Aberration analysis method and device of extreme ultraviolet lithography system and electronic equipment

Abstract

The invention provides an aberration analysis method and device of an extreme ultraviolet lithography system and electronic equipment, which can be applied to the technical field of aberration optimization and analysis of high-resolution imaging systems. The aberration analysis method of the extreme ultraviolet lithography system comprises the following steps: acquiring photoetching parameter information of the extreme ultraviolet lithography system; the photoetching parameter information is processed through an aberration influence prediction model, photoetching influence distribution information is obtained, the aberration influence prediction model is constructed according to the incidence relation between the photoetching parameter information and the photoetching influence distribution information, and the aberration influence prediction model is constructed according to the incidence relation between the photoetching parameter information and the photoetching influence distribution information. The photoetching influence distribution information comprises any one of aberration distribution information and exposure pattern distribution information, and the photoetching influence distribution information is used for representing the influence degree of the inconsistency degree between the actual wavefront and the ideal wavefront on the photoetching parameter information of the extreme ultraviolet photoetching system; and analyzing the photoetching influence distribution information to obtain a target error influence analysis result of the extreme ultraviolet photoetching system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aberration optimization and analysis of high-resolution imaging systems, and more particularly to a method, apparatus, and electronic device for aberration analysis of an extreme ultraviolet lithography system. Background Art

[0002] The lithography process is one of the key processes in integrated circuit manufacturing. Through the lithography process, patterns can be transferred onto photosensitive materials, thereby etching a predetermined circuit pattern on a substrate to achieve a circuit function corresponding to the circuit pattern. However, in an actual optical system, due to factors such as diffraction limitation, processing, and manufacturing errors in the optical system, the propagation direction and phase of the ideal wavefront on the image plane are changed, and there is an aberration between the actual wavefront and the ideal wavefront.

[0003] In the process of implementing the above inventive concept, the inventors found that: due to the limitations of optical parameters and manufacturing processes in the prior art, the aberration between the actual wavefront and the ideal wavefront causes errors in the lithography results, and the influence of the aberration on the lithography results is difficult to verify and predict, thus making it impossible to optimize relevant optical parameters or process parameters. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, and electronic device for aberration analysis of an extreme ultraviolet lithography system.

[0005] According to a first aspect of the present disclosure, there is provided a method for aberration analysis of an extreme ultraviolet lithography system, including: obtaining lithography parameter information of the extreme ultraviolet lithography system; processing the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, where the aberration influence prediction model is constructed according to the correlation between the lithography parameter information and the lithography influence distribution information, and the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system; analyzing the lithography influence distribution information to obtain an analysis result of the target error influence of the extreme ultraviolet lithography system.

[0006] According to an embodiment of the present disclosure, the lithography parameter information includes any one of aberration information and exposure pattern information; processing the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information includes: inputting the aberration information into the aberration influence prediction model for distribution prediction processing to obtain exposure pattern distribution information, or inputting the exposure pattern information into the aberration influence prediction model for distribution prediction processing to obtain aberration distribution information.

[0007] According to an embodiment of the present disclosure, an aberration influence prediction model is constructed through the following operations, including: based on a Poisson distribution model, using aberration information and exposure pattern change information as variables, and using an exposure pattern distribution range statistical value and a correction factor as parameters, constructing an aberration influence prediction model, where the exposure pattern change distribution information is used to characterize the degree of change in the line width of the exposure pattern of an extreme ultraviolet lithography system deviating from a predetermined line width under the influence of aberration information.

[0008] According to an embodiment of the present disclosure, based on a Poisson distribution model, using aberration information and exposure pattern change information as variables, and using an exposure pattern distribution range statistical value and a correction factor as parameters, constructing an aberration influence prediction model includes: based on predetermined exposure pattern change distribution information, obtaining an initial prediction model, where the initial prediction model is constructed according to a Poisson distribution model and a gamma function; based on the correction factor, the exposure pattern distribution range statistical value, and the exposure pattern change information, constructing an exposure image change number relationship sub-model and an exposure image change distribution relationship sub-model of the initial prediction model, where the exposure image change number relationship sub-model is constructed according to the correction factor and the exposure pattern change information, and the exposure image change distribution relationship sub-model is constructed according to the correction factor and the exposure pattern distribution range statistical value; processing the initial prediction model according to the exposure image change number relationship sub-model and the exposure image change distribution relationship sub-model to obtain an intermediate prediction model; using a predetermined correction factor aberration relationship model to process the intermediate prediction model to obtain an aberration influence prediction model, where the predetermined correction factor aberration relationship model is constructed with aberration information as an independent variable and correction factor coefficient information.

[0009] According to an embodiment of the present disclosure, the correction factor coefficient information is obtained through the following operations, including: based on the predetermined exposure pattern change distribution information, using a genetic algorithm to determine the correction factor parameter value in the intermediate prediction model; performing polynomial fitting according to the predetermined aberration information and the correction factor parameter value to obtain the correction factor coefficient information.

[0010] According to an embodiment of the present disclosure, the predetermined exposure pattern change distribution information is obtained in the following manner, including: obtaining predetermined exposure pattern change information corresponding one-to-one to each of a plurality of predetermined aberration information; based on predetermined aberration range information, performing a partitioning process on the plurality of predetermined aberration information to obtain a plurality of predetermined aberration intervals; according to each predetermined aberration interval and the predetermined exposure pattern change information corresponding to each predetermined aberration interval, obtaining initial exposure pattern change distribution information corresponding to each predetermined aberration interval; performing a normalization process on the initial pattern change distribution information corresponding to each predetermined aberration interval to obtain the predetermined exposure pattern change distribution information corresponding to each predetermined aberration interval.

