback data, seriously delays the research and development progress, and ultimately prolongs the correction time and increases production costs.
[0048] To address this issue, this application proposes a correction method and data acquisition approach for an optical proximity effect correction model. By screening test patterns, some patterns are identified where formula calculations can replace actual data acquisition, thus accurately determining the actual test values of critical dimensions of the test patterns exposed on the wafer. Then, the calculation formula proposed for the first time in this application is used to determine the actual measured values of the critical dimensions of these test patterns. In other words, the actual measured values of the critical dimensions of the screened test patterns are determined using fast, convenient, and low-cost computer software. Other test patterns that are not screened are still acquired using existing data acquisition methods. By combining formula calculations with actual exposure, the accuracy of data acquisition during the optical proximity effect correction model modeling process is ensured, while the calculations for some actual data acquisitions are simplified using formula calculations, thereby reducing labor costs and improving modeling efficiency.
[0049] The correction method of the optical proximity effect correction model in this application will be further described below with reference to the accompanying drawings and embodiments.
[0050] Please refer to Figure 1 The illustration shows a flowchart of a correction method for an optical proximity effect correction model according to an embodiment of the present invention. Figure 1 As shown, the correction method for the optical proximity effect correction model may include at least the following steps:
[0051] Step S101: Determine the actual measured values of critical dimensions of the test pattern set after exposure on the wafer under the initial information of the photolithography conditions, and establish an initial optical proximity effect correction model.
[0052] Step S102: Adjust the information of the photolithography conditions, and determine the first simulated value of the key dimensions of the test pattern set under the initial information of the photolithography conditions, and the second simulated value of the key dimensions of the test pattern set under the adjusted information of the photolithography conditions.
[0053] Step S103: Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result. The first type of test graphics refers to the test graphics whose difference is within the threshold range, and the second type of test graphics refers to the test graphics whose difference is not within the threshold range.
[0054] Step S104: Using different data acquisition methods, the actual measurement values of the key dimensions of the first type of test pattern and the second type of test pattern are re-collected to obtain collected data; wherein, the collected data are the actual measurement values of the key dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions.
[0055] Step S105: Using the collected data, the initial optical proximity effect correction model is corrected to obtain the target optical proximity effect correction model.
[0056] In step S101 above, a set of test patterns (multiple test patterns) can be provided first. For example, this application refers to the multiple test patterns as a test pattern set. Then, based on the lithography condition information in the existing OPC model modeling process, the actual measured values of the critical dimensions of each test pattern in the test pattern set after exposure on the wafer are determined (this data can also be called basic data or historical data). Subsequently, an initial optical proximity effect correction model can be established based on the basic data or historical data.
[0057] It should be specifically noted that the information regarding the lithography conditions (basic parameters for modeling) used in establishing the initial optical proximity effect correction model is referred to in this application as initial information, i.e., the standard lithography condition information currently used in existing technology modeling. Specifically, the lithography conditions can be the actual process conditions in lithography, such as the energy, focus, or light source of the illumination, which can be set by each manufacturer according to their own machine tool conditions. The light source specifically refers to the type of light source, such as its shape. Since the intensity of the light source changes with its shape, this change inevitably leads to a change in the OPC model. Therefore, when the shape of the light source changes, existing technologies require re-collecting actual test values for each test pattern in the test pattern set. This results in long data acquisition times, which invisibly increases the accuracy of the feedback data, prolongs the correction time, and increases production costs.
[0058] In steps S102 and S103 above, in view of the impact of the change in the shape of the light source in the lithography conditions on the data acquisition during the OPC model modeling process, after establishing the initial proximity effect correction model, this application does not re-acquire the actual measured values of the key dimensions of all test graphics in the test graphic set, but first classifies the test graphics in the test graphic set.
[0059] The specific method is as follows: Adjust the initial information of the lithography conditions, for example, change the shape of the light source in the lithography conditions when establishing the initial proximity effect correction model in step S101, so as to simulate / approximate the influence of actual lithography process fluctuations on OPC modeling / correction / data acquisition, that is, obtain the adjustment information of the lithography conditions; then use optical software to calculate / determine the first simulated value of the key dimension of each test pattern in the test pattern set under the initial information of the lithography conditions, and the second simulated value of the key dimension of each test pattern under the adjustment information of the lithography conditions.
