Information processing method and device, electronic equipment and storage medium

Abstract

The present disclosure provides an information processing method and device, electronic equipment and storage medium, which can be applied to the field of high-resolution imaging technology. The method comprises: processing development pattern information of a photolithography system by using an information relationship model to obtain target parameter fluctuation range information, wherein the information relationship model is used to represent a first correlation between the development pattern information and the target parameter fluctuation range information, the target parameter fluctuation range information is used to represent a target fluctuation range of a system parameter of the photolithography system, and the development pattern information represents pattern information obtained by developing a photoresist by the photolithography system; verifying parameter information of a plurality of system parameters of the photolithography system according to the target parameter fluctuation range information to obtain a parameter verification result; determining a target system parameter represented by the parameter verification result as being abnormal from the plurality of system parameters; and determining a target module from the photolithography system according to the target system parameter.

Description

Technical Field

[0001] This disclosure relates to the field of high-resolution imaging technology, and more particularly to an information processing method, apparatus, electronic device, medium, and program product. Background Technology

[0002] Photolithography is one of the steps in integrated circuit manufacturing. It helps to transfer patterns, thereby enabling specific circuit functions.

[0003] In the process of realizing the inventive concept disclosed herein, the inventors discovered that in related technologies, the efficiency and accuracy of determining system parameters with abnormal fluctuations are low. Summary of the Invention

[0004] In view of the above problems, this disclosure provides information processing methods, apparatus, electronic devices, media and program products.

[0005] According to a first aspect of this disclosure, an information processing method is provided, comprising: processing development pattern information of a lithography system using an information relationship model to obtain target parameter fluctuation range information, wherein the information relationship model is used to characterize a first correlation between the development pattern information and the target parameter fluctuation range information, the target parameter fluctuation range information is used to characterize the target fluctuation range of system parameters of the lithography system, and the development pattern information characterizes the pattern information obtained by the lithography system developing photoresist; verifying the parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information to obtain parameter verification results; determining, from the multiple system parameters, the target system parameter characterized by the parameter verification result as an anomaly; and determining a target module from the lithography system based on the target system parameter.

[0006] According to embodiments of this disclosure, the information relationship model is constructed through the following operations: an initial information relationship model is constructed based on parameter information, light intensity distribution information, and development pattern information, wherein the light intensity distribution information is used to characterize the light intensity distribution of the lithography system, the light intensity distribution information does not include higher-order light intensity minors, and the order of the higher-order light intensity minors is at least second; the initial information relationship model is processed based on the predetermined development pattern information to obtain the information relationship model.

[0007] According to embodiments of this disclosure, processing an initial information relationship model based on predetermined development pattern information to obtain an information relationship model includes: fitting the model parameters of the initial information relationship model based on the predetermined development pattern information to obtain the information relationship model.

[0008] According to embodiments of this disclosure, the light intensity distribution information includes target light intensity distribution information and light intensity fluctuation information. The target light intensity distribution information is the light intensity distribution information obtained when the parameter values ​​of the system parameters do not fluctuate. The light intensity fluctuation information is used to characterize the light intensity distribution fluctuation of the lithography system when the parameter values ​​of at least one of the multiple system parameters fluctuate. The parameter information includes first parameter information and second parameter information. The first parameter information corresponds to the system parameters under the condition of no fluctuation, and the second parameter information corresponds to the system parameters under the condition of fluctuation.

[0009] According to embodiments of this disclosure, an initial information relationship model is constructed based on parameter information, light intensity distribution information, and development pattern information, including: obtaining a first sub-relationship model and a second sub-relationship model, wherein the first sub-relationship model is used to characterize a second correlation between the first parameter information and the development pattern information corresponding to the target light intensity distribution information, the second sub-relationship model is used to characterize a third correlation between the second parameter information and the development pattern change information corresponding to the light intensity fluctuation information, and the development pattern change information is used to characterize the change status of the development pattern information of the lithography system under the condition of fluctuation in the parameter values ​​of the system parameters; and an initial information relationship model is obtained based on the first sub-relationship model and the second sub-relationship model.

[0010] According to embodiments of this disclosure, the target parameter fluctuation range information includes a first parameter fluctuation upper limit value and a first parameter fluctuation lower limit value, and the second parameter information includes a second parameter fluctuation upper limit value and a second parameter fluctuation lower limit value. Based on the target parameter fluctuation range information, the parameter information of multiple system parameters of the lithography system is verified to obtain parameter verification results, including: verifying the second parameter fluctuation upper limit value according to the first parameter fluctuation upper limit value to obtain a first parameter verification result; verifying the second parameter fluctuation lower limit value according to the first parameter fluctuation lower limit value to obtain a second parameter verification result; and obtaining the parameter verification result based on the first parameter verification result and the second parameter verification result.

[0011] According to embodiments of this disclosure, processing the development pattern information of a lithography system using an information relationship model to obtain target parameter fluctuation range information includes: obtaining target development pattern information based on the development pattern information and predetermined development tolerance information; and processing the target development pattern information using an information relationship model to obtain parameter fluctuation range information.

[0012] A second aspect of this disclosure provides an information processing apparatus, comprising: a first processing module, configured to process development pattern information of a lithography system using an information relationship model to obtain target parameter fluctuation range information, wherein the information relationship model is used to characterize a first correlation between the development pattern information and the target parameter fluctuation range information, the target parameter fluctuation range information is used to characterize the target fluctuation range of system parameters of the lithography system, and the development pattern information characterizes the pattern information obtained by the lithography system developing photoresist; a verification module, configured to verify parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information to obtain parameter verification results; a first determination module, configured to determine, from the multiple system parameters, the target system parameter characterized by the parameter verification result as an anomaly; and a second determination module, configured to determine a target module from the lithography system based on the target system parameter.

[0013] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the methods described above.

[0014] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods described above.

[0015] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0016] According to the information processing method, apparatus, electronic device, medium, and program product provided in this disclosure, the system parameters of the lithography system are verified by utilizing the target parameter fluctuation range information. This avoids verifying system parameters based on only a single parameter value, expands the range of parameter values ​​used for system parameter verification, thereby avoiding inaccurate verification and improving the accuracy of determining the target system parameters. Furthermore, by using an information relationship model to process the development pattern information to obtain the target parameter fluctuation range information, the efficiency of obtaining the target parameter fluctuation range information is improved, thereby improving the efficiency of determining the target system parameters. Attached Figure Description

[0017] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 This diagram illustrates an application scenario of the information processing method according to an embodiment of the present disclosure.

[0019] Figure 2A flowchart illustrating an information processing method according to an embodiment of the present disclosure is shown schematically.

[0020] Figure 3 A flowchart illustrating an information processing method according to another embodiment of the present disclosure is shown schematically;

[0021] Figure 4 A schematic diagram illustrating the experimental conditions of an exploratory experiment according to an embodiment of the present disclosure is shown.

[0022] Figure 5 A schematic diagram illustrating deviation information of a pattern size model according to an embodiment of the present disclosure is shown.

[0023] Figure 6 A schematic diagram illustrating deviation information of a pattern offset model according to an embodiment of the present disclosure is shown.