[0011] According to an embodiment of the present disclosure, the method further includes: obtaining an aberration information model, where the aberration information model includes a plurality of geometric aberration terms, and each geometric aberration term corresponds to an aberration coefficient; obtaining a plurality of random coefficient groups by performing random experiments on the aberration information model, where each random coefficient group includes a plurality of aberration coefficients that are the same in number as the geometric aberration terms; inputting the plurality of random coefficient groups into the aberration information model for processing to obtain a plurality of predetermined aberration information; and obtaining a plurality of predetermined exposure pattern change information according to the plurality of predetermined aberration information and the sample exposure width information.

[0012] According to an embodiment of the present disclosure, obtaining a plurality of predetermined exposure pattern change information according to the plurality of predetermined aberration information and the sample exposure width information includes: obtaining experimental exposure width information corresponding to each predetermined aberration information; and obtaining a plurality of predetermined exposure pattern change information based on a predetermined exposure pattern change degree function according to the plurality of experimental exposure width information and the sample exposure width information.

[0013] A second aspect of the present disclosure provides an aberration analysis device for an extreme ultraviolet lithography system, including: an acquisition module, configured to acquire lithography parameter information of the extreme ultraviolet lithography system; a processing module, configured to process the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, where the aberration influence prediction model is constructed according to the correlation between the lithography parameter information and the lithography influence distribution information, and the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system; and an analysis module, configured to analyze the lithography influence distribution information to obtain an analysis result of the target error influence of the extreme ultraviolet lithography system.

[0014] A third aspect of the present disclosure provides an electronic device, including: one or more processors; and a memory, configured to store one or more programs, where when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above method.

[0015] A fourth aspect of the present disclosure further provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor is caused to execute the above method.

[0016] A fifth aspect of the present disclosure further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0017] Method, apparatus and electronic device for aberration analysis of extreme ultraviolet lithography system according to the present disclosure. By first obtaining the lithography parameter information of the current extreme ultraviolet lithography system, inputting the lithography parameter information into the aberration influence prediction model, and using the aberration influence prediction model constructed based on the correlation between the lithography parameter information and the lithography influence distribution information to perform information analysis and processing on the input lithography parameter information, the aberration distribution information or exposure pattern distribution information of the current extreme ultraviolet lithography system is obtained. According to the aberration distribution information or exposure pattern distribution information, the current extreme ultraviolet lithography system is analyzed, so as to obtain the target error influence analysis result. It realizes that the aberration distribution information or exposure pattern distribution information with higher influence analysis accuracy can be obtained according to the lithography parameter information related to the extreme ultraviolet lithography system. Thus, based on the aberration distribution information approaching the Poisson distribution, aberration influence information such as the aberration probability of the current extreme ultraviolet lithography system can be analyzed. Also, based on the exposure pattern distribution information approaching the Poisson distribution, information such as the influence of aberration on the lithography pattern of the current extreme ultraviolet lithography system can be analyzed, so as to provide a reference selection basis for relevant parameter indicators for the manufacture of extreme ultraviolet lithography lenses based on the target error influence analysis result analyzed from the relatively accurate lithography influence distribution information, and to provide a relevant optimization direction for the control guidance of aberration. Further, under the condition of making full use of the computing power of the computer, based on the aberration influence prediction model, the lithography process is optimized from multiple dimensions, the imaging quality of the lithography pattern is improved, and the efficiency of analyzing the influence of aberration on the lithography result is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Through the following description of the embodiments of the present disclosure with reference to the drawings, the above content and other objects, features and advantages of the present disclosure will become clearer. In the drawings:

[0019] Figure 1 The application scenario diagram of the method for aberration analysis of the extreme ultraviolet lithography system according to the embodiment of the present disclosure is shown;

[0020] Figure 2 The flowchart of the method for aberration analysis of the extreme ultraviolet lithography system according to the embodiment of the present disclosure is shown;

[0021] Figure 3 The schematic diagram of the relative distribution of the predetermined exposure pattern change information according to the embodiment of the present disclosure is shown;

[0022] Figure 4 The schematic diagram of the predetermined exposure pattern change distribution information in multiple predetermined aberration intervals according to the embodiment of the present disclosure is shown;

[0023] Figure 5 The schematic diagram of the lithography influence distribution information obtained through the aberration influence prediction model of the present disclosure according to the embodiment of the present disclosure is shown;

[0024] Figure 6a A schematic diagram showing the relationship between a scaling factor and predetermined aberration information according to an embodiment of the present disclosure;

[0025] Figure 6b A schematic diagram showing the relationship between a displacement factor and predetermined aberration information according to an embodiment of the present disclosure;

[0026] Figure 6c A schematic diagram showing the relationship between a desired transformation factor and predetermined aberration information according to an embodiment of the present disclosure;

[0027] Figure 7 A structural block diagram of an apparatus for aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure;

[0028] Figure 8 A block diagram of an electronic device for a method of aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure. Detailed implementation manners

[0029] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

[0030] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described 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 of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0032] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning commonly understood by those of ordinary skill in the art (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C).

[0033] In the technical solution of the present disclosure, the user information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties. Moreover, the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, complies with relevant laws, regulations, and standards, adopts necessary confidentiality measures, does not violate public order and good customs, and provides corresponding operation entrances for users to choose to authorize or refuse.