[0060] Next, for each test pattern, the difference between the first simulated value of the critical dimension under the initial information of the photolithography conditions and the second simulated value of the critical dimension under the adjusted information of the photolithography conditions is compared. For example, it is compared whether the difference between the first simulated value and the second simulated value is within a threshold range (preferably less than or equal to 1.5 nm). If so, it indicates that when the shape of the light source changes in the actual photolithography process, the measured value of the corresponding critical dimension exposed on the wafer changes little or negligibly. Thus, this application classifies such test patterns as the first type of test patterns, that is, test patterns corresponding to the difference between the first simulated value and the second simulated value within the threshold range (difference less than or equal to 1.5 nm). The remaining test patterns, that is, test patterns corresponding to the difference between the first simulated value and the second simulated value not being within the threshold range (difference greater than 1.5 nm), are called the second type of test patterns. Obviously, the second type of test patterns in this application are test patterns that have a significant impact on the measured value of the corresponding critical dimension exposed on the wafer when the shape of the light source changes in the actual photolithography process.
[0061] In step S104 above, it can be seen from the classification of the test patterns in the test pattern set in step S103 that the first type of test patterns have low sensitivity to changes in the shape of the light source. Therefore, in the process of OPC model modeling / correction / data acquisition, it is not necessary to expose the first type of test patterns on the wafer to determine the actual measured value of its key dimensions. In this regard, this application proposes an acquisition method that can reduce acquisition cost, data acquisition computation, and modeling cost, namely, directly calculating using a preset formula.
[0062] Preferably, the preset formula can be:
[0063] A' = (A / a) * a';
[0064] Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
[0065] Obviously, this application uses "A / a" to represent the variation / pattern distortion coefficient of the key dimension of the test pattern on the wafer during the optical imaging process; therefore, the "A / a" coefficient of the first type of test pattern does not change with the change of the light source shape, that is, it is a constant value; therefore, the actual measured value A' of the key dimension of the first type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions can be directly determined based on the second simulation value without actual acquisition.
[0066] For the second type of test pattern, the actual measured values of the critical dimensions exposed on the wafer vary with the shape of the light source under the lithography conditions, resulting in an "A / a" coefficient that cannot be directly calculated using a formula. Therefore, to ensure the accuracy of OPC modeling / calibration / data acquisition, this application still uses actual exposure on the wafer for the second type of test pattern to determine the actual measured values of the critical dimensions under the adjusted lithography conditions. This achieves the goal of screening and classifying test patterns, allowing the actual measured values of the critical dimensions of some test patterns under the adjusted lithography conditions to be calculated using formulas instead of actual exposure on the wafer. This saves on the actual data acquisition costs of the first type of test patterns, reduces the computational load of data acquisition, and lowers the modeling costs.
[0067] In step S105 above, the data of the first type of test pattern calculated by the formula in step S104 and the data of the second type of test pattern determined by the actual exposure method can be merged to obtain the collected data. Then, the initial optical proximity effect correction model established in step S101 is corrected to finally obtain the target optical proximity effect correction model.
[0068] In addition, other embodiments of this application also provide a data acquisition method for an optical proximity effect correction model, which may specifically include the following steps:
[0069] Step S201: Provide a test pattern set and determine the actual measured values of critical dimensions of the test pattern set after exposure on the wafer under the initial information of the photolithography conditions.
[0070] Step S202: Adjust the information of the photolithography conditions, and determine the first simulated value of the key dimensions of the test pattern set under the initial information of the photolithography conditions, and the second simulated value of the key dimensions of the test pattern set under the adjusted information of the photolithography conditions.
[0071] Step S203: Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result; wherein, the first type of test graphics is the test graphics corresponding to the difference within the threshold range, and the second type of test graphics is the test graphics corresponding to the difference not within the threshold range.