[0024] Figure 7 A flowchart illustrating a method for verifying the fluctuation range of developing pattern information according to an embodiment of the present disclosure is shown schematically.

[0025] Figure 8 A schematic diagram illustrating the verification results of the fluctuation range of a graphical size model according to an embodiment of the present disclosure is shown.

[0026] Figure 9 A schematic diagram illustrating a comparison of the fluctuation range of target parameters according to an embodiment of the present disclosure is shown.

[0027] Figure 10 A schematic block diagram of an information processing apparatus according to an embodiment of the present disclosure is shown; and

[0028] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure. Detailed Implementation

[0029] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0032] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0033] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0034] For extreme ultraviolet (EUV) lithography, the lithography system employs a reflective structure. To avoid shading, light must be incident at an angle onto the mask and then reflected into the projection system, resulting in mask 3D effects. These mask 3D effects affect the critical dimension (CD) and pattern shift (PS) information, thus impacting interlayer alignment and consequently the implementation of specific functions in the circuit. Furthermore, for nodes of 5nm and below, fluctuations in the lithography system parameters may amplify the impact of mask 3D effects.

[0035] Based on this, the inventors discovered that due to the fluctuation of system parameters of the photolithography system and the coupling effect between system parameters, there is a problem that the size and offset of the pattern obtained by photolithography are difficult to control.

[0036] Furthermore, in order to ensure that the pattern obtained by photolithography has good graphic fidelity and accurate position, it is necessary to control the fluctuation range of parameters in the current photolithography system.

[0037] Based on this, the inventors discovered that in related technologies, the efficiency and accuracy of determining abnormally fluctuating system parameters are low.

[0038] Therefore, there is an urgent need for a method that can provide systematic guidance for system parameter control during the lithography system development stage and provide clear indicators for system parameter monitoring during the process production stage.

[0039] In view of this, embodiments of the present disclosure provide an information processing method, comprising: processing the development pattern information of a lithography system using an information relationship model to obtain target parameter fluctuation range information, wherein the information relationship model is used to characterize a first correlation between the development pattern information and the target parameter fluctuation range information, the target parameter fluctuation range information is used to characterize the target fluctuation range of the system parameters of the lithography system, and the development pattern information characterizes the pattern information obtained by the lithography system developing photoresist; verifying the parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information to obtain parameter verification results; determining the target system parameter characterized by the verification anomaly from the multiple system parameters; and determining the target module from the lithography system based on the target system parameter.

[0040] Figure 1 The diagram illustrates an application scenario of the information processing method according to an embodiment of the present disclosure.

[0041] like Figure 1 As shown, application scenario 100 according to this embodiment may include a lithography system 101 and a server 102. A network can be used as a medium to provide a communication link between the server 102 and the lithography system 101. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0042] Server 102 can collect information such as the development pattern of lithography system 101 via the network.

[0043] Server 102 can be a server that provides various services, such as a backend management server (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0044] It should be noted that the information processing method provided in this embodiment can generally be executed by server 102. Correspondingly, the information processing apparatus provided in this embodiment can generally be located in server 102. The information processing method provided in this embodiment can also be executed by a server or server cluster that is different from server 102 and capable of communicating with lithography system 101 and / or server 102. Correspondingly, the information processing apparatus provided in this embodiment can also be located in a server or server cluster that is different from server 102 and capable of communicating with lithography system 101 and / or server 102.

[0045] It should be understood that Figure 1 The number of servers and lithography systems shown is merely illustrative. Depending on implementation requirements, any number of servers and lithography systems can be used.

[0046] The following will be based on Figure 1 The described scene, through Figures 2-9 The information processing method of the disclosed embodiments will be described in detail.

[0047] Figure 2 A flowchart illustrating an information processing method according to an embodiment of the present disclosure is shown schematically.

[0048] like Figure 2 As shown, the information processing method of this embodiment includes operations S210 to S240.

[0049] In operation S210, the development pattern information of the lithography system is processed using the information relationship model to obtain the target parameter fluctuation range information. The information relationship model is used to characterize the first correlation between the development pattern information and the target parameter fluctuation range information. The target parameter fluctuation range information is used to characterize the target fluctuation range of the system parameters of the lithography system. The development pattern information characterizes the pattern information obtained by the lithography system developing the photoresist.

[0050] During operation S220, the parameter information of multiple system parameters of the lithography system is verified according to the target parameter fluctuation range information, and the parameter verification results are obtained.

[0051] In operation S230, the target system parameter that indicates the verification anomaly is determined from multiple system parameters.

[0052] In operation S240, the target module is determined from the lithography system based on the target system parameters.

[0053] According to embodiments of this disclosure, the lithography system may include an extreme ultraviolet (EUV) lithography system. The lithography system may include an illumination module and a projection module, etc. The target module may be a module in the lithography system corresponding to the target system parameters. For example, if the target system parameter is the pupil ellipticity in the y-direction, the illumination module can be determined as the target module.

[0054] The system parameters of a photolithography system may include exposure dose, pupil ellipticity x in the x direction, pupil ellipticity y in the y direction, pupil non-balance x in the x direction, pupil non-balance y in the y direction, polarization, slit position, numerical aperture (NA), and stray light (flare).

[0055] According to embodiments of this disclosure, the developed pattern information can be the actual pattern information obtained by developing photoresist using a photolithography system. The developed pattern information can include pattern linewidth information and pattern position offset information, etc. The pattern linewidth information can be used to characterize the pattern linewidth under parameter fluctuations. For example, the pattern position offset information can be used to characterize the pattern position offset under parameter fluctuations.

[0056] According to embodiments of this disclosure, the parameter information may include the actual system parameter values ​​of the lithography system. These parameter values ​​can be obtained under conditions of system parameter fluctuations. System parameter fluctuations refer to changes in the system parameter values ​​compared to their original values. The original parameter values ​​can be predetermined. For example, if the system parameter is 1, the system parameter value may fluctuate to values ​​close to 1, such as 0.9, 1.1, and 0.93. Multiple parameter values ​​corresponding to the system parameter can be collected under conditions of system parameter fluctuations. These multiple parameter values ​​may include the aforementioned values ​​such as 0.9, 1.1, and 0.93. Based on these multiple parameter values, the parameter information corresponding to the system parameter can be obtained.

[0057] According to embodiments of this disclosure, the target fluctuation range can be the fluctuation range of parameters under ideal conditions corresponding to the developed pattern information. The target parameter fluctuation range information can include the upper and lower limits of the system parameters of the aforementioned lithography system under ideal conditions.

[0058] According to embodiments of this disclosure, the developing pattern changes with parameter fluctuations. Therefore, the aforementioned information relationship model can be constructed based on a first correlation between the developing pattern information and the target parameter fluctuation range information. Based on this, the actual developing pattern information can be processed using the information relationship model to determine the target parameter fluctuation range information corresponding to the developing pattern information.

[0059] According to embodiments of this disclosure, the pattern linewidth information and pattern position offset information of the developed pattern can be input into an information relationship model, and the target parameter fluctuation range information can be output.

[0060] According to embodiments of this disclosure, it can be determined whether there are parameter values ​​in the parameter information that do not belong to the target parameter fluctuation range information.