[0034] Lithography is one of the key processes in integrated circuit manufacturing. Lithography can help complete the transfer of patterns, thereby realizing specific circuit functions. With the continuous advancement of Moore's Law, the critical dimensions in lithography are constantly shrinking, and the lithography difficulty is increasing. The pattern size has approached the resolution limit, bringing great challenges to the lithography process, and the requirements for lithography results are also becoming more and more strict. If the process tolerance is 10%, the maximum deviation of the exposure result for a 14nm line pattern is about 1.4nm. The deviation of the lithography result comes from the fluctuations of parameters in the lithography system or process. In order to better control each parameter and obtain an acceptable lithography result, it is necessary to accurately master the influence characteristics of each parameter on the lithography result. In the current most advanced integrated circuit manufacturing, the aberration in the extreme ultraviolet lithography system is one of the most representative parameters.

[0035] From the perspective of optical design, zero aberration of the projection optical system is an ideal design. However, in an actual optical system, due to factors such as the diffraction limit of the optical system and processing and manufacturing errors, the propagation direction and phase of the ideal wavefront on the image plane are changed, and there is aberration between the actual wavefront and the ideal wavefront. The influence of aberration on the lithography imaging quality usually can be manifested as problems such as the reduction of the process window, the distortion and decrease of fidelity of the pattern, and the movement and asymmetry of the pattern center position.

[0036] However, since the influences of different aberrations on the lithography result are all different, it is difficult to predict the influence of aberration on the lithography result, which in turn brings trouble to the manufacturing of extreme ultraviolet lithography lenses and aberration control and optimization. During the R & D process, the R & D personnel found that due to the limitations of the optical parameters and preparation processes in the prior art, the aberration between the actual wavefront and the ideal wavefront causes errors in the lithography result, and it is difficult to verify and predict the influence of aberration on the lithography result, thus unable to optimize the relevant optical parameters or process parameters.

[0037] In view of this, embodiments of the present disclosure provide a method for aberration analysis of an extreme ultraviolet lithography system, including: obtaining lithography parameter information of the extreme ultraviolet lithography system; processing the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, where the aberration influence prediction model is constructed according to the correlation between the lithography parameter information and the lithography influence distribution information, the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system; analyzing the lithography influence distribution information to obtain the target error influence analysis result of the extreme ultraviolet lithography system.

[0038] Figure 1 FIG. shows an application scenario diagram of the method for aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure.

[0039] As Figure 1 shown, the application scenario according to this embodiment may include a lithography system 101 and a server 102. The network may be a medium for providing 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.

[0040] The server 102 may collect aberration information and experimental development patterns of the lithography system 101 through the network.

[0041] The server 102 may be a server that provides various services, such as a background management server (for example only). The background management server may analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0042] It should be noted that the aberration processing method provided by the embodiments of the present disclosure may generally be executed by the server 102. Correspondingly, the aberration processing device provided by the embodiments of the present disclosure may generally be set in the server 102. The aberration processing method provided by the embodiments of the present disclosure may also be executed by a server or a server cluster different from the server 102 and capable of communicating with the lithography system 101 and / or the server 102. Correspondingly, the aberration processing device provided by the embodiments of the present disclosure may also be set in a server or a server cluster different from the server 102 and capable of communicating with the lithography system 101 and / or the server 102.

[0043] It should be understood that Figure 1 the number of servers and lithography systems in

[0044] The following will be based on Figure 1 the described scenario, and will detail a method for aberration analysis of an extreme ultraviolet lithography system of a disclosed embodiment through Figures 2 to 6c the aberration analysis method of the extreme ultraviolet lithography system of the disclosed embodiment will be described in detail.

[0045] Figure 2 FIG. shows a flowchart of a method for aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure.

[0046] As Figure 2 shown, the aberration analysis method of the extreme ultraviolet lithography system of this embodiment includes operations S210 to S230.

[0047] In operation S210, lithography parameter information of the extreme ultraviolet lithography system is obtained.

[0048] According to an embodiment of the present disclosure, based on the lithography parameter information of the extreme ultraviolet lithography system, the influence distribution of the current aberration on the lithography result can be estimated using an aberration influence prediction model, so that the parameters in the lithography system can be adjusted according to the distribution condition to optimize subsequent processes and aberration control, etc.

[0049] In operation S220, the lithography parameter information is processed using the aberration influence prediction model to obtain lithography influence distribution information.

[0050] According to an embodiment of the present disclosure, the aberration influence prediction model is constructed based on the correlation relationship between the lithography parameter information and the lithography influence distribution information, and the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information.

[0051] According to an embodiment of the present disclosure, both the aberration distribution information and the exposure pattern distribution information can be distribution information close to the Poisson distribution.

[0052] According to an embodiment of the present disclosure, the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system.

[0053] In operation S230, the lithography influence distribution information is analyzed to obtain the target error influence analysis result of the extreme ultraviolet lithography system.

[0054] According to an embodiment of the present disclosure, by analyzing the aberration distribution information or the exposure pattern distribution information close to the Poisson distribution, the target error influence analysis result can be obtained, so as to adjust the system parameters in the lithography system based on the target error influence analysis result.

