Aberration processing method based on deterministic screening experiment, electronic device and medium

Abstract

The disclosure provides an aberration processing method based on a deterministic screening experiment, an electronic device and a medium, which can be applied to the field of high-resolution imaging technology. The method comprises: inputting experimental development pattern information of a photolithography system and experimental aberration information corresponding to the experimental development pattern information into a relationship model, and fitting to obtain I coefficients of the relationship model, wherein the experimental aberration information and the experimental development pattern information are obtained by using the photolithography system to perform a deterministic screening experiment on an object, the relationship model is constructed according to the correlation between the aberration information and the development pattern information, the coefficients are used to represent the influence degree of the geometric aberration term corresponding to the coefficients on the development pattern information, and I is a positive integer greater than 1; determining a target coefficient from the I coefficients of the relationship model; and optimizing the aberration information according to a geometric aberration term corresponding to the target coefficient in the I geometric aberration terms to obtain target aberration information.

Description

Technical Field

[0001] This disclosure relates to the field of high-resolution imaging, and more particularly to an aberration processing method, electronic device, and medium based on a deterministic screening experiment. Background Technology

[0002] Photolithography is a key process in the manufacturing of large-scale integrated circuits.

[0003] From an optical design perspective, zero aberration in projection lithography is an ideal design. However, in actual lithography systems, due to diffraction limitations and manufacturing errors, the propagation direction and phase of the ideal wavefront on the image plane change, resulting in aberrations between the actual and ideal wavefronts.

[0004] In the process of realizing the inventive concept disclosed herein, the inventors discovered that modeling and verifying aberrations is time-consuming. Summary of the Invention

[0005] In view of the above problems, this disclosure provides an aberration processing method, electronic device and medium based on deterministic screening experiments.

[0006] According to the first aspect of this disclosure, an aberration processing method based on a deterministic screening experiment is provided, comprising: inputting experimental development pattern information of a lithography system and experimental aberration information corresponding to the experimental development pattern information into a relational model, fitting I coefficients of the relational model, wherein both the experimental aberration information and the experimental development pattern information are obtained by performing a deterministic screening experiment on an object using a lithography system, the relational model is constructed based on the correlation between the aberration information and the development pattern information, the aberration information includes I geometric aberration terms corresponding to the I coefficients, the coefficients are used to characterize the influence of the geometric aberration terms corresponding to the coefficients on the development pattern information, and I is a positive integer greater than 1; determining a target coefficient from the I coefficients of the relational model; and optimizing the aberration information based on the geometric aberration terms corresponding to the target coefficients among the I geometric aberration terms to obtain target aberration information.

[0007] According to embodiments of this disclosure, the experimental development pattern information includes experimental pattern linewidth information and experimental pattern position offset information. The relational model includes a first relational model and a second relational model. The first relational model is constructed based on the correlation between aberration information and pattern linewidth information, and the second relational model is constructed based on the correlation between aberration information and pattern position offset information. The experimental development pattern information of the lithography system and the experimental aberration information corresponding to the experimental development pattern information are input into the relational model, and I coefficients of the relational model are fitted. This includes: inputting the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into the first relational model, and fitting M first coefficients of the first relational model, where M is a positive integer greater than 1; inputting the experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information into the second relational model, and fitting N second coefficients of the second relational model, where N is a positive integer greater than 1, and M+N=I.

[0008] According to an embodiment of this disclosure, the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information are input into a first relational model, and M first coefficients of the first relational model are fitted. This includes: inputting the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into a first relational model, and fitting M first initial coefficients of the first relational model; processing the M first initial coefficients using a first predetermined function to calculate the second-order coefficients corresponding to each of the M first initial coefficients; and adding the M first initial coefficients and the second-order coefficients corresponding to each of the M first initial coefficients to obtain the M first coefficients.

[0009] According to embodiments of this disclosure, determining a target coefficient from I coefficients includes: determining a first target coefficient from M first coefficients; and determining a second target coefficient from N second coefficients.

[0010] According to an embodiment of this disclosure, determining a first target coefficient from M first coefficients includes: sorting the M first coefficients to obtain a first sorting result regarding pattern linewidth information; and determining the first target coefficient from the M first coefficients based on the first sorting result and a first predetermined threshold.

[0011] According to an embodiment of this disclosure, determining a first target coefficient from M first coefficients based on a first sorting result and a first predetermined threshold includes: calculating a first total coefficient sum based on the M first coefficients; accumulating m first coefficients from the M first coefficients according to the first sorting result to obtain a first coefficient sum, where m is a positive integer less than M and greater than 1; obtaining a first ratio based on the proportion of the first coefficient sum to the first total coefficient sum; and determining the m first coefficients as the first target coefficient if the first ratio is greater than or equal to the first predetermined threshold.

[0012] According to embodiments of this disclosure, the aberration information is optimized based on the geometric aberration term corresponding to the target coefficient among I geometric aberration terms to obtain target aberration information. This includes: constructing a first geometric aberration set comprising M first target geometric aberration terms based on I geometric aberration terms and M first target coefficients, wherein the M first target geometric aberration terms correspond to the M first target coefficients; constructing a second geometric aberration set comprising N second target geometric aberration terms based on I geometric aberration terms and N second target coefficients, wherein the N second target geometric aberration terms correspond to the N second target coefficients; and optimizing the aberration information based on the first geometric aberration set and the second geometric aberration set to obtain the target aberration information.

[0013] According to embodiments of this disclosure, optimizing aberration information based on a first set of geometric aberrations and a second set of geometric aberrations to obtain target aberration information includes: determining the union of the first set of geometric aberrations and the second set of geometric aberrations as the target set of geometric aberrations; and optimizing the aberration information based on the geometric aberration terms in the target set of geometric aberrations to obtain the target aberration information.