[0061] If, among multiple parameter values, there is a parameter value that does not fall within the aforementioned target parameter fluctuation range information, it can be determined that the system parameter is abnormal, and thus a parameter verification result representing the abnormality of the system parameter can be obtained; if, among multiple parameter values ​​corresponding to the system parameter, there are parameter values ​​that fall within the aforementioned target parameter fluctuation range information, it can be determined that the system parameter is normal, and thus a parameter verification result representing the normality of the system parameter can be obtained.

[0062] According to embodiments of this disclosure, a target module corresponding to the type of the target system parameters can be queried based on the type of the target system parameters.

[0063] According to embodiments of this disclosure, by utilizing target parameter fluctuation range information to verify the system parameters of the lithography system, the method avoids verifying system parameters based solely on a single parameter value, thus expanding the range of parameter values ​​used for system parameter verification. This avoids inaccurate verification and improves the accuracy of determining target system parameters. Furthermore, by using an information relationship model to process the development pattern information to obtain target parameter fluctuation range information, the efficiency of obtaining target parameter fluctuation range information is improved, thereby enhancing the efficiency of determining target system parameters.

[0064] According to embodiments of this disclosure, the information relationship model is constructed through the following operations: An initial information relationship model is constructed based on parameter information, light intensity distribution information, and development pattern information. The light intensity distribution information characterizes the light intensity distribution of the lithography system, and does not include higher-order light intensity minors, which are at least second-order. The initial information relationship model is then processed based on the predetermined development pattern information to obtain the final information relationship model.

[0065] According to embodiments of this disclosure, the predetermined development pattern information may be development pattern information obtained experimentally that corresponds to a light intensity distribution including a small amount of higher-order light intensity.

[0066] According to embodiments of this disclosure, the initial information relationship model can be used to characterize a fourth correlation between parameter information, light intensity distribution information, and development pattern information.

[0067] According to embodiments of this disclosure, an association can be established between predetermined development pattern information corresponding to a light intensity distribution including higher-order light intensity fractions and an initial information relationship model excluding higher-order light intensity fractions, to obtain the aforementioned information relationship model.

[0068] According to embodiments of this disclosure, by using light intensity distribution information that does not include a small amount of high-order light intensity, and combining parameter information and development pattern information, an initial information relationship model is constructed, which reduces the amount of information processed in the process of constructing the initial information relationship model and improves the efficiency of constructing the initial information relationship model.

[0069] According to embodiments of this disclosure, processing an initial information relationship model based on predetermined development pattern information to obtain an information relationship model includes: fitting the model parameters of the initial information relationship model based on the predetermined development pattern information to obtain the information relationship model.

[0070] According to embodiments of this disclosure, a Monte Carlo experiment can be used to fit the model parameters in the initial information relationship model based on the predetermined development pattern information, thereby establishing the correlation between the predetermined development pattern information and the initial information relationship model, and obtaining the information relationship model.

[0071] According to embodiments of this disclosure, by fitting an initial information relationship model with the predetermined development pattern information corresponding to a light intensity distribution including a small amount of higher-order light intensity, the problem of inaccurate output results of an initial information relationship model that does not include a small amount of higher-order light intensity is avoided. Thus, an information relationship model with accurate output results can be obtained.

[0072] According to embodiments of this disclosure, the light intensity distribution information includes target light intensity distribution information and light intensity fluctuation information. The target light intensity distribution information is the light intensity distribution information obtained when the system parameter values ​​do not fluctuate. The light intensity fluctuation information is used to characterize the light intensity distribution fluctuation of the lithography system when at least one of the multiple system parameters fluctuates. The parameter information includes first parameter information and second parameter information. The first parameter information corresponds to the system parameters under the condition of no fluctuation, and the second parameter information corresponds to the system parameters under the condition of fluctuation.

[0073] According to embodiments of this disclosure, the first parameter information may include multiple first parameter values, which can characterize the system parameter values ​​under non-fluctuation conditions. The second parameter information may include multiple second parameter values, which can characterize the system parameter values ​​under fluctuating conditions.

[0074] According to embodiments of this disclosure, an initial information relationship model is constructed based on parameter information, light intensity distribution information, and development pattern information. This includes obtaining a first sub-relationship model and a second sub-relationship model. The first sub-relationship model characterizes a second association between the first parameter information and the development pattern information corresponding to the target light intensity distribution information. The second sub-relationship model characterizes a third association between the second parameter information and the development pattern change information corresponding to the light intensity fluctuation information. The development pattern change information characterizes the changes in the development pattern information of the lithography system under fluctuations in system parameter values. Based on the first and second sub-relationship models, the initial information relationship model is obtained.

[0075] According to embodiments of this disclosure, the development pattern information corresponding to the target light intensity distribution information can characterize the development pattern information of the photolithography system when the parameter values ​​of the system parameters do not fluctuate.

[0076] According to embodiments of this disclosure, the development pattern change information corresponding to light intensity fluctuation information can be used to characterize the change of the development pattern of the photolithography system under the condition of fluctuation in the parameter values ​​of the system parameters.

[0077] According to embodiments of this disclosure, an initial information relationship model is constructed by using a first sub-relationship model representing a second correlation between the first parameter information and the developing pattern information corresponding to the target light intensity distribution information, and a second sub-relationship model representing a third correlation between the second parameter information and the developing pattern change information corresponding to the light intensity fluctuation information. This avoids directly constructing the initial information relationship model based on the parameter information, light intensity distribution information, and developing pattern information, thereby reducing the amount of information required to construct the initial information relationship model and improving the efficiency of constructing the initial information relationship model.

[0078] To better understand the contents of this disclosure, specific embodiments are described below. The symbols, Chinese names, and meanings used in the following embodiments are shown in Table 1.

[0079] Table 1

[0080]

[0081] According to embodiments of this disclosure, the light intensity fluctuations caused by fluctuations in a single system parameter can be determined based on the Abbe imaging principle. Furthermore, the superposition nature of the light intensity fluctuations caused by each parameter in the case of fluctuations in multiple system parameters can be determined, thereby obtaining the aforementioned light intensity fluctuation information.

[0082] For example, based on the Abbe imaging principle, the light intensity distribution on the wafer after the mask passes through the photolithography system is determined. The light intensity distribution on the wafer is shown in formula (1):

[0083]

[0084] Here, x can represent the x-direction of the spatial coordinate axis. y can represent the y-direction of the spatial coordinate axis. z can represent the z-direction of the spatial coordinate axis. It can represent the normalized spatial frequency domain in the x-direction. It can represent the normalized spatial frequency domain in the y-direction. It can represent the effective light source function. It can represent the mask spectrum. It can represent a spatial transfer function. It can represent the distribution of light intensity at a location. For ease of representation, the function will be referred to as ... The expression is as follows: 'i' can represent an imaginary number.