[0055] According to an embodiment of the present disclosure, by first obtaining the lithography parameter information of the current extreme ultraviolet lithography system, inputting the lithography parameter information into the aberration influence prediction model, and performing information analysis and processing on the input lithography parameter information by using the aberration influence prediction model constructed based on the correlation between the lithography parameter information and the lithography influence distribution information, the aberration distribution information or the exposure pattern distribution information of the current extreme ultraviolet lithography system is obtained. According to the aberration distribution information or the exposure pattern distribution information, the current extreme ultraviolet lithography system is analyzed, so as to obtain the target error influence analysis result. It is realized that the aberration distribution information or the exposure pattern distribution information with higher influence analysis accuracy can be obtained according to the lithography parameter information related to the extreme ultraviolet lithography system. Thus, based on the aberration distribution information approaching the Poisson distribution, aberration influence information such as the aberration probability of the current extreme ultraviolet lithography system can be analyzed. Also, based on the exposure pattern distribution information approaching the Poisson distribution, information such as the influence of aberration on the lithography pattern of the current extreme ultraviolet lithography system can be analyzed. This is convenient for providing a reference selection basis for relevant parameter indicators for the manufacture of extreme ultraviolet lithography lenses based on the target error influence analysis result analyzed from the relatively accurate lithography influence distribution information, and for providing a relevant optimization direction for aberration control guidance. Further, under the condition of making full use of the computing power of the computer, based on the aberration influence prediction model, the lithography process is optimized from multiple dimensions, the imaging quality of the lithography pattern is improved, and the efficiency of analyzing the influence of aberration on the lithography result is enhanced.

[0056] According to an embodiment of the present disclosure, the lithography parameter information includes any one of aberration information and exposure pattern information.

[0057] According to an embodiment of the present disclosure, the method of using the aberration influence prediction model to process the lithography parameter information to obtain the lithography influence distribution information further includes the following operations.

[0058] According to an embodiment of the present disclosure, input the aberration information into the aberration influence prediction model for distribution prediction processing to obtain the exposure pattern distribution information, or input the exposure pattern information into the aberration influence prediction model for distribution prediction processing to obtain the aberration distribution information.

[0059] According to an embodiment of the present disclosure, the aberration information or the exposure pattern information can be obtained first, and the obtained information is input into the aberration influence prediction model to obtain the lithography influence distribution information corresponding to the input information. Thus, based on the obtained aberration information and the exposure pattern distribution information, the influence analysis result of the exposure pattern under the influence of the aberration can be obtained. Based on the obtained exposure pattern information and the aberration distribution information, the analysis result of the distribution of the aberration of the lithography system under the exposure pattern information can be obtained.

[0060] According to an embodiment of the present disclosure, the aberration influence prediction model can be constructed through the following operations.

[0061] According to an embodiment of the present disclosure, based on the Poisson distribution model, with aberration information and exposure pattern change information as variables, and with the exposure pattern distribution range statistical value and the correction factor as parameters, an aberration influence prediction model is constructed.

[0062] According to an embodiment of the present disclosure, the exposure pattern change distribution information is used to characterize the degree of change in the line width of the exposure pattern of the extreme ultraviolet lithography system deviating from the predetermined line width under the influence of aberration information.

[0063] According to an embodiment of the present disclosure, a method for constructing an aberration influence prediction model based on the Poisson distribution model, with aberration information and exposure pattern change information as variables, and with the exposure pattern distribution range statistical value and the correction factor as parameters, includes the following operations.

[0064] According to an embodiment of the present disclosure, an initial prediction model is obtained based on the predetermined exposure pattern change distribution information.

[0065] According to an embodiment of the present disclosure, the initial prediction model is constructed according to the Poisson distribution model and the gamma function.

[0066] According to an embodiment of the present disclosure, the predetermined exposure pattern change distribution information is the distribution information obtained based on the actual influence of the aberration on the exposure pattern. The distribution information corresponding to multiple aberration intervals can be used for analysis. It can be found that the predetermined exposure pattern change distribution has an obvious tailing effect and is relatively similar to the Poisson distribution. Therefore, an initial prediction model is constructed according to the Poisson distribution model and the gamma function.

[0067] According to an embodiment of the present disclosure, the Poisson distribution model can be established according to formula (1), and formula (1) is as follows.

[0068] (1);

[0069] Wherein, P(x = k) can represent the probability that an event occurs k times in the Poisson distribution model, can represent the expectation that an event occurs in the Poisson distribution model, and k can represent the k times that an event occurs in the Poisson distribution model, where k is a non-negative integer.

[0070] According to an embodiment of the present disclosure, the initial prediction model can be established according to formula (2), and formula (2) is as follows.

[0071] (2);

[0072] Wherein, y i(k) can be characterized as the exposure pattern distribution information or aberration distribution information obtained when the aberration information in the initial prediction model is k or the exposure pattern information is k. It can be characterized as a gamma function, where k in the initial prediction model can be all real numbers.

[0073] According to an embodiment of the present disclosure, based on the correction factor, the exposure pattern distribution range statistical value, and the exposure pattern change information, an exposure image change number relationship sub-model and an exposure image change distribution relationship sub-model of the initial prediction model are constructed.

[0074] According to an embodiment of the present disclosure, the exposure image change number relationship sub-model is constructed according to the correction factor and the exposure pattern change information, and the exposure image change distribution relationship sub-model is constructed according to the correction factor and the exposure pattern distribution range statistical value.

[0075] According to an embodiment of the present disclosure, the correction factor may include a scaling factor, a displacement factor, and an expected transformation factor, and the exposure pattern distribution range statistical value may be characterized as the median value of the sum of the distribution ranges of all exposure pattern change information.

[0076] According to an embodiment of the present disclosure, the exposure image change number relationship sub-model constructed according to the correction factor and the exposure pattern change information can be established according to formula (3), and formula (3) is shown as follows.