[0014] A second 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.

[0015] A third 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.

[0016] According to the aberration processing method, electronic equipment, and medium based on deterministic screening experiments provided in this disclosure, a deterministic screening experiment is conducted using a photolithography system to obtain comprehensive experimental development pattern information and experimental aberration information, and to fit the coefficients of an accurate relational model. Since the coefficients are related to the influence of geometric aberration terms on the development pattern information, by selecting target coefficients, the geometric aberration terms corresponding to the target coefficients can be identified as the target geometric aberration terms with strong influence. Furthermore, based on the target geometric aberration terms, the aberration information is optimized to reduce the geometric aberration terms with weak influence on the development pattern information, and only the geometric aberration terms with strong influence on the development pattern information are calculated. This fully utilizes the computing power of the computer and improves the efficiency of the computer in calculating aberration information, thereby reducing the amount of aberration information without sacrificing aberration accuracy. The required number of geometric aberrations can be reasonably selected according to different accuracy and calculation time requirements, which has great flexibility and can also provide reasonable guidance for other parameter influence analysis fields. 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 schematically illustrates an application scenario of the aberration processing method according to an embodiment of the present disclosure.

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

[0020] Figure 3 A schematic diagram of an imaging pattern under diode illumination of a lithography system according to an embodiment of the present disclosure is shown.

[0021] Figure 4 A schematic diagram of an imaging pattern under ring illumination conditions of a lithography system according to an embodiment of the present disclosure is shown.

[0022] Figure 5 This schematically illustrates the relationship between a predetermined threshold for target geometric aberration terms, development pattern information deviation, and the proportion of target geometric aberration terms in a photolithography system with diode illumination and vertical lines according to an embodiment of the present disclosure.

[0023] Figure 6 This schematically illustrates the relationship between a predetermined threshold for target geometric aberration terms, development pattern information deviation, and the proportion of target geometric aberration terms in a lithography system with ring illumination and vertical lines according to an embodiment of the present disclosure.

[0024] Figure 7 This schematically illustrates the relationship between a predetermined threshold for target geometric aberration terms, development pattern information deviation, and the proportion of target geometric aberration terms in a lithography system according to an embodiment of the present disclosure under ring illumination-horizontal line conditions.

[0025] Figure 8 A schematic block diagram of an aberration processing apparatus according to an embodiment of the present disclosure is shown; and

[0026] Figure 9 A block diagram schematically illustrates an electronic device suitable for implementing an aberration processing method according to an embodiment of the present disclosure. Detailed Implementation

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.).

[0031] 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.

[0032] As the most crucial component in a projection lithography system, the projection lens plays a decisive role in the lithography imaging performance. In practical projection lithography systems, due to diffraction limitations of the lithography system and errors in processing and manufacturing, aberration-free projection lenses do not exist. Aberration information can be represented by orthogonal Zernike polynomials within the unit circle, with each term representing a type of geometric aberration.

[0033] Aberration information can affect the lithographic imaging quality of a lithography system; therefore, it is necessary to control and optimize aberration information.

[0034] Different geometric aberration terms have varying degrees of influence on lithography results, and there are complex coupling effects between different geometric aberration terms. Therefore, when conducting research on aberration optimization and control, it is usually necessary to consider a large number of terms.

[0035] Based on this, the inventors discovered that modeling and verifying aberrations is time-consuming.

[0036] In view of this, embodiments of the present disclosure provide an aberration processing method based on a deterministic screening experiment, comprising: inputting experimental development pattern information of a lithography system and experimental aberration information corresponding to the experimental development pattern information into a relational model, fitting I coefficients of the relational model, wherein both the experimental aberration information and the experimental development pattern information are obtained by performing a deterministic screening experiment on an object using a lithography system, the relational model is constructed based on the correlation between aberration information and development pattern information, the aberration information includes I geometric aberration terms corresponding to the I coefficients, the coefficients are used to characterize the influence of the geometric aberration terms corresponding to the coefficients on the development pattern information, and I is a positive integer greater than 1; determining a target coefficient from the I coefficients of the relational model; and optimizing the aberration information based on the geometric aberration terms corresponding to the target coefficients among the I geometric aberration terms to obtain target aberration information.

[0037] Figure 1 The diagram illustrates an application scenario of aberration processing according to an embodiment of the present disclosure.

[0038] 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.

[0039] Server 102 can collect aberration information and experimental development patterns from lithography system 101 via the network.

[0040] 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.

[0041] It should be noted that the aberration processing method provided in this embodiment can generally be executed by server 102. Correspondingly, the aberration processing apparatus provided in this embodiment can generally be located in server 102. The aberration 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 aberration 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.

[0042] 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.

[0043] The following will be based on Figure 1 The described scene, through Figures 2-7 The aberration processing method of the disclosed embodiments will be described in detail.

[0044] Figure 2 A flowchart illustrating an aberration processing method according to an embodiment of the present disclosure is shown schematically.

[0045] like Figure 2 As shown, this embodiment 200 includes operations S210 to S230.

[0046] In operation S210, the experimental development pattern information of the photolithography system and the experimental aberration information corresponding to the experimental development pattern information are input into the relational model, and the I coefficients of the relational model are obtained by fitting.

[0047] In operation S220, the target coefficient is determined from the I coefficients of the relational model.

[0048] In operation S230, the aberration information is optimized based on the geometric aberration term corresponding to the target coefficient among the I geometric aberration terms to obtain the target aberration information. According to embodiments of this disclosure, the experimental development pattern information can be an imaged development pattern obtained by using a photolithography system.

[0049] According to embodiments of this disclosure, experimental aberration information can be aberration information obtained by an object through experiments using a photolithography system. For example, aberration information can be represented by orthogonal Zernike polynomials within a unit circle, where each term represents a type of geometric aberration, such as spherical aberration, coma, astigmatism, etc. Aberration information may include I geometric aberration terms corresponding to I coefficients, where the coefficients characterize the influence of the corresponding geometric aberration terms on the developed pattern information, and I is a positive integer greater than 1.