[0085] Assuming the light source function With space transfer function Simultaneous fluctuations in light intensity distribution It can be shown in formula (2):

[0086]

[0087] in, This represents the initial light intensity distribution. It can represent the mask spectrum. It can represent the fluctuation of light source. The resulting fluctuations in light intensity. It can represent the effective light source function. It can represent a spatial transfer function. It can represent the fluctuation of the spatial transfer function. The resulting fluctuations in light intensity. It can represent The light intensity is a higher-order small quantity. i can represent an imaginary number. x can represent the x-direction of the spatial coordinate axis. y can represent the y-direction of the spatial coordinate axis. It can represent the normalized spatial frequency domain in the x-direction. It can represent the normalized spatial frequency domain in the y-direction.

[0088] Based on this, for a photolithography system without fluctuations, in the light source function With space transfer function When at least one of the equal functions undergoes a small change, the light intensity distribution It can be used to determine the target light intensity distribution Light intensity fluctuations caused by changes in various functions and target light intensity distribution Light intensity higher order small quantity The linear superposition of the light intensity distribution. As shown in formula (3):

[0089]

[0090] in, It can represent the fluctuations in light intensity caused by system parameter fluctuations. It can represent the distribution of light intensity of the target. This can represent the coupling effects of various orders of system parameters, i.e., the higher-order minor quantities of light intensity mentioned above. n can represent the number of system parameters.

[0091] Based on this, the above light intensity fluctuation information can be represented by the above formula (3).

[0092] According to embodiments of this disclosure, a second correlation relationship and a third correlation relationship can be obtained based on the correlation between the light intensity distribution after fluctuations of multiple system parameters and the information of the developed pattern, and the linear superposition property of the corresponding light intensity fluctuations caused by each system parameter. Then, a first sub-relationship model is constructed based on the second correlation relationship, and a second sub-relationship model is constructed based on the third correlation relationship. Thus, an initial information relationship model can be constructed based on the first sub-relationship model and the second sub-relationship model.

[0093] For example: for patterns that include multiple lines, It can be a function of the light intensity distribution and the location corresponding to that light intensity distribution, where, The value can represent the light intensity distribution, and x can represent the location of the light intensity distribution. Based on this, the light intensity distribution... The functional relationship with position x is:

[0094]

[0095] Among them, f -1 () represents the inverse function.

[0096] When the light intensity is the development threshold constant (TTS), the position x corresponding to this light intensity can be:

[0097]

[0098] in, The distribution of light intensity can be represented by x1 and x2, where x1 and x2 can both represent the location of the light intensity distribution. Let x1 be defined as... <x2。

[0099] For extreme ultraviolet lithography, the most important information regarding the developed pattern includes pattern linewidth and pattern position offset, while the light intensity distribution... The pattern linewidth and pattern position offset information after calibration by the development threshold constant are as follows:

[0100]

[0101] Among them, x0 is defined as the ideal position of the pattern center, and x1 < x2 is defined. CD can represent the pattern line width information, and PS can represent the pattern position change information.

[0102] Assume x0 = 0. For the target light intensity distribution The developed pattern information thereof is:

[0103]

[0104] Among them, CD ideal can represent the pattern line width information corresponding to the target light intensity distribution And PS ideal can represent the pattern position offset information corresponding to the target light intensity distribution

[0105] Based on this, the above formula (7) can be used to represent the relationship between the target light intensity distribution and the developed pattern information corresponding to the target light intensity distribution.

[0106] When the system parameters fluctuate, the light intensity distribution is Then the developed pattern information corresponding to this light intensity distribution can be:

[0107]

[0108] Among them, CD pi can represent the pattern line width information developed after the target parameter pi fluctuates. PS pi can represent the pattern position offset information developed after the target parameter pi fluctuates. can represent the light intensity fluctuation caused by the system parameter fluctuation.

[0109] Therefore, the light intensity fluctuation causes the change of the developed pattern information of the target light intensity distribution to be:

[0110] <000043​​​​​​​​​​​​​​​​The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. ΔCD fi >0 can indicate that the pattern line width increases, ΔCD fi <0 indicates that the pattern line width has decreased. ΔPS fi >0 can indicate that the change in pattern position offset is in the positive x-direction, ΔPS fi <0 indicates that the pattern position shifts negatively in the x-direction. ΔCD fi The magnitude of the absolute value | ΔCD fi | can represent the magnitude of the change in pattern line width, ΔPS fi The magnitude of the absolute value | ΔPS fi | can represent the magnitude of the change in pattern position offset.

[0112] Based on this, the above formula (9) can be used to represent the relationship between light intensity fluctuations and the development pattern change information corresponding to light intensity fluctuations.

[0113] When system parameters fluctuate, the final developed pattern information can be determined based on the target light intensity distribution. The development pattern information and light intensity fluctuations It is obtained by the linear superposition of the changes in pattern linewidth or pattern offset caused by the changes, that is:

[0114]

[0115] Where, ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. CD ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information. CD can represent the pattern linewidth information obtained after system parameter fluctuations. PS can represent the pattern position offset information obtained after system parameter fluctuations.

[0116] When the light intensity is the development threshold TTS, the light intensity distribution is within a very small range at position x corresponding to that light intensity. The relationship with position x is linear, therefore:

[0117]

[0118] The coupling effect between system parameters is much smaller than the target light intensity distribution. Light intensity fluctuations caused by variations in various system parameters When linearly superimposed, we obtain Based on this, higher-order insignificant quantities of light intensity can be ignored. Furthermore, when multiple system parameters fluctuate simultaneously, the intensity distribution, excluding higher-order small quantities of light intensity, is compared with... The corresponding development pattern information can be based on the target light intensity distribution. The corresponding development pattern information, and various light intensity fluctuations It is obtained by the linear superposition of the changes in pattern linewidth and pattern position offset, that is:

[0119]

[0120] Among them, CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. CD ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information.

[0121] In p i_max -p i_min <<p i_ideal In this case, system parameter p i The change in the developed pattern information caused by fluctuations can be approximated by a linear relationship. Where p i_max This can represent the system parameter p. i The maximum value after the fluctuation, p i_min This can represent the system parameter p. i The minimum value after fluctuation, p i_ideal For system parameter p iThe ideal value of is such that the system parameter fluctuations are minimal. Therefore, the system parameter p i Light intensity fluctuations caused by fluctuations Caused target light intensity distribution The changes in the developing pattern information can be represented as:

[0122]

[0123] Among them, CD pi_max This can represent the system parameter p. i When at its maximum value, the pattern linewidth information in the developed pattern information is displayed. (CD) pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information. ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. i This can represent fluctuating system parameters. i_ideal It can represent system parameters that have not fluctuated.

[0124] Where, p i This can correspond to the information in the second parameter. i_ideal It can correspond to the information in the first parameter.

[0125] Based on this, a second correlation can be obtained by considering the correlation between the first parameter information and the target light intensity distribution information, and the correlation between the target light intensity distribution information and the developing pattern information. A first sub-relationship model can then be constructed based on the second correlation.

[0126] Furthermore, based on the correlation between the second parameter information and the light intensity fluctuation information, and the third correlation between the light intensity fluctuation information and the development pattern change information, a third correlation relationship can be obtained, and a second sub-relationship model can be constructed based on the third correlation relationship.