[0072] Step S204: Using different data acquisition methods, the actual measurement values of the key dimensions of the first type of test pattern and the second type of test pattern are re-collected to obtain collected data; wherein, the collected data are the actual measurement values of the key dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions.
[0073] The step of re-collecting the actual measurement values of the key dimensions of the first type of test pattern and the second type of test pattern using different data acquisition methods includes: calculating the actual measurement values of the key dimensions of the first type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions using a preset formula; and determining the actual measurement values of the key dimensions of the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions using the actual exposure method.
[0074] Preferably, the preset formula can be:
[0075] A' = (A / a) * a';
[0076] Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
[0077] Please see Figure 2 Figure 2 illustrates the use of Figure 1 The correction method of the optical proximity effect correction model shown in the figure yields a comparison curve of the critical dimensions (proposed structure) of the first type of test pattern and the critical dimensions of the first type of test pattern determined by the actual exposure method of the existing technology.
[0078] like Figure 2 As shown, the key dimensions of the first type of test pattern calculated using the preset formula proposed in this application are compared with those collected by CDSEM. The results show that the maximum bias between the two is 1.5 nm, with approximately 1% exceeding ±1 nm, and 99% of the data bias remaining within ±1 nm. Therefore, the correction method and data acquisition method of the optical proximity effect correction model proposed in this application, which utilizes formula calculation instead of actual exposure on the wafer to determine the actual measured values, can indeed reduce the actual data acquisition cost, computational load, and modeling cost of the first type of test pattern. Figure 2 In the text, “gauge-1~gauge-51” represent the 1st to the 51st test images of the first type of test, respectively.
[0079] In summary, the correction method and data acquisition method for the optical proximity effect correction model provided in this application both include: determining the actual measured values of the critical dimensions of the test pattern set after exposure on the wafer under the initial information of the photolithography conditions; adjusting the information of the photolithography conditions and determining the first simulated value of the critical dimensions of the test pattern set under the initial information of the photolithography conditions, and the second simulated value of the critical dimensions of the test pattern set under the adjusted information of the photolithography conditions; determining whether the difference between the first simulated value and the second simulated value corresponding to each test pattern in the test pattern set is within a threshold range, and classifying multiple test patterns in the test pattern set based on the determination result; and then re-collecting the actual measured values of the critical dimensions for the classified test patterns using different data acquisition methods. In this application, on the one hand, by comparing the first and second simulated values of the key dimensions of each test pattern in the test pattern set under the initial information and the adjusted information (the changed lithography conditions) of the lithography conditions, test patterns (the first type of test patterns) whose key dimensions change less or within an acceptable threshold range with the change of lithography conditions are selected. Then, the actual measured values of the key dimensions of the selected first type of test patterns after exposure on the wafer are determined using the calculation formula proposed in this application. An unexpected effect is that a new data acquisition method for the actual measured values of key dimensions is proposed, and the actual measured values are determined by using formula calculation instead of actual exposure on the wafer. This saves the cost of acquiring the actual data of the selected first type of test patterns, reduces the computational load of data acquisition, and reduces the modeling cost.
[0080] On the other hand, for test patterns (the second type of test patterns) where the key dimensions change significantly or exceed the acceptable threshold range due to changes in photolithography conditions, this application still adopts the method of actual exposure on the wafer to collect actual test values of the key dimensions. An unexpected effect is that by combining formula calculation and actual exposure, the accuracy of data acquisition during the modeling process of optical proximity effect correction is ensured, while the calculation of some actual data acquisition is simplified by formula calculation, thereby reducing labor costs and improving modeling efficiency.
[0081] This invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The memory is used to store computer programs. When the processor executes the program stored in the memory, it implements a correction method or data acquisition method for an optical proximity effect correction model provided in this invention.
[0082] In addition, other implementations of the OPC modeling method implemented by the processor executing the program stored in the memory are the same as those mentioned in the aforementioned method embodiment section, and will not be repeated here.
[0083] The communication bus mentioned in the control terminal above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.
[0084] The communication interface is used for communication between the aforementioned electronic devices and other devices.
[0085] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.
[0086] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0087] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform a correction method or data acquisition method for an optical proximity effect correction model as described in any of the above embodiments.