[0050] According to embodiments of this disclosure, both experimental aberration information and experimental development pattern information are obtained through deterministic screening experiments performed on the object using a photolithography system. The specific experimental content of the deterministic screening experiment may involve combining the values ​​of each geometric aberration term (0, maximum, or minimum) to obtain experimental aberration information and collecting experimental development pattern information.

[0051] For example, multiple geometric aberration terms can include geometric aberration term A and geometric aberration term B. The maximum value of geometric aberration term A and the maximum value of geometric aberration term B can be combined to obtain experimental aberration information; the minimum value of geometric aberration term A and the maximum value of geometric aberration term B can be combined to obtain experimental aberration information; and geometric aberration term A (set to 0) and the maximum value of geometric aberration term B can be combined to obtain experimental aberration information.

[0052] The experimental aberration information can be obtained by combining the maximum value of geometric aberration term A with geometric aberration term B set to 0; the experimental aberration information can also be obtained by combining the minimum value of geometric aberration term A with geometric aberration term B set to 0; and the experimental aberration information can also be obtained by combining geometric aberration term A set to 0 with geometric aberration term B set to 0.

[0053] The maximum value of geometric aberration term A and the minimum value of geometric aberration term B can be combined to obtain experimental aberration information; the minimum value of geometric aberration term A and the minimum value of geometric aberration term B can be combined to obtain experimental aberration information; the minimum value of geometric aberration term A and the minimum value of geometric aberration term B, which are set to 0, can be combined to obtain experimental aberration information.

[0054] For example, taking the combination of two geometric aberration terms as an example, the value of geometric aberration term A can be (0, -2, 13), and the value of geometric aberration term B can be (0, 1, 8). Combining the values ​​of geometric aberration term A and geometric aberration term B, the experimental aberration information can be (0, 0), (0, 1), (0, 8), (-2, 0), (-2, 1), (-2, 8), (13, 0), (13, 1), (13, 8).

[0055] Based on this, as many different experimental aberration information as possible can be obtained, thus expanding the experimental aberration information and consequently expanding the development pattern information, thereby improving the accuracy of the determined coefficients.

[0056] According to embodiments of this disclosure, the relationship model can be constructed based on the association between aberration information and development pattern information.

[0057] According to embodiments of this disclosure, experimental development pattern information of the lithography system and experimental aberration information corresponding to the experimental development pattern information can be input into a relational model, and the I coefficients of the relational model can be obtained by fitting the model using the least squares method.

[0058] According to embodiments of this disclosure, the target coefficient can characterize the geometric aberration coefficients that have a higher influence on the developed pattern information among the I coefficients obtained from the relational model and the I geometric aberration terms corresponding to the I coefficients.

[0059] According to embodiments of this disclosure, I coefficients can be sorted to obtain a sorting result. Then, based on the sorting result, a coefficient with a predetermined sorting position is determined from the i coefficients as the target coefficient. The predetermined sorting position can be determined according to requirements, and this disclosure does not limit it.

[0060] For example, the I coefficients in a relational model can include coefficient 1, coefficient 2, and coefficient 3. Coefficients 1 and 2 have a higher influence on the developed pattern information. Therefore, coefficients 1 and 2 can be the target coefficients.

[0061] According to embodiments of this disclosure, optimizing aberration information may include: deleting geometric aberration terms that do not correspond to the target coefficient from the I geometric aberration terms included in the aberration information, thereby optimizing the aberration information.

[0062] According to embodiments of this disclosure, a deterministic screening experiment is performed using a photolithography system to obtain comprehensive experimental development pattern information and experimental aberration information, and coefficients of an accurate relational model are fitted. Since the coefficients are related to the influence of geometric aberration terms on the development pattern information, by selecting target coefficients, the geometric aberration terms corresponding to the target coefficients can be identified as target geometric aberration terms with significant influence. Furthermore, based on the target geometric aberration terms, the aberration information is optimized, reducing the number of geometric aberration terms with little or no influence on the development pattern information, and only calculating the geometric aberration terms with significant influence on the development pattern information. This reduces the amount of aberration information without sacrificing aberration accuracy. The required number of geometric aberrations can be reasonably selected according to different accuracy and runtime requirements, providing greater flexibility and offering reasonable guidance for other parameter influence analysis fields. This fully utilizes the computing power of computers, improving the efficiency of computer calculation of aberration information.

[0063] According to embodiments of this disclosure, the experimental development pattern information includes experimental pattern linewidth information and experimental pattern position offset information. The relationship model includes a first relationship model and a second relationship model. The first relationship model is constructed based on the correlation between aberration information and pattern linewidth information, and the second relationship model is constructed based on the correlation between aberration information and pattern position offset information.

[0064] According to embodiments of this disclosure, experimental pattern linewidth information (critical dimension, CD) can be used to characterize the linewidth of a developed pattern obtained through experimental imaging.

[0065] According to embodiments of this disclosure, experimental pattern shift (PS) information can be used to characterize the positional shift of the developed pattern in experimental imaging.

[0066] According to embodiments of this disclosure, experimental development pattern information of the photolithography system and experimental aberration information corresponding to the experimental development pattern information are input into a relational model, and I coefficients of the relational model are fitted. This includes: inputting experimental pattern linewidth information and experimental aberration information corresponding to the experimental pattern linewidth information into a first relational model, and fitting M first coefficients of the first relational model, where M is a positive integer greater than 1; inputting experimental pattern position offset information and experimental aberration information corresponding to the experimental pattern position offset information into a second relational model, and fitting N second coefficients of the second relational model, where N is a positive integer greater than 1, and M+N=I.