[0127] Therefore, we can obtain the final light intensity distribution, which characterizes the light intensity distribution excluding higher-order minor quantities, when multiple system parameters fluctuate simultaneously. The initial information relationship model between the information and the developing pattern information is shown in formula (14):

[0128]

[0129] Among them, CD pi_max This can represent the system parameter p. i When at its maximum value, the pattern linewidth information in the developed pattern information is displayed. (CD) pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information. ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. i This can represent fluctuating system parameters. i_ideal This can represent system parameters that have not fluctuated. CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information.

[0130] According to embodiments of this disclosure, an information relationship model can be constructed by fitting an initial information relationship model.

[0131] For example: the light intensity distribution information used to construct the initial information relationship model does not include higher-order small quantities of light intensity. Therefore, there is a difference between the developed pattern information corresponding to the light intensity distribution information that does not include higher-order light intensity factors and the actual predetermined developed pattern information. Based on this, in order to ensure that the actual fluctuation range of the system parameters is within the fluctuation range of the target parameters, it is necessary to make the developed pattern information corresponding to the light intensity distribution information that does not include higher-order light intensity factors equal to the actual developed pattern information, that is, to compensate for the influence of the ignored higher-order light intensity factors. The functional relationship between the two is shown in formula (15):

[0132]

[0133] Wherein, CD can represent the linewidth information of the predetermined pattern in the predetermined developing pattern information. PS can represent the position offset information of the predetermined pattern in the predetermined developing pattern information. CD pi_max This can represent the system parameter p. i When at its maximum value, the pattern linewidth information in the developed pattern information is displayed. (CD) pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information. ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. i This can represent fluctuating system parameters. i_ideal This can represent system parameters that have not fluctuated. CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information.

[0134] The function in formula (15) can be expanded into an analytic polynomial form, as shown in formula (16):

[0135]

[0136] Wherein, CD can represent the linewidth information of the predetermined pattern in the predetermined developing pattern information. PS can represent the position offset information of the predetermined pattern in the predetermined developing pattern information. CD pi_max This can represent the system parameter p. i When at its maximum value, the pattern linewidth information in the developed pattern information is displayed. (CD) pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information. ΔCD fi It can represent light intensity fluctuations The resulting target light intensity distribution The variation in pattern linewidth in the developed pattern information. ΔPS fi It can represent light intensity fluctuations The resulting target light intensity distribution The change in pattern position offset in the developed pattern information. i This can represent fluctuating system parameters. i_ideal This can represent system parameters that have not fluctuated. CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information. Both j and z can represent the order of the polynomial. c j and c z Both can represent the coefficients of a polynomial. Both m and k can represent the highest-degree term of the polynomial. A higher highest-degree term results in a more accurate information relationship model, but also requires more modeling time. Therefore, m and k need to be determined based on the system parameters and the properties of the developing pattern information.

[0137] For CD and PS in formula (16), 2000 Monte Carlo experiments with uniformly random distributions of each parameter can be conducted respectively, and the Monte Carlo experiments can be used to fit c in formula (16). j and c z From this, we can obtain the information relationship model.

[0138] According to embodiments of this disclosure, processing the development pattern information of a photolithography system using an information relationship model to obtain target parameter fluctuation range information includes: obtaining target development pattern information based on the development pattern information and predetermined development tolerance information; and processing the target development pattern information using an information relationship model to obtain parameter fluctuation range information.

[0139] According to embodiments of this disclosure, by obtaining target development pattern information including tolerance based on development pattern information and predetermined development tolerance information, and then determining parameter fluctuation range information based on the target development pattern information, the range of parameter fluctuation range information is expanded, thereby reducing the error in verifying parameter fluctuation range information and improving the accuracy of parameter verification results.

[0140] According to embodiments of this disclosure, the inverse function of the information relationship model can be calculated, and based on predetermined tolerance information and actual predetermined development pattern information, the development pattern information corresponding to light intensity distribution information that does not include a small amount of higher-order light intensity, and the fluctuation range of the development pattern information can be determined.

[0141] For example: target light intensity distribution excluding higher-order small quantities of light intensity. The developing pattern information can be determined by the inverse function of formula (17), that is:

[0142]

[0143] Among them, f -1 () can represent the inverse function. CD can represent the linewidth information of a predetermined pattern in the predetermined developing pattern information. PS can represent the position offset information of a predetermined pattern in the predetermined developing pattern information. CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information.

[0144] The fluctuation range of the developed pattern information, which does not include high-order small amounts of light intensity distribution information, can be determined based on predetermined tolerance information and the actual predetermined developed pattern information, i.e.:

[0145]

[0146] Among them, (CD) wo_high_order ) ± This can represent the upper and lower limits of the pattern linewidth in the developed pattern information, excluding higher-order small values ​​of light intensity. (PS) wo_high_order ) ± It can represent the upper and lower limits of the pattern position offset in the developed pattern information, excluding higher-order small values ​​of light intensity. CD ± It can represent the upper and lower limits of the pattern linewidth variation in the actual pre-developed pattern information. PS ± It can represent the upper and lower limits of the pattern position offset change in the actual predetermined development pattern information.

[0147] Based on the correlation between system parameters and the developing pattern information corresponding to the light intensity distribution information excluding higher-order light intensity, and the fluctuation range of the developing pattern information corresponding to the light intensity distribution information excluding higher-order light intensity, the target parameter range information corresponding to the system parameters can be determined.

[0148] Therefore, the fluctuation range of the developed pattern information, which excludes high-order light intensity distribution information, can be made to correspond to the fluctuation range of the target parameters. That is, when all system parameters fluctuate, the developed pattern information, which excludes high-order light intensity distribution information, is guaranteed to be within both the fluctuation range of the pattern linewidth and the fluctuation range of the pattern position. In other words:

[0149]

[0150] Based on this, the fluctuation range of the target parameter can be determined according to the fluctuation range of the development pattern information corresponding to the light intensity distribution information that does not include the higher-order small amount of light intensity, as shown in formula (20):

[0151]

[0152] Among them, CD pi_max This can represent the system parameter p. i When at its maximum value, the pattern linewidth information in the developed pattern information is displayed. (CD) pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information is displayed. i This can represent fluctuating system parameters. CD wo_high_orderThis can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information.

[0153] To achieve good circuit functionality, the developed pattern information must simultaneously meet the requirements for pattern linewidth and pattern position. Therefore, the fluctuation range of the target parameters corresponding to all system parameters needs to simultaneously satisfy the fluctuation range of pattern linewidth variation and pattern offset variation in the developed pattern information corresponding to the light intensity distribution information excluding higher-order small quantities of light intensity. That is, the fluctuation range of the system parameters is the intersection of the solutions of the two equations in formula (20). If the fluctuation range of the system parameters exceeds the fluctuation range of the target parameters, the developed pattern information will definitely not meet the tolerance requirements of the yield for the actual developed pattern information of the photolithography.