[0077] (3);

[0078] Wherein, k can be characterized as the exposure image change number relationship sub-model in the initial prediction model, a can be characterized as the scaling factor, v can be characterized as the exposure pattern change information, and b can be characterized as the displacement factor.

[0079] According to an embodiment of the present disclosure, the exposure image change distribution relationship sub-model constructed according to the correction factor and the exposure pattern distribution range statistical value can be established according to formula (4), and formula (4) is shown as follows.

[0080] (4);

[0081] Wherein, can be characterized as the exposure image change distribution relationship sub-model in the initial prediction model, c can be characterized as the expected transformation factor, V m can be characterized as the exposure pattern distribution range statistical value.

[0082] According to an embodiment of the present disclosure, the initial prediction model is processed according to the exposure image change number relationship sub-model and the exposure image change distribution relationship sub-model to obtain an intermediate prediction model.

[0083] According to an embodiment of the present disclosure, the number-of-changes relationship sub-model of the exposure image and the distribution relationship sub-model of the exposure image changes are brought into k in the initial model and , and an intermediate prediction model is obtained.

[0084] According to an embodiment of the present disclosure, the intermediate prediction model is processed by using a predetermined correction factor aberration relationship model to obtain an aberration influence prediction model.

[0085] According to an embodiment of the present disclosure, the predetermined correction factor aberration relationship model is constructed with aberration information as the independent variable and correction factor coefficient information.

[0086] According to an embodiment of the present disclosure, the correction factor coefficient information includes scaling factor coefficient information, displacement factor coefficient information, and desired transformation factor coefficient information.

[0087] According to an embodiment of the present disclosure, the predetermined correction factor aberration relationship model may include a predetermined scaling factor aberration relationship sub-model, a predetermined displacement factor aberration relationship sub-model, and a predetermined desired transformation factor aberration relationship sub-model. Based on the aberration information and the scaling factor coefficient information, the predetermined scaling factor aberration relationship sub-model can be constructed. Based on the aberration information and the displacement factor coefficient information, the predetermined scaling factor aberration relationship sub-model can be constructed. Based on the aberration information and the desired transformation factor coefficient information, the predetermined scaling factor aberration relationship sub-model can be constructed.

[0088] According to an embodiment of the present disclosure, the predetermined scaling factor aberration relationship sub-model can be established according to formula (5), and formula (5) is as follows.

[0089] (5);

[0090] Wherein, can be characterized as the predetermined scaling factor aberration relationship sub-model, order can be characterized as the highest order of the polynomial used for fitting, q can be characterized as the q-th order, and f a q can be characterized as the scaling factor coefficient information of the q-th order, and rms can be characterized as the aberration information.

[0091] According to an embodiment of the present disclosure, the predetermined displacement factor aberration relationship sub-model can be established according to formula (6), and formula (6) is as follows.

[0092] (6);

[0093] Wherein, b can be characterized as the predetermined displacement factor aberration relationship sub-model, order can be characterized as the highest order of the polynomial used for fitting, q can be characterized as the q-th order, and f b qThe displacement factor coefficient information can be characterized as order q, and the rms can be characterized as aberration information.

[0094] According to an embodiment of the present disclosure, a predetermined desired transformation factor aberration relationship sub-model can be established according to formula (7), and formula (7) is shown as follows.

[0095] (7);

[0096] Wherein, c can be characterized as a predetermined desired transformation factor aberration relationship sub-model, order can be characterized as the highest order of the polynomial used for fitting, q can be characterized as order q, and f c q can be characterized as the desired transformation factor coefficient information of order q, and the rms can be characterized as aberration information.

[0097] According to an embodiment of the present disclosure, the predetermined scaling factor aberration relationship sub-model, the predetermined displacement factor aberration relationship sub-model, and the predetermined desired transformation factor aberration relationship sub-model are respectively introduced into the intermediate prediction model to obtain an aberration influence prediction model. The aberration influence prediction model can be established according to formula (8), and formula (8) is shown as follows.

[0098] (8);

[0099] Wherein, can be characterized as an aberration influence prediction model.

[0100] According to an embodiment of the present disclosure, first analyze the actual predetermined exposure pattern variation distribution information. Based on the predetermined exposure pattern variation distribution information with characteristics close to the Poisson distribution, use the Poisson distribution model and the gamma function to construct an initial prediction model, so that the aberration influence prediction model built based on the initial prediction model can output distribution information close to the actual error influence distribution. Then, further expand and process the initial prediction model according to the relationship between the exposure pattern information - correction factor - aberration information. Based on the correction factor, the statistical value of the exposure pattern distribution range, and the exposure pattern variation information, respectively construct the exposure image change number relationship sub-model and the exposure image change distribution relationship sub-model in the initial prediction model that can express the relationship between the correction factor and the exposure pattern information, and substitute the exposure image change number relationship sub-model and the exposure image change distribution relationship sub-model into the initial prediction model to construct an intermediate prediction model. Then, substitute the predetermined scaling factor aberration relationship sub-model, the predetermined displacement factor aberration relationship sub-model, and the predetermined desired transformation factor aberration relationship sub-model that can represent the relationship between the aberration information and the correction factor into the intermediate prediction model to construct an aberration influence prediction model that can obtain various distribution information, realizing the construction of the aberration influence prediction model. And in the process of constructing the aberration influence prediction model, consider the relationship between the exposure pattern information - correction factor - aberration information from multiple dimensions and angles, and further improve and process the initial prediction model established based on the Poisson distribution model based on the above relationship, so that the obtained aberration influence prediction model has high robustness while also being able to fully predict relatively accurate aberration distribution information or exposure pattern distribution information, facilitating providing a reference selection basis for relevant parameter indicators for the manufacture of extreme ultraviolet lithography lenses based on the target error influence analysis results obtained from relatively accurate lithography influence distribution information, and facilitating providing relevant optimization directions for the control guidance of aberrations.