[0067] According to embodiments of this disclosure, the first coefficient can characterize the geometric aberration term coefficients of a first relational model obtained by fitting experimental pattern linewidth information and experimental aberration information corresponding to the experimental pattern linewidth information. Correspondingly, the second coefficient can characterize the geometric aberration term coefficients of a second relational model obtained by fitting experimental pattern position offset information and experimental aberration information corresponding to the experimental pattern position offset information.

[0068] According to embodiments of this disclosure, the experimental pattern linewidth information and the corresponding experimental aberration information are input into a first relational model, and M first coefficients of the first relational model are obtained by least squares fitting. The experimental pattern position offset information and the corresponding experimental aberration information are input into a second relational model, and N second coefficients of the second relational model are obtained by least squares fitting.

[0069] According to embodiments of this disclosure, by processing experimental pattern linewidth information and experimental pattern position offset information respectively, a first coefficient corresponding to the pattern linewidth information and a second coefficient corresponding to the pattern position offset information are obtained. Thus, the accurate target geometric aberration term can be determined based on the first coefficient corresponding to the pattern linewidth information and the second coefficient corresponding to the pattern position offset information.

[0070] According to an embodiment of this disclosure, the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information are input into a first relational model, and M first coefficients of the first relational model are fitted. This includes: inputting the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into a first relational model, and fitting M first initial coefficients of the first relational model; processing the M first initial coefficients using a first predetermined function to calculate the second-order coefficients corresponding to each of the M first initial coefficients; and adding the M first initial coefficients and the second-order coefficients corresponding to each of the M first initial coefficients to obtain the M first coefficients.

[0071] According to an embodiment of this disclosure, the absolute value of the coefficient of the i-th geometric aberration term in the first initial coefficients of the first relational model can be used to characterize the influence of the i-th geometric aberration term Zi on the CD of the developed pattern information, as shown in the following formula (1).

[0072] S_CD DSD_i =|C zi | (1)

[0073] Among them, S_CD DSD_i Let C be the degree of influence of the i-th geometric aberration term Zi on the CD in the developed pattern information. Zi The coefficient of the i-th geometric aberration term Zi can be represented. The influence of the geometric aberration terms corresponding to the M first initial coefficients on the CD in the developed pattern information can be calculated according to formula (1).

[0074] According to an embodiment of this disclosure, the first initial coefficient C is as follows (2): Zi The absolute values ​​of their respective second-order coefficients can be used to characterize the influence of the second-order effect of the i-th geometric aberration term on the CD in the developed pattern information.

[0075] S_CD 2 DSD_i =|C 2 zi | (2)

[0076] Among them, S_CD 2 DSD_i Let C be the influence of the second-order effect of the i-th geometric aberration term Zi on CD. 2 Zi It can represent the second-order coefficients corresponding to the first initial coefficient Zi in the first relational model.

[0077] According to embodiments of this disclosure, the first coefficient can be expressed as the sum of the first initial coefficients of the first relationship model obtained by fitting the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information, and the second-order coefficients corresponding to the first initial coefficients. The total influence of the i-th geometric aberration term on CD in the developed pattern information can characterize the sum of the single-item influence and the second-order effect influence. The total influence of the i-th geometric aberration term on CD in the developed pattern information can be calculated according to formula (3), and this total influence corresponds to the aforementioned first coefficient.

[0078] S_CD_T DSD_i =S_CD DSD_i +S_CD 2 DSD_i (3)

[0079] Among them, S_CD_T DSD_i S_CD represents the total influence of the i-th geometric aberration term on CD in the developed pattern information. DSD_i S_CD represents the influence of the i-th geometric aberration term Zi on CD in the developed pattern information. 2 DSD_i Let represent the influence of the second-order effect of the i-th geometric aberration term Zi on CD.

[0080] According to embodiments of this disclosure, the experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information are input into a second relational model, and N second coefficients of the second relational model are fitted. This includes: inputting the experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information into a second relational model, and fitting N second initial coefficients of the second relational model; processing the N second initial coefficients using a second predetermined function to calculate the second-order coefficients corresponding to each of the N second initial coefficients; and adding the N second initial coefficients and the second-order coefficients corresponding to each of the N second initial coefficients to obtain N second coefficients.

[0081] According to an embodiment of this disclosure, as shown in formula (4), the absolute value of the coefficient of the i-th geometric aberration term in the second initial coefficients of the second relational model can be used to characterize the influence of the i-th geometric aberration term Zi on the PS of the developed pattern information.

[0082] S_PS DSD_i =|C zi | (4)

[0083] Among them, S_PS DSD_i Let C be the influence of the i-th geometric aberration term Zi on the PS of the developed pattern information. ZiThis represents the coefficient of the i-th geometric aberration term in the second relational model. The influence of the geometric aberration terms corresponding to the N second initial coefficients on the PS of the developed pattern information can be calculated according to formula (4).

[0084] According to an embodiment of this disclosure, the second initial coefficient C is as follows (5): Zi The absolute values ​​of their respective second-order coefficients can be used to characterize the influence of the second-order effect of the i-th geometric aberration term on the PS of the developed pattern information.

[0085] S_PS 2 DSD_i =|C 2 zi | (5)

[0086] Among them, S_PS 2 DSD_i Let C be the degree of influence of the second-order effect of the i-th geometric aberration term Zi on PS. 2 Zi The second initial coefficient Zi in the second relational model can be represented by the second-order coefficients. The influence of the second-order effect of the geometric aberration terms corresponding to the N second initial coefficients on the PS of the developed pattern information can be calculated according to formula (5).