[0154] Once the target fluctuation range of a certain system parameter is determined, the resulting fluctuations in light intensity... The resulting changes in the developing pattern information can be determined according to formula (14). Based on this, the fluctuation range of the next system parameter can be determined according to formula (21):

[0155]

[0156] Where g is the number of determined parameters. CD pi_max This can represent the pattern linewidth information in the developed pattern information when the system parameter pi is at its maximum value. CD pi_min This can represent the system parameter p. i When at its minimum value, the pattern line width information in the developed pattern information is displayed. PS pi_max This can represent the system parameter p. i When at its maximum value, the pattern position offset information in the developed pattern information is displayed. PS pi_min This can represent the system parameter p. i When at its minimum value, the pattern position offset information in the developed pattern information is displayed. i This can represent fluctuating system parameters. CD wo_high_order This can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern linewidth information. PS wo_high_orderThis can be expressed as a higher-order insignificant quantity excluding light intensity under parameter fluctuation conditions. The light intensity distribution information corresponds to the pattern position offset information. ideal It can represent the distribution of light intensity relative to the target. The corresponding pattern line width information. PS ideal It can represent the distribution of light intensity relative to the target light intensity. The corresponding pattern position offset information.

[0157] Repeat the above formula until the target fluctuation ranges for all parameters are determined. The order in which the fluctuation budget ranges for each parameter are determined can be based on the difficulty of controlling that parameter or the level of attention paid to it.

[0158] Based on this, the above formula (21) can be used to characterize the fluctuation range information of the target parameter.

[0159] According to embodiments of this disclosure, the target parameter fluctuation range information includes a first parameter fluctuation upper limit value and a first parameter fluctuation lower limit value, and the second parameter information includes a second parameter fluctuation upper limit value and a second parameter fluctuation lower limit value. Based on the target parameter fluctuation range information, the parameter information of multiple system parameters of the lithography system is verified to obtain parameter verification results, including: verifying the second parameter fluctuation upper limit value according to the first parameter fluctuation upper limit value to obtain a first parameter verification result; verifying the second parameter fluctuation lower limit value according to the first parameter fluctuation lower limit value to obtain a second parameter verification result; and obtaining the parameter verification result based on the first parameter verification result and the second parameter verification result.

[0160] According to embodiments of this disclosure, by verifying the upper and lower limits of the second parameter fluctuation separately, instead of verifying all the second parameter information, verification efficiency is improved. Furthermore, since the upper and lower limits are verified separately, the second parameter information within the largest possible range can be verified, thus improving the accuracy of the verified second parameter information.

[0161] Figure 3 A flowchart illustrating an information processing method according to another embodiment of the present disclosure is shown schematically.

[0162] like Figure 3 As shown, the information processing method of this embodiment includes operations S310 to S350.

[0163] When operating S310, we investigated the superposition properties between the fluctuations of multiple system parameters and the corresponding light intensity fluctuations of each system parameter.

[0164] In operating S320, based on the superposition property, the correlation between light intensity distribution information and development pattern information after fluctuations in multiple system parameters is analyzed.

[0165] In operation S330, based on the correlation, an initial information relationship model is constructed between system parameters and the developing pattern information corresponding to the light intensity distribution information that does not include high-order small amounts of light intensity.

[0166] In operation S340, the model parameters of the initial information relationship model are fitted according to the predetermined development pattern information to obtain the information relationship model.

[0167] When operating the S350, the information relationship model is used to process the developing pattern information to obtain the target parameter fluctuation range information.

[0168] Figure 4 A schematic diagram illustrating the experimental conditions of an exploratory experiment according to an embodiment of the present disclosure is shown.

[0169] like Figure 4 As shown, the method for calculating target parameter fluctuation information disclosed herein can be used to investigate 14 / 28nm line patterns based on system parameters in the extreme ultraviolet lithography system, including exposure dose, x-direction pupil ellipticity (ellipticity x), y-direction pupil ellipticity (ellipticity y), x-direction pupil non-balance (non-balance x), y-direction pupil non-balance (non-balance y), polarization, slit position, numerical aperture (NA), and flare. Here, "Illumination condition" represents the illumination condition, "Pupil" represents the pupil, and "Mask Stack" represents the mask stack.

[0170] The aforementioned information relationship model can include a pattern size model and a pattern offset model. The pattern size model and pattern offset model can be constructed based on the pattern size information and pattern position offset information in the developed pattern information and the actual predetermined developed pattern information.

[0171] Figure 5 A schematic diagram illustrating deviation information of a pattern size model according to an embodiment of the present disclosure is shown.

[0172] For the pattern size model, a total of 1000 experimental combinations were used for model fitting and validation. A summary of the biases in the pattern size model is as follows: Figure 5As shown, Deviations of CD model can represent the deviations of the pattern size model, Prediction deviation can represent the prediction deviation, Range within can represent the range, Median Line can represent the median line, Mean can represent the average value, Outliers can represent the deviation values, Low can represent the lower limit value, High can represent the upper limit value, Training Set can represent the model fitting set, and Validation Set can represent the model validation set. In the model fitting set, there are 979 instances of low deviation (≤1.9%) and 21 instances of high deviation (>1.9%). In the model validation set, there are 977 instances of low deviation (≤1.9%) and 23 instances of high deviation (>1.9%). Figure 5 It is evident that the model exhibits very small fitting and validation biases, with an average error of approximately 0.5% and a maximum error of less than 4%. Therefore, the model is accurate.

[0173] Figure 6 A schematic diagram illustrating deviation information of a pattern offset model according to an embodiment of the present disclosure is shown.

[0174] For the pattern migration model, a total of 1000 experimental combinations were used for both model fitting and validation. A summary of the pattern migration model biases is as follows: Figure 6 As shown in the diagram. Here, Deviations of PS model represents the deviations of the pattern offset model, Prediction deviation represents the prediction deviation, Range within represents the range, Median Line represents the median line, Mean represents the average value, Outliers represent the error values, Low represents the lower limit, and High represents the upper limit. Training Set represents the model fitting set, and Validation Set represents the model validation set. In the model fitting set, there are 965 low deviations (≤0.07nm) and 35 high deviations (>0.07nm). In the model validation set, there are 972 low deviations (≤0.07nm) and 28 high deviations (>0.07nm). Figure 6 As can be seen, the model's fitting and validation biases are very small, with an average error of approximately 0.02 nm and a maximum error of less than 0.1 nm. Therefore, the model is accurate.

[0175] Figure 7 A flowchart illustrating a method for verifying the fluctuation range of developing pattern information according to an embodiment of the present disclosure is shown.

[0176] like Figure 7As shown, the method for verifying the fluctuation range of the developing pattern information in this embodiment includes operations S710 to S740.

[0177] In operation S710, the fluctuation range of the developing pattern information corresponding to the light intensity distribution information that does not include higher-order small amounts of light intensity is calculated.

[0178] When operating S720, a set of parameters that falls within the boundary of the fluctuation range is randomly generated based on the fluctuation range.

[0179] By operating the S730 and conducting experiments using the parameter set, the developed pattern information is obtained.

[0180] In operation S740, the developed pattern information is compared with the tolerance boundary of the actual predetermined developed result based on the yield, and a comparison result is obtained.

[0181] Figure 8 A schematic diagram illustrating the verification results of the fluctuation range of a graphical size model according to an embodiment of the present disclosure is shown.