[0101] According to an embodiment of the present disclosure, the correction factor coefficient information is obtained according to the following operations.

[0102] According to an embodiment of the present disclosure, based on the predetermined exposure pattern variation distribution information, use the genetic algorithm to determine the correction factor parameter values in the intermediate prediction model.

[0103] According to an embodiment of the present disclosure, according to the multiple predetermined exposure pattern variation distribution information corresponding to multiple aberration intervals, use the genetic algorithm to traverse and find the optimal correction factor parameter values, and the correction factor parameter values may include the scaling factor parameter values, the displacement factor parameter values, and the desired transformation factor parameter values.

[0104] According to an embodiment of the present disclosure, during the process of traversing and searching for the optimal correction factor parameter value using a genetic algorithm, the intermediate scaling factor parameter value, intermediate displacement factor parameter value, and intermediate expected transformation factor parameter value obtained in the intermediate process can be substituted into the intermediate prediction model, so as to verify and compare the intermediate distribution information output by the intermediate prediction model with the substitution of the correction factor parameter value and the predetermined exposure pattern change distribution information. Thus, based on the error between the intermediate distribution information and the predetermined exposure pattern change distribution information, the genetic algorithm is used to continue the optimization process of the correction factor parameter value until the error between the obtained intermediate distribution information and the predetermined exposure pattern change distribution information meets the predetermined error threshold, that is, in the case of the smallest error, the correction factor parameter value substituted into the intermediate prediction model at this time can be determined as the optimal correction factor parameter value.

[0105] According to an embodiment of the present disclosure, polynomial fitting is performed based on the predetermined aberration information and the correction factor parameter value to obtain the correction factor coefficient information.

[0106] According to an embodiment of the present disclosure, polynomial fitting is respectively performed on the optimal scaling factor parameter value, displacement factor parameter value, and expected transformation factor parameter value determined by the genetic algorithm and the predetermined aberration information, so as to obtain the q-order scaling factor coefficient information f a q 、the q-order displacement factor coefficient information f b q and the q-order expected transformation factor coefficient information f c q .

[0107] According to an embodiment of the present disclosure, based on the predetermined exposure pattern change distribution information, the genetic algorithm is used to determine the correction factor parameter value in the intermediate prediction model, and then polynomial fitting is performed according to the determined correction factor parameter value and the predetermined aberration information to obtain the correction factor coefficient information, realizing the determination of the scaling factor coefficient information, displacement factor coefficient information, and expected transformation factor coefficient information in the predetermined scaling factor aberration relationship sub-model, predetermined displacement factor aberration relationship sub-model, and predetermined expected transformation factor aberration relationship sub-model, so as to be substituted into the intermediate prediction model to construct an aberration influence prediction model.

[0108] According to an embodiment of the present disclosure, the predetermined exposure pattern change distribution information is obtained in the following manner.

[0109] According to an embodiment of the present disclosure, an aberration information model is obtained.

[0110] According to an embodiment of the present disclosure, the aberration information model includes a plurality of geometric aberration terms, and each geometric aberration term corresponds to an aberration coefficient.

[0111] According to an embodiment of the present disclosure, for an optical system with a circular pupil, orthogonal Zernike polynomials can be used to describe the influence of aberration on the wavefront. The specific influence can be obtained according to Equation (9), which is shown as follows.

[0112] (9);

[0113] Where, can be characterized as the optical path difference, c j can be characterized as the coefficient of the j-th Zernike term that can reflect the magnitude of the aberration, can be characterized as the Zernike polynomial that can reflect the type of aberration.

[0114] According to an embodiment of the present disclosure, the geometric aberration term can be characterized as a Zernike term. Based on the expression of the influence of the aberration of an optical system with a circular pupil on the wavefront, the first 37 Zernike terms are used as the aberration information model.

[0115] According to an embodiment of the present disclosure, by performing random experiments on the aberration information model, multiple random coefficient groups are obtained.

[0116] According to an embodiment of the present disclosure, each random coefficient group includes multiple aberration coefficients that are consistent with the number of geometric aberration terms.

[0117] According to an embodiment of the present disclosure, multiple random distributions of Zernike term coefficients are generated by random experiments, that is, multiple random coefficient groups. Each random coefficient group includes 37 coefficients corresponding to 37 Zernike terms. At the same time, while performing random experiments, the distribution range of all random coefficients can be obtained.

[0118] According to an embodiment of the present disclosure, multiple random coefficient groups are input into the aberration information model for processing to obtain multiple predetermined aberration information.

[0119] For example, 1000 random coefficient groups are generated by random experiments. The 1000 random coefficient groups are input into the aberration information model to obtain 1000 pieces of predetermined aberration information. The predetermined aberration information can characterize the magnitude of the predetermined aberration.

[0120] According to an embodiment of the present disclosure, multiple predetermined exposure pattern change information is obtained based on multiple predetermined aberration information and sample exposure width information.

[0121] According to an embodiment of the present disclosure, the method for obtaining multiple predetermined exposure pattern change information based on multiple predetermined aberration information and sample exposure width information includes the following operations.

[0122] According to an embodiment of the present disclosure, the experimental exposure width information corresponding to each predetermined aberration information is obtained.