[0087] According to embodiments of this disclosure, the second coefficient can be expressed as the sum of the second initial coefficients and the second-order coefficients corresponding to the second initial coefficients of the second relationship model fitted based on the experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information. The total influence of the i-th geometric aberration term on the PS of the developed pattern information can characterize the sum of the single-item influence and the second-order effect influence. The total influence of the i-th geometric aberration term on the PS of the developed pattern information can be calculated according to formula (6), and this total influence can correspond to the aforementioned second coefficient.

[0088] S_PS_T DSD_i =S_PS DSD_i +S_PS 2 DSD_i (6)

[0089] Among them, S_PS_T DSD_i S_PS represents the total influence of the i-th geometric aberration term on the PS of the developed pattern information. DSD_i S_PS represents the influence of the i-th geometric aberration term Zi on the PS in the developed pattern information. 2 DSD_i Let represent the influence of the second-order effect of the i-th geometric aberration term Zi on PS.

[0090] According to embodiments of this disclosure, the total influence of geometric aberration terms on the CD and PS of the developed pattern information is calculated based on the first coefficient of the first relational model and the second coefficient of the second relational model, respectively. At the same time, geometric aberrations and the second-order coupling effect between aberrations are taken into account, making the data on aberration influence more comprehensive and reliable.

[0091] According to embodiments of this disclosure, determining a target coefficient from I coefficients includes: determining a first target coefficient from M first coefficients; and determining a second target coefficient from N second coefficients.

[0092] According to embodiments of this disclosure, a first target coefficient is used to characterize the coefficients corresponding to geometric aberration terms that have a high influence on the experimental pattern linewidth information in the developed pattern information. The influence can characterize the total influence of geometric aberration terms on the CD of the developed pattern information.

[0093] According to embodiments of this disclosure, the second target coefficient is used to characterize the coefficients among the second coefficients corresponding to geometric aberration terms that have a high influence on the experimental pattern position offset information in the developed pattern information. The influence can characterize the total influence of the geometric aberration terms on the PS of the developed pattern information.

[0094] According to an embodiment of this disclosure, determining a first target coefficient from M first coefficients includes: sorting the M first coefficients to obtain a first sorting result regarding pattern linewidth information; and determining the first target coefficient from the M first coefficients based on the first sorting result and a first predetermined threshold.

[0095] According to embodiments of this disclosure, the first sorting result can characterize the result of sorting M first coefficients from largest to smallest based on the first coefficients corresponding to the developed pattern CD information.

[0096] According to embodiments of this disclosure, the first predetermined threshold can be used to characterize a threshold that is manually preset according to actual needs, and this disclosure does not limit it.

[0097] According to an embodiment of this disclosure, determining a second target coefficient from N second coefficients includes: sorting the N second coefficients to obtain a second sorting result regarding pattern position offset information; and determining the second target coefficient from the N second coefficients based on the second sorting result and a second predetermined threshold.

[0098] According to embodiments of this disclosure, the second sorting result can characterize the result of sorting N second coefficients from largest to smallest based on the second coefficients corresponding to the PS information of the developed pattern.

[0099] According to embodiments of this disclosure, the second predetermined threshold can be used to characterize a threshold that is manually preset according to actual needs, and this disclosure does not limit this. According to embodiments of this disclosure, when performing aberration modeling, the more geometric aberrations considered, the higher the model accuracy; however, the more aberrations selected, the longer the computation time. Therefore, by determining the first target coefficient and the second target coefficient according to actual needs, the number of selected aberrations is reduced, thereby reducing computation time without sacrificing aberration accuracy.

[0100] According to an embodiment of this disclosure, determining a first target coefficient from M first coefficients based on a first sorting result and a first predetermined threshold includes: calculating a first total coefficient sum based on the M first coefficients; accumulating m first coefficients from the M first coefficients according to the first sorting result to obtain a first coefficient sum, where m is a positive integer less than M and greater than 1; obtaining a first ratio based on the proportion of the first coefficient sum to the first total coefficient sum; and determining the m first coefficients as the first target coefficient if the first ratio is greater than or equal to the first predetermined threshold.

[0101] According to embodiments of this disclosure, the sum of the first coefficients can be used to represent the sum of the total influence of all geometric aberration terms on the CD of the developed pattern information. For example, the coefficients of the selected geometric aberration terms can be determined according to formula (7) by considering the total influence of the selected geometric aberration terms on the CD of the developed pattern information.

[0102]

[0103] Where n_DSD_cd is the number of selected geometric aberration terms, which correspond to the m first coefficients mentioned above. D is the total number of geometric aberration terms, which corresponds to the M first coefficients mentioned above. T is the first predetermined threshold.

[0104] According to embodiments of this disclosure, determining a second target coefficient from N second coefficients based on a second sorting result and a second predetermined threshold includes: calculating a second total coefficient sum based on the N second coefficients; accumulating n second coefficients from the N second coefficients according to the second sorting result to obtain a second coefficient sum, where n is a positive integer less than N and greater than 1; obtaining a second ratio based on the proportion of the second coefficient sum to the second total coefficient sum; and determining the n second coefficients as the second target coefficient if the second ratio is greater than or equal to the second predetermined threshold.

[0105] According to embodiments of this disclosure, the sum of the second coefficients of the second coefficient can be used to represent the sum of the total influence of all geometric aberration terms on the PS of the developed pattern information. For example, the coefficient of the selected geometric aberration term can be determined according to formula (8) by the total influence of the selected geometric aberration term on the PS of the developed pattern information.

[0106]

[0107] Where n_DSD_ps is the number of selected geometric aberration terms, which correspond to the n second coefficients mentioned above. D is the total number of geometric aberration terms, which corresponds to the N second coefficients mentioned above. T is the second predetermined threshold.

[0108] According to embodiments of this disclosure, the accuracy of the determined first and second target coefficients is improved by determining the coefficients and their proportions to the total sum of coefficients.