[0088] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).
[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0090] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and computer-readable storage media are basically similar to the method embodiments, and therefore the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.
Claims
1. A correction method for an optical proximity effect correction model, characterized in that, include: Under the initial information of photolithography conditions, determine the actual measured values of the critical dimensions of the test pattern set after exposure on the wafer, and establish an initial optical proximity effect correction model; The initial information of the photolithography conditions is adjusted, and the first simulated value of the key dimension of the test pattern set under the initial information of the photolithography conditions and the second simulated value of the key dimension of the test pattern set under the adjusted information of the photolithography conditions are determined. Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result; wherein, the first type of test graphics is the test graphics corresponding to the difference being within the threshold range, and the second type of test graphics is the test graphics corresponding to the difference not being within the threshold range; Different data acquisition methods are used to re-collect the actual measurement values of the key dimensions of the first type of test pattern and the second type of test pattern to obtain collected data; wherein, the collected data are the actual measurement values of the key dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions. Using the collected data, the initial optical proximity effect correction model is corrected to obtain the target optical proximity effect correction model; The step of re-collecting the actual measurements of key dimensions for the first type of test graphics and the second type of test graphics using different data acquisition methods includes: Using a preset formula, the actual measured values of the critical dimensions of the first type of test pattern after exposure on the wafer are calculated under the adjustment information of the photolithography conditions. Using the actual exposure method, the actual measured values of the critical dimensions of the second type of test pattern after exposure on the wafer are determined under the adjustment information of the photolithography conditions. Furthermore, the preset formula is: A' = (A / a) * a'; Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
2. The correction method as described in claim 1, characterized in that, The photolithography conditions include: the energy of the light, the focal point, or the light source.
3. The correction method as described in claim 2, characterized in that, The information for adjusting the photolithography conditions includes: adjusting the shape information of the light source in the photolithography conditions.
4. The correction method as described in claim 1, characterized in that, The threshold range is less than a set value, which is 1.5 nm.
5. A data acquisition method for an optical proximity effect correction model, characterized in that, include: Provide a test pattern set to determine the actual measured values of critical dimensions of the test pattern set after exposure on the wafer, under the initial information of the photolithography conditions; Adjust the information of the photolithography conditions, and determine the first simulated value of the key dimension of the test pattern set under the initial information of the photolithography conditions, and the second simulated value of the key dimension of the test pattern set under the adjusted information of the photolithography conditions; Determine whether the difference between the first simulation value and the second simulation value corresponding to each test graphic in the test graphic set is within a threshold range, and classify the multiple test graphics in the test graphic set into a first type of test graphics and a second type of test graphics based on the determination result; wherein, the first type of test graphics is the test graphics corresponding to the difference being within the threshold range, and the second type of test graphics is the test graphics corresponding to the difference not being within the threshold range; Different data acquisition methods are used to re-collect the actual measurement values of the key dimensions of the first type of test pattern and the second type of test pattern to obtain collected data; wherein, the collected data are the actual measurement values of the key dimensions of the first type of test pattern or the second type of test pattern after exposure on the wafer under the adjustment information of the photolithography conditions. The step of re-collecting the actual measurements of key dimensions for the first type of test graphics and the second type of test graphics using different data acquisition methods includes: Using a preset formula, the actual measured values of the critical dimensions of the first type of test pattern after exposure on the wafer are calculated under the adjustment information of the photolithography conditions. Using the actual exposure method, the actual measured values of the critical dimensions of the second type of test pattern after exposure on the wafer are determined under the adjustment information of the photolithography conditions. Furthermore, the preset formula is: A' = (A / a) * a'; Wherein, A' is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the adjustment information of photolithography conditions, A is the actual measured value of the critical dimension of the first type of test pattern after exposure on the wafer under the initial information of photolithography conditions, a' is the second simulated value of the critical dimension of the first type of test pattern under the adjustment information of photolithography conditions, and a is the first simulated value of the critical dimension of the first type of test pattern under the initial information of photolithography conditions.
6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the correction method as described in any one of claims 1 to 4, or the steps of the data acquisition method as described in claim 5.