[0123] According to an embodiment of the present disclosure, using a predetermined light source and a predetermined mask pattern, an exposure experiment under the influence of predetermined aberration information is carried out. After carrying out exposure experiments with multiple different predetermined aberration information, the exposure results of the exposure experiments corresponding to each predetermined aberration information are obtained. The exposure results can be characterized as the width between the lines of the pattern presented after exposure, that is, the experimental exposure width information.

[0124] According to an embodiment of the present disclosure, based on a predetermined exposure pattern change degree function, multiple predetermined exposure pattern change information are obtained according to multiple experimental exposure width information and sample exposure width information.

[0125] According to an embodiment of the present disclosure, based on a predetermined exposure pattern change degree function, the predetermined exposure pattern change information corresponding to the experimental exposure width information is obtained according to any one of the experimental exposure width information and the sample exposure width information, and then the multiple experimental exposure width information are all calculated based on the predetermined exposure pattern change degree function, so as to obtain multiple predetermined exposure pattern change information.

[0126] According to an embodiment of the present disclosure, the predetermined exposure pattern change degree function can be established according to formula (10), and formula (10) is shown as follows.

[0127] (10)

[0128] Wherein, v can be characterized as the predetermined exposure pattern change information, can be characterized as the sample exposure width information, can be characterized as the experimental exposure width information.

[0129] According to an embodiment of the present disclosure, by first obtaining an aberration information model including multiple geometric aberration terms, performing random experiments on the coefficients of the geometric aberration terms in the aberration information model to obtain multiple random coefficient groups, and substituting the obtained multiple random coefficient groups into the aberration information model to obtain multiple predetermined aberration information, so as to simulate the multiple predetermined aberration information to obtain a larger sample range, providing a relatively large sample basis for obtaining relatively accurate predetermined exposure pattern change distribution information. Then, exposure experiments are performed according to each predetermined aberration information to obtain experimental exposure width information corresponding to each predetermined aberration information. Based on a predetermined exposure pattern change degree function, according to the sample exposure width information and each experimental exposure width information, the predetermined exposure pattern change information under each predetermined aberration information is calculated to obtain multiple predetermined exposure pattern change information, realizing a large-range sample obtained based on random experiments, that is, multiple predetermined aberration information to obtain multiple predetermined exposure pattern change information, so as to simulate more accurate predetermined exposure pattern change distribution information closer to the actual situation according to the multiple predetermined exposure pattern change information, and constructing a model based on the accurate predetermined exposure pattern change distribution information to obtain an aberration influence prediction model required for aberration analysis of the present application.

[0130] According to an embodiment of the present disclosure, obtain predetermined exposure pattern change information corresponding one-to-one to each of the multiple predetermined aberration information.

[0131] According to an embodiment of the present disclosure, based on predetermined aberration range information, perform division processing on the multiple predetermined aberration information to obtain multiple predetermined aberration intervals.

[0132] According to an embodiment of the present disclosure, the total size range of the multiple predetermined aberration information can be obtained first, and then based on the predetermined aberration range information, division processing is performed on the multiple predetermined aberration information. All the predetermined aberration information can be represented according to the root mean square of the error between the ideal wavefront and the actual wavefront. All the predetermined aberration information obtained according to the root mean square of the error between the ideal wavefront and the actual wavefront can be represented by formula (11), and formula (11) is as follows.

[0133] (11);

[0134] Wherein, can be characterized as all the predetermined aberration information obtained according to the root mean square of the error between the ideal wavefront and the actual wavefront, can be characterized as the normalization factor of the Zernike term coefficient.

[0135] According to an embodiment of the present disclosure, the normalization factor of the Zernike term coefficient can be represented by formula (12), and formula (12) is as follows.

[0136] (12);

[0137] Among them, can be characterized as a Dirac function, j can be characterized as the j-th term, and m and n can be characterized as normalization factor parameters. Among them, the relationship between m, n, and j can be obtained by querying from Table 1. In the case of m = 0, = 1. In the case of m ≠ 0, = 0.

[0138] Table 1

[0139]

[0140] According to an embodiment of the present disclosure, all predetermined aberration information obtained based on the root mean square representation of the error between the ideal wavefront and the actual wavefront is divided based on preset predetermined aberration range information to obtain a plurality of predetermined aberration intervals.

[0141] For example, the predetermined aberration range information can be set to 0.01. Based on the predetermined aberration range information, a predetermined aberration interval where the predetermined aberration information is less than 0.05, a predetermined aberration interval where the predetermined aberration information is between 0.05 and 0.06, a predetermined aberration interval where the predetermined aberration information is between 0.06 and 0.07, a predetermined aberration interval where the predetermined aberration information is between 0.07 and 0.08, and a predetermined aberration interval where the predetermined aberration information is greater than 0.08 are obtained.

[0142] According to an embodiment of the present disclosure, based on each predetermined aberration interval and the predetermined exposure pattern change information corresponding to each predetermined aberration interval, initial exposure pattern change distribution information corresponding to each predetermined aberration interval is obtained.

[0143] According to an embodiment of the present disclosure, the initial exposure pattern change distribution information corresponding to each predetermined aberration interval can be represented by formula (13), and formula (13) is as follows.

[0144] (13);

[0145] Among them, can be characterized as the initial exposure pattern change distribution information corresponding to the i-th predetermined aberration interval, l can be characterized as the l-th predetermined aberration information within the i-th predetermined aberration interval, v l can be characterized as the predetermined exposure pattern change information corresponding to the l-th predetermined aberration information, and V can be characterized as the interval of the distribution range of all predetermined exposure pattern change information. can be characterized as a constant.