[0109] According to embodiments of this disclosure, the aberration information is optimized based on the geometric aberration term corresponding to the target coefficient among I geometric aberration terms to obtain target aberration information, including: constructing a first geometric aberration set including M first target geometric aberration terms based on I geometric aberration terms and M first target coefficients; constructing a second geometric aberration set including N second target geometric aberration terms based on I geometric aberration terms and N second target coefficients; and optimizing the aberration information based on the first geometric aberration set and the second geometric aberration set to obtain target aberration information.

[0110] According to embodiments of this disclosure, target aberration information can be used to characterize the aberration information of geometric aberration terms corresponding to the first target coefficient and the second target coefficient obtained from the aberration information.

[0111] According to embodiments of this disclosure, M first target geometric aberration terms correspond to M first target coefficients; N second target geometric aberration terms correspond to N second target coefficients.

[0112] According to embodiments of this disclosure, aberration information is optimized to obtain target aberration information of geometric aberration terms corresponding to target coefficients, thus solving the problem of excessive computation time caused by too many selected geometric aberration terms in the prior art.

[0113] According to embodiments of this disclosure, optimizing aberration information based on a first set of geometric aberrations and a second set of geometric aberrations to obtain target aberration information includes: determining the union of the first set of geometric aberrations and the second set of geometric aberrations as the target set of geometric aberrations; and optimizing the aberration information based on the geometric aberration terms in the target set of geometric aberrations to obtain the target aberration information.

[0114] According to embodiments of this disclosure, target aberration information can be represented as the final aberration information obtained by optimizing the aberration information of geometric aberration terms in the target geometric aberration set obtained by the union of the first target geometric aberration set and the second target geometric aberration set. The influence of geometric aberration terms on the developed pattern information needs to consider both CD and PS, and the final aberration information needs to be optimized based on the union of the first target geometric aberration set and the second target geometric aberration set.

[0115] According to embodiments of this disclosure, the target geometric aberration set can be obtained according to the following formula (9):

[0116] Z DSD =Z_CD DSD ∪Z_PS DSD (9)

[0117] Among them, Z DSD For the target geometric aberration set. Z_CD DSD Z_PS can represent the geometric aberration set of the first target. DSD ∪ can represent the second target geometric aberration set, and ∪ can represent the union of the two sets.

[0118] According to embodiments of this disclosure, a first geometric aberration set and a second geometric aberration set are used to optimize aberration information. The resulting target aberration information can largely represent the influence of all geometric aberrations, but reduces the number of selected target aberrations and reduces computation time.

[0119] To better understand the above content, this disclosure combines... Figures 3-7 The following embodiments are also provided to illustrate the above aberration processing method.

[0120] According to embodiments of this disclosure, the aberration processing method is applied to various patterns under different illumination conditions in an extreme ultraviolet (EUV) lithography system, considering cases at 13 different field-of-view positions. For optical surface types with the same linewidth, the wavefront aberration of the EUV lithography system is approximately 10 times that of the deep ultraviolet (DUV) lithography system. Experimental conditions are shown in Table 1.

[0121] Table 1

[0122]

[0123]

[0124] Figure 3 The diagram illustrates an image pattern of a photolithography system under diode illumination conditions according to an embodiment of the present disclosure.

[0125] Figure 4A schematic diagram of an imaged pattern under ring illumination conditions is shown for a lithography system according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the first 37 geometric aberration terms are considered, with aberration variations ranging from -20mλ to 20mλ. Target aberration information affecting pattern imaging is identified using a deterministic screening experiment.

[0126] Referring to step S210 in embodiment 200 above, the experimental development pattern information and the experimental aberration information corresponding to the experimental development pattern information are input into a relationship model, wherein the aberration level is 0, -20mλ, and 20mλ, and the experimental scheme includes 86 sets of experiments.

[0127] Referring to step S210 in the above embodiment 200, the influence of each geometric aberration term on the CD and PS of the developed pattern information is calculated according to the target coefficient, and then sorted.

[0128] According to embodiments of this disclosure, in order to demonstrate the necessity of optimizing the geometric aberration set to obtain the target geometric aberration set, predetermined thresholds of 70%, 80%, and 90% are preset. Furthermore, since the deterministic screening experiment excludes geometric aberration terms with low influence when calculating model coefficients, a predetermined threshold of 100% is set. These preset thresholds correspond to the aforementioned first and second predetermined thresholds.

[0129] According to embodiments of this disclosure, the average aberrations selected for each lighting condition are shown in Table 2.

[0130] Table 2

[0131]

[0132]

[0133] Referring to step S230 in the above embodiment 200, the union of the first target geometric aberration set and the second target geometric aberration set is determined as the target geometric aberration set.

[0134] According to embodiments of this disclosure, random experiments can be conducted using experimental aberration coefficients for 37 geometric aberration terms, and development pattern parameters can be collected. These development pattern parameters can then be used as development pattern parameters considering all geometric aberration terms. Alternatively, the values ​​corresponding to the target geometric aberration term can be kept unchanged in the above experiments, while the values ​​of unselected geometric aberration terms can be set to 0. The experiments can then be conducted again, and development pattern information can be collected as development pattern information considering only the target geometric aberration term. The deviation between the development pattern information considering all geometric aberration terms and considering only the target geometric aberration term can be compared for the light source and pattern under study.

[0135] Figure 5The diagram illustrates the relationship between a predetermined threshold for target geometric aberrations, development pattern information deviation, and the percentage of target geometric aberrations in a photolithography system with diode illumination and vertical lines according to an embodiment of the present disclosure.

[0136] like Figure 5 As shown, the horizontal axis represents the proportion of the influence information of the target geometric aberration term to the influence of all geometric aberration terms, and the vertical axis on the right represents the proportion of the number of target geometric aberration terms. The vertical axis on the left represents the deviation between the developed pattern information considering all geometric aberration terms and the developed pattern information considering only the target geometric aberration term.