[0182] Taking the graphic size model as an example, the verification results of the parameter fluctuation range are as follows: Figure 8 As shown in the figure, Distribution of validation deviations represents the distribution of parameter fluctuation range deviations, Deviation represents the deviation, Range within represents the range, Median Line represents the median line, Mean represents the average value, Outliers represents the error value, Low represents the lower limit value, High represents the upper limit value, Upper Limit Group represents the validation results of the upper limit of the fluctuation range, and Lowers Limit Group represents the validation results of the lower limit of the fluctuation range. 2000 sets of experiments were used to validate the upper and lower limits of the fluctuation range. In the upper limit group, there were 1958 low deviations (≤1.9%) and 42 high deviations (>1.9%). In the lower limit group, there were 1712 low deviations (≤1.9%) and 288 high deviations (>1.9%). The validation results of the upper limit of the fluctuation range were distributed around 15.4 nm, with an average deviation of 0.08% from the CD value at 15.4 nm, and a maximum deviation not exceeding 4%. The verification results for the lower limit of the fluctuation range are distributed around 12.6 nm, with an average deviation of 0.09% from the CD value at 12.6 nm and a maximum deviation of no more than 3%. The results indicate that the fluctuation range obtained from this model is accurate.

[0183] Regarding the fluctuation range of pattern offset, due to the condition settings, the pattern offset cannot exceed the upper limit of the range; therefore, only the verification of the lower limit needs to be considered. Experiments were designed to verify the lower limit of the model, with 2000 sets of experiments used to verify the lower limit of the fluctuation range. Taking the center position of the exposure slit as an example, the average deviation of the PS was 0.0008 nm, and the maximum deviation did not exceed 0.0021 nm. The results show that the accuracy of the fluctuation range obtained from this model is accurate.

[0184] Figure 9 A schematic diagram illustrating a comparison of the fluctuation range of target parameters according to an embodiment of the present disclosure is shown.

[0185] The target parameter fluctuation range for each system parameter can be calculated. Assume that for two different equipment, the stray light levels are different, at 5% and 10% respectively. Parameters are prioritized according to their importance or process difficulty, and the target parameter fluctuation range for each system parameter is calculated sequentially, assuming that the exposure dose is considered last. The target parameter fluctuation range of the investigated system parameters is compared to the following when meeting the 14 / 28nm line exposure requirements: Figure 9 As shown, "Comparison of budget ranges" represents a comparison of budget fluctuation ranges. "Proportion of budget" represents the proportion of budget fluctuation ranges. "Polarization" represents polarization. "TTS" represents exposure dose. "NA" represents numerical aperture. "Flare" represents stray light. "Ellipticity y" represents the ellipticity of the pupil in the y-direction. "Ellipticity x" represents the ellipticity of the pupil in the x-direction. "Non-balance y" represents the polar symmetry of the pupil in the y-direction. "Non-balance x" represents the polar symmetry of the pupil in the x-direction. It is evident that when stray light in the lithography system can be better controlled, the target parameter fluctuation range of other system parameters will become wider. This method can update the ranges of other system parameters in real time when the target parameter fluctuation range of one system parameter changes, ensuring that the process budget is allocated reasonably. For two different equipment, the above target parameter fluctuation ranges can be used as targets for system parameter monitoring respectively.

[0186] When developing a new lithography system, system parameters can be prioritized based on their importance or process complexity. The target parameter fluctuation range for each parameter can then be calculated sequentially, providing a reasonable guidance and objective. For different machine characteristics within different lithography systems used in production, the target parameter fluctuation range for other system parameters can be determined based on the known target parameter fluctuation range, thus providing a clear parameter range for process monitoring.

[0187] According to embodiments of this disclosure, the information processing method can quickly obtain a clear target fluctuation range for each system parameter based solely on the influence of a single system parameter and a small number of random experiments. Furthermore, it can control the development pattern information of the lithography system through an overall fluctuation budget approach, exhibiting high accuracy. Moreover, the information processing method of this disclosure is highly applicable to tolerance budget analysis of key parameters in various parts of the lithography system.

[0188] According to embodiments of this disclosure, the information processing method treats the influence of complex coupling effects between system parameters on light intensity as a higher-order minor quantity of light intensity, and further compensates for this influence with the fitting coefficient between the developed pattern information and the actual developed pattern information, which does not include the higher-order minor quantity of light intensity. Based on this, in the case of multi-parameter tolerance budget analysis of a lithography system, a small number of random experiments can be conducted based on a single parameter to quickly obtain a clear target parameter fluctuation range for the system parameters, thus providing clear guidance for the development of the lithography system. Furthermore, the information processing method of this disclosure is applicable to system parameters of light sources, illumination systems, masks, projection systems, and other components in a lithography system, and has a wide range of applications. In addition, for the same target scenario, the parameter fluctuation range of each system parameter can be updated synchronously according to the specific requirements of the lithography system and lithography process, reducing the resources consumed by repetitive modeling and analysis.

[0189] Based on the above information processing method, this disclosure also provides an information processing apparatus. The following will be combined with... Figure 10 The device is described in detail.

[0190] Figure 10 A schematic block diagram of an information processing apparatus according to an embodiment of the present disclosure is shown.

[0191] like Figure 10 As shown, the information processing device 1000 of this embodiment includes a first processing module 1010, a verification module 1020, a first determination module 1030, and a second determination module.

[0192] The first processing module 1010 is used to process the development pattern information of the photolithography system using an information relationship model to obtain target parameter fluctuation range information. The information relationship model characterizes the first correlation between the development pattern information and the target parameter fluctuation range information. The target parameter fluctuation range information characterizes the target fluctuation range of the system parameters of the photolithography system. The development pattern information characterizes the pattern information obtained by the photolithography system developing the photoresist. In one embodiment, the first processing module 1010 can be used to execute the operation S210 described above, which will not be repeated here.

[0193] The verification module 1020 is used to verify the parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information, and obtain the parameter verification result. In one embodiment, the verification module 1020 can be used to perform the operation S220 described above, which will not be repeated here.

[0194] The first determining module 1030 is used to determine, from multiple system parameters, the target system parameter whose parameter verification result indicates a verification anomaly. In one embodiment, the first determining module 1030 can be used to perform the operation S230 described above, which will not be repeated here.

[0195] The second determining module 1040 is used to determine the target module from the lithography system based on the target system parameters. In one embodiment, the second determining module 1040 can be used to perform the operation S240 described above, which will not be repeated here.

[0196] According to embodiments of this disclosure, the information processing apparatus further includes a construction module and a second processing module. The construction module is used to construct an initial information relationship model based on parameter information, light intensity distribution information, and development pattern information. The light intensity distribution information characterizes the light intensity distribution of the lithography system and does not include higher-order light intensity variables, which are at least second-order. The second processing module is used to process the initial information relationship model based on predetermined development pattern information to obtain an information relationship model.

[0197] According to embodiments of this disclosure, the first processing module includes a fitting submodule. The fitting submodule is used to fit the model parameters of an initial information relationship model based on predetermined development pattern information to obtain an information relationship model.