[0137] According to embodiments of this disclosure, the selected average proportion of CD aberrations refers to the ratio of the average number of geometric aberration terms corresponding to the first target coefficient to the total number of geometric aberration terms in the aberration information. The selected average proportion of PS aberrations refers to the ratio of the average number of geometric aberration terms corresponding to the second target coefficient to the total number of geometric aberration terms in the aberration information. The selected average proportion of aberrations refers to the ratio of the average number of the union of the geometric aberration terms corresponding to the first target coefficient and the geometric aberration terms corresponding to the second target coefficient to the total number of geometric aberration terms in the aberration information.

[0138] Figure 6 The diagram illustrates the relationship between a predetermined threshold for target geometric aberrations, development pattern information deviation, and the percentage of target geometric aberrations in a lithography system with ring illumination and vertical lines according to an embodiment of the present disclosure.

[0139] like Figure 6 As shown, the horizontal axis represents the proportion of the influence of the target geometric aberration to the influence of all geometric aberrations, the vertical axis on the right represents the proportion of the number of target geometric aberrations, and the vertical axis on the left represents the deviation between the number of developed pattern information considering all geometric aberrations and the number of developed pattern information considering only the target geometric aberration.

[0140] Figure 7 The diagram illustrates the relationship between a predetermined threshold for a target geometric aberration term, a development pattern information deviation, and the percentage of the number of target geometric aberration terms in a lithography system under ring illumination-horizontal line conditions according to an embodiment of the present disclosure.

[0141] like Figure 7 As shown, the horizontal axis represents the proportion of the influence of the target geometric aberration to the influence of all geometric aberrations, the vertical axis on the right represents the proportion of the number of target geometric aberrations, and the vertical axis on the left represents the deviation between the developed pattern information considering all geometric aberrations and the developed pattern information considering only the target geometric aberration.

[0142] According to embodiments of this disclosure, based on the comparison of the above experimental results, the deterministic screening experiment achieved high accuracy, while significantly reducing the number of geometric aberration terms corresponding to the target coefficients. Specifically, by selecting no more than 50% of the geometric aberration terms, a 90% proportion of geometric aberration terms corresponding to the target coefficients can be achieved, with an average CD deviation of no more than 1% and an average PS deviation of no more than 0.3%.

[0143] Based on the above aberration processing methods, this disclosure also provides an aberration processing device based on a deterministic screening experiment. The following will be combined with... Figure 8 The device is described in detail.

[0144] Figure 8 A schematic block diagram of an aberration processing apparatus according to an embodiment of the present disclosure is shown.

[0145] like Figure 8 As shown, the aberration processing apparatus 800 of this embodiment includes a fitting module 810, a determination module 820, and an optimization module 830.

[0146] The fitting module 810 is used to input the experimental development pattern information of the photolithography system and the experimental aberration information corresponding to the experimental development pattern information into the relational model, and to fit I coefficients of the relational model. In one embodiment, the fitting module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0147] The determination module 820 is used to determine the target coefficient from the I coefficients of the relational model. In one embodiment, the determination module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0148] The optimization module 830 is used to optimize the aberration information based on the geometric aberration term corresponding to the target coefficient among the I geometric aberration terms, to obtain the target aberration information. In one embodiment, the optimization module 830 can be used to perform the operation S230 described above, which will not be repeated here.

[0149] According to embodiments of this disclosure, the fitting module 810 includes a first fitting submodule and a second fitting submodule.

[0150] The first fitting submodule is used to input the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into the first relational model, and fit to obtain M first coefficients of the first relational model.

[0151] The second fitting submodule is used to input the experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information into the second relational model, and to fit and obtain N second coefficients of the second relational model.

[0152] According to embodiments of this disclosure, the first fitting submodule includes a fitting unit, a first calculation unit, and a second calculation unit.

[0153] The fitting unit is used to input the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into the first relational model, and fit to obtain M first initial coefficients of the first relational model;

[0154] The first calculation unit is used to process M first initial coefficients using a first predetermined function, and calculate the second-order coefficients corresponding to each of the M first initial coefficients.

[0155] The second calculation unit is used to add the M first initial coefficients and the second-order coefficients corresponding to the M first initial coefficients to obtain the M first coefficients.

[0156] According to embodiments of this disclosure, the determining module 820 includes a first determining submodule and a second determining submodule.

[0157] The first determining submodule is used to determine the first target coefficient from M first coefficients.

[0158] The second determination submodule is used to determine the second target coefficient from N second coefficients.

[0159] According to embodiments of this disclosure, the first determining submodule includes a sorting unit and a determining unit.

[0160] The sorting unit is used to sort the M first coefficients to obtain the first sorting result regarding the pattern linewidth information;

[0161] The determining unit is used to determine a first target coefficient from M first coefficients based on a first sorting result and a first predetermined threshold.

[0162] According to embodiments of this disclosure, the determining unit includes a calculation subunit, an accumulation subunit, an acquisition subunit, and a determining subunit.

[0163] The calculation subunit is used to calculate the first total coefficient sum based on the M first coefficients.

[0164] The accumulator subunit is used to accumulate m of the M first coefficients according to the first sorting result to obtain the sum of the first coefficients, where m is a positive integer less than M and greater than 1.

[0165] Obtain a sub-unit to obtain a first ratio based on the proportion of the first coefficient to the first total coefficient.

[0166] A sub-unit is defined to determine m first coefficients as first target coefficients when the first ratio is greater than or equal to a first predetermined threshold.

[0167] According to embodiments of this disclosure, the optimization module 830 includes a first construction submodule, a second construction submodule, and an optimization submodule.

[0168] The first construction submodule is used to construct a first geometric aberration set including M first target geometric aberration terms based on I geometric aberration terms and M first target coefficients.

[0169] The second construction submodule is used to construct a second geometric aberration set including N second target geometric aberration terms based on I geometric aberration terms and N second target coefficients.

[0170] The optimization submodule is used to optimize the aberration information based on the first geometric aberration set and the second geometric aberration set to obtain the target aberration information.

[0171] According to embodiments of this disclosure, the optimization submodule includes a determining unit and a obtaining unit.

[0172] The determining unit is used to determine the target geometric aberration set as the union of the first target geometric aberration set and the second target geometric aberration set;

[0173] The unit is used to optimize the aberration information based on the geometric aberration terms in the target geometric aberration set to obtain the target aberration information.

[0174] According to embodiments of this disclosure, any plurality of modules among fitting module 810, determining module 820, and optimizing module 830 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of fitting module 810, determining module 820, and optimizing module 830 may 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 circuitry, or implemented in any one of software, hardware, and firmware methods, or in a suitable combination of any of these. Alternatively, at least one of fitting module 810, determining module 820, and optimizing module 830 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0175] Figure 9 A block diagram schematically illustrates an electronic device suitable for implementing an aberration processing method according to an embodiment of the present disclosure.

[0176] like Figure 9As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 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 901 may also include onboard memory for caching purposes. The processor 901 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.

[0177] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of aberration processing according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0178] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0179] 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.

[0180] 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 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0181] When the computer program is executed by the processor 901, 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.

[0182] 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 downloaded and installed via the communication section 909, and / or installed from a removable medium 911. 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.

[0183] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 aberration processing method based on a deterministic screening experiment, comprising: The experimental development pattern information of the lithography system and the experimental aberration information corresponding to the experimental development pattern information are input into the relational model, and I coefficients of the relational model are fitted. The experimental aberration information and the experimental development pattern information are obtained by the object using the lithography system to perform the deterministic screening experiment. The relational model is constructed based on the correlation between aberration information and development pattern information. The aberration information includes I geometric aberration terms corresponding to the I coefficients. The coefficients are used to characterize the influence of the geometric aberration terms corresponding to the coefficients on the development pattern information. I is a positive integer greater than 1. Determine the target coefficient from the I coefficients of the relational model; Based on the geometric aberration term corresponding to the target coefficient among the I geometric aberration terms, the aberration information is optimized to obtain the target aberration information; The experimental development pattern information includes experimental pattern linewidth information and experimental pattern position offset information. The relationship model includes a first relationship model and a second relationship model. The first relationship model is constructed based on the correlation between the aberration information and the pattern linewidth information, and the second relationship model is constructed based on the correlation between the aberration information and the pattern position offset information. The experimental development pattern information of the photolithography system and the experimental aberration information corresponding to the experimental development pattern information are input into a relational model, and I coefficients of the relational model are fitted, including: The experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information are input into the first relational model, and M first coefficients of the first relational model are obtained by fitting, where M is a positive integer greater than 1; The experimental pattern position offset information and the experimental aberration information corresponding to the experimental pattern position offset information are input into the second relation model, and N second coefficients of the second relation model are obtained by fitting, where N is a positive integer greater than 1, and M+N=1; The step of inputting the experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information into a first relational model, and fitting M first coefficients of the first relational model, includes: The experimental pattern linewidth information and the experimental aberration information corresponding to the experimental pattern linewidth information are input into the first relational model, and M first initial coefficients of the first relational model are obtained by fitting. The first predetermined function is used to process the M first initial coefficients to calculate the second-order coefficients corresponding to each of the M first initial coefficients. The M first initial coefficients are obtained by adding the M first initial coefficients and the second-order coefficients corresponding to each of the M first initial coefficients; The step of determining the target coefficient from the I coefficients includes: Determine the first target coefficient from the M first coefficients; Determine the second target coefficient from the N second coefficients; The step of determining the first target coefficient from the M first coefficients includes: The M first coefficients are sorted to obtain a first sorting result regarding the pattern linewidth information; Based on the first sorting result and the first predetermined threshold, the first target coefficient is determined from the M first coefficients; The step of determining the first target coefficient from the M first coefficients based on the first sorting result and the first predetermined threshold includes: Based on the M first coefficients, a first total coefficient sum is calculated, which represents the total influence of all geometric aberration terms corresponding to the first coefficients on the line width information of the experimental pattern. According to the first sorting result, sum m of the first coefficients out of M first coefficients to obtain the sum of the first coefficients, where m is a positive integer less than M and greater than 1; The first ratio is obtained based on the proportion of the first coefficient to the sum of the first total coefficients; If the first ratio is greater than or equal to the first predetermined threshold, the m first coefficients are determined as the first target coefficients.

2. The method according to claim 1, wherein, The step of optimizing the aberration information based on the geometric aberration term corresponding to the target coefficient among the I geometric aberration terms to obtain target aberration information includes: Based on the I geometric aberration terms and the M first target coefficients, a first geometric aberration set is constructed, comprising M first target geometric aberration terms, wherein the M first target geometric aberration terms correspond to the M first target coefficients; Based on the I geometric aberration terms and the N second target coefficients, a second geometric aberration set is constructed, comprising N second target geometric aberration terms, wherein the N second target geometric aberration terms correspond to the N second target coefficients; Based on the first geometric aberration set and the second geometric aberration set, the aberration information is optimized to obtain the target aberration information.

3. The method according to claim 2, wherein, The step of optimizing the aberration information based on the first geometric aberration set and the second geometric aberration set to obtain target aberration information includes: The union of the first target geometric aberration set and the second target geometric aberration set is determined as the target geometric aberration set; Based on the geometric aberration terms in the target geometric aberration set, the aberration information is optimized to obtain the target aberration information.

4. 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 3.

5. 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 3.

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