[0198] According to embodiments of this disclosure, the construction module includes a first acquisition submodule and a second acquisition submodule. The first acquisition submodule is used to acquire a first sub-relationship model and a second sub-relationship model. The first sub-relationship model characterizes a second correlation between first parameter information and development pattern information corresponding to target light intensity distribution information. The second sub-relationship model characterizes a third correlation between second parameter information and development pattern change information corresponding to light intensity fluctuation information. The development pattern change information characterizes the change in development pattern information of the lithography system under fluctuations in system parameter values. The second acquisition submodule is used to obtain an initial information relationship model based on the first and second sub-relationship models.

[0199] According to embodiments of this disclosure, the verification module 1020 includes a first verification submodule, a second verification submodule, and a third acquisition submodule. The first verification submodule is used to verify the upper limit of the fluctuation of the second parameter according to the upper limit of the fluctuation of the first parameter, obtaining a first parameter verification result; the second verification submodule is used to verify the lower limit of the fluctuation of the second parameter according to the lower limit of the fluctuation of the first parameter, obtaining a second parameter verification result; the third acquisition submodule is used to obtain a parameter verification result based on the first parameter verification result and the second parameter verification result.

[0200] According to embodiments of this disclosure, the first processing module 1010 includes a fourth acquisition submodule and a processing submodule. The fourth acquisition submodule is used to obtain target development pattern information based on development pattern information and predetermined development tolerance information; the processing submodule is used to process the target development pattern information using an information relationship model to obtain parameter fluctuation range information.

[0201] According to embodiments of this disclosure, any plurality of modules among the first processing module 1010, verification module 1020, first determination module 1030, and second determination module 1040 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first processing module 1010, verification module 1020, first determination module 1030, and second determination module 1040 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first processing module 1010, the verification module 1020, the first determining module 1030, and the second determining module 1040 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0202] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure.

[0203] like Figure 11As shown, an electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage portion 1108 into a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0204] RAM 1103 stores various programs and data required for the operation of electronic device 1100. Processor 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Processor 1101 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 1102 and / or RAM 1103. It should be noted that the programs may also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0205] According to embodiments of this disclosure, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to a bus 1104. The electronic device 1100 may also include one or more of the following components connected to the input / output (I / O) interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output (I / O) interface 1105 as needed. A removable medium 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1110 as needed so that computer programs read from it can be installed into the storage section 1108 as needed.

[0206] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0207] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 1102 and / or RAM 1103 and / or one or more memories other than ROM 1102 and RAM 1103 described above.

[0208] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to enable the computer system to implement the information processing methods provided in the embodiments of this disclosure.

[0209] When the computer program is executed by the processor 1101, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0210] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1109, and / or installed from the removable medium 1111. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0211] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by processor 1101, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0212] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0213] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0214] It should be noted that, unless it is explicitly stated that there is a sequential order of execution between different operations, or that there is a sequential order of execution between different operations in terms of technical implementation, the execution order between multiple operations may not be significant, and multiple operations may be executed simultaneously.

[0215] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0216] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. An information processing method, comprising: The development pattern information of the lithography system is processed using an information relationship model to obtain target parameter fluctuation range information. The information relationship model is used to characterize the first correlation between the development pattern information and the target parameter fluctuation range information. The target parameter fluctuation range information is used to characterize the target fluctuation range of the system parameters of the lithography system. The development pattern information characterizes the pattern information obtained by the lithography system developing photoresist. Based on the target parameter fluctuation range information, the parameter information of multiple system parameters of the lithography system is verified to obtain the parameter verification result; From the plurality of system parameters, determine the target system parameter whose verification result characterizes the verification anomaly; Based on the target system parameters, the target module is determined from the lithography system; The information relationship model is constructed through the following operations: Based on the parameter information, light intensity distribution information, and development pattern information, an initial information relationship model is constructed, wherein the light intensity distribution information is used to characterize the light intensity distribution of the lithography system, the light intensity distribution information does not include higher-order light intensity minors, and the order of the higher-order light intensity minors is at least second order; Based on the predetermined development pattern information, the initial information relationship model is processed to obtain the information relationship model; The light intensity distribution information includes target light intensity distribution information and light intensity fluctuation information. The target light intensity distribution information is the light intensity distribution information obtained when the parameter values ​​of the system parameters do not fluctuate. The light intensity fluctuation information is used to characterize the light intensity distribution fluctuation of the lithography system when the parameter values ​​of at least one of the multiple system parameters fluctuate. The parameter information includes first parameter information and second parameter information. The first parameter information corresponds to the system parameters under no fluctuation conditions, and the second parameter information corresponds to the system parameters under fluctuating conditions.

2. The method according to claim 1, wherein, The step of processing the initial information relationship model based on the predetermined development pattern information to obtain the information relationship model includes: Based on the predetermined development pattern information, the model parameters of the initial information relationship model are fitted to obtain the information relationship model.

3. The method according to claim 1, wherein, The step of constructing an initial information relationship model based on the parameter information, light intensity distribution information, and development pattern information includes: Obtain a first sub-relationship model and a second sub-relationship model, wherein the first sub-relationship model is used to characterize the second correlation between the first parameter information and the development pattern information corresponding to the target light intensity distribution information, and the second sub-relationship model is used to characterize the third correlation between the second parameter information and the development pattern change information corresponding to the light intensity fluctuation information, and the development pattern change information is used to characterize the change status of the development pattern information of the lithography system under the condition of fluctuation of the parameter value of the system parameter; The initial information relation model is obtained based on the first sub-relation model and the second sub-relation model.

4. The method according to claim 1, wherein, The target parameter fluctuation range information includes the upper limit value of the first parameter fluctuation and the lower limit value of the first parameter fluctuation, and the second parameter information includes the upper limit value of the second parameter fluctuation and the lower limit value of the second parameter fluctuation; The step of verifying the parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information to obtain parameter verification results includes: The upper limit of the fluctuation of the second parameter is verified according to the upper limit of the fluctuation of the first parameter to obtain the verification result of the first parameter. The lower limit of the fluctuation of the second parameter is verified according to the lower limit of the fluctuation of the first parameter to obtain the verification result of the second parameter. The parameter verification result is obtained based on the first parameter verification result and the second parameter verification result.

5. The method according to any one of claims 1 to 4, wherein, The process of using an information relationship model to process the development pattern information of the photolithography system to obtain the target parameter fluctuation range information includes: Based on the development pattern information and the predetermined development tolerance information, the target development pattern information is obtained; The target development pattern information is processed using the information relationship model to obtain the parameter fluctuation range information.

6. An information processing apparatus for implementing the information processing method as described in any one of claims 1 to 5, comprising: The first processing module is used to process the development pattern information of the photolithography system using an information relationship model to obtain the target parameter fluctuation range information. The information relationship model is used to characterize the first correlation between the development pattern information and the target parameter fluctuation range information. The target parameter fluctuation range information is used to characterize the target fluctuation range of the system parameters of the photolithography system. The development pattern information characterizes the pattern information obtained by the photolithography system developing the photoresist. The verification module is used to verify the parameter information of multiple system parameters of the lithography system according to the target parameter fluctuation range information, and obtain the parameter verification results; The first determining module is used to determine the target system parameter that represents the verification anomaly from multiple system parameters; The second determining module is used to determine the target module from the lithography system based on the target system parameters.

7. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

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