[0146] According to an embodiment of the present disclosure, the initial pattern change distribution information corresponding to each predetermined aberration interval is normalized to obtain the predetermined exposure pattern change distribution information corresponding to each predetermined aberration interval.

[0147] According to an embodiment of the present disclosure, generally speaking, when the predetermined aberration interval is small enough, the result distribution in different predetermined aberration intervals can be regarded as the result distribution of the predetermined aberration information.

[0148] According to an embodiment of the present disclosure, by obtaining the predetermined exposure pattern change information corresponding one by one to each of the multiple predetermined aberration information, based on the predetermined aberration range information, the multiple predetermined aberration information is divided to obtain multiple predetermined aberration intervals. Thus, according to the predetermined exposure pattern change information corresponding to each predetermined aberration interval, the initial exposure pattern change distribution information corresponding to each predetermined aberration interval can be obtained. The initial pattern change distribution information corresponding to each predetermined aberration interval is normalized to obtain the predetermined exposure pattern change distribution information corresponding to each predetermined aberration interval, realizing the acquisition of the actual predetermined exposure pattern change distribution information based on the predetermined aberration information, facilitating the analysis according to the distribution of the obtained predetermined exposure pattern change distribution information, and thus enabling a better construction of the aberration influence prediction model.

[0149] Figure 3 A schematic diagram showing the relative distribution of the predetermined exposure pattern change information according to an embodiment of the present disclosure is shown.

[0150] As Figure 3 shown, Figure 3 A schematic diagram showing the relative distribution of the predetermined exposure pattern change information is shown. The abscissa can represent the predetermined exposure pattern change information, and the ordinate can represent the relative frequency. It can be seen from the figure that in the case of a line pattern with a critical dimension / period of 14 nm / 56 nm, the coefficients of the Zernike terms are randomly distributed within the interval of [-50m , 50m . Affected by the predetermined aberration information, the probability that the predetermined exposure pattern change information is 5% is the largest.

[0151] Figure 4 A schematic diagram showing the predetermined exposure pattern change distribution information under multiple predetermined aberration intervals according to an embodiment of the present disclosure is shown.

[0152] As Figure 4 shown, Figure 4It shows the distribution information of the change of a predetermined exposure pattern under multiple predetermined aberration intervals. The abscissa can be characterized as the change information of the predetermined exposure pattern, and the ordinate can be characterized as the relative frequency. The multiple predetermined aberration intervals include a predetermined aberration interval where the predetermined aberration information is less than 0.05, a predetermined aberration interval where the predetermined aberration information is between 0.05 and 0.06, a predetermined aberration interval where the predetermined aberration information is between 0.06 and 0.07, a predetermined aberration interval where the predetermined aberration information is between 0.07 and 0.08, and a predetermined aberration interval where the predetermined aberration information is greater than 0.08. It can be seen from the figure that the distribution of the change of the predetermined exposure pattern under each predetermined aberration interval has a trailing effect and is close to the Poisson distribution.

[0153] Figure 5 It shows a schematic diagram of the lithography impact distribution information obtained by the aberration impact prediction model of the present disclosure according to an embodiment of the present disclosure.

[0154] As Figure 5 shown, Figure 5 It shows the lithography impact distribution information obtained by the aberration impact prediction model of the present disclosure. The abscissa can be characterized as the exposure pattern change information in the lithography impact distribution information, and the ordinate can be characterized as the relative frequency. The multiple predetermined aberration intervals include a predetermined aberration interval where the predetermined aberration information is less than 0.05, a predetermined aberration interval where the predetermined aberration information is between 0.05 and 0.06, a predetermined aberration interval where the predetermined aberration information is between 0.06 and 0.07, a predetermined aberration interval where the predetermined aberration information is between 0.07 and 0.08, and a predetermined aberration interval where the predetermined aberration information is greater than 0.08. It can be seen from the figure that the lithography impact distribution obtained by the aberration impact prediction model of the present disclosure is close to the distribution of the change of the predetermined exposure pattern.

[0155] Figure 6a It shows a schematic diagram of the relationship between the scaling factor and the predetermined aberration information according to an embodiment of the present disclosure. Figure 6b It shows a schematic diagram of the relationship between the displacement factor and the predetermined aberration information according to an embodiment of the present disclosure. Figure 6c It shows a schematic diagram of the relationship between the desired transformation factor and the predetermined aberration information according to an embodiment of the present disclosure.

[0156] As Figures 6a to 6c shown, Figures 6a to 6c It shows the relationship between the scaling factor, the displacement factor, and the desired transformation factor and the predetermined aberration information after polynomial fitting. Figure 6a The abscissa of Figure 6b can be characterized as the predetermined aberration information, and the ordinate can be characterized as the scaling factor. Figure 6c The abscissa ofFigures 6a to 6c It can be seen that in the case where the highest order of the polynomial is 4 and the aberration information takes values of [0.04, 0.055, 0.065, 0.075, 0.09], the functional relationships between the scaling factor, the displacement factor, and the desired transformation factor and the predetermined aberration information.

[0157] Figure 7 The structural block diagram of the device for aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure is shown.

[0158] As Figure 7 shown, the device for aberration analysis of the extreme ultraviolet lithography system of this embodiment includes: an acquisition module 710, a processing module 720, and an analysis module 730.

[0159] The acquisition module 710 is configured to acquire the lithography parameter information of the extreme ultraviolet lithography system. The acquisition module 710 can be used to perform the operation S210 described above, which will not be elaborated here.

[0160] The processing module 720 is configured to process the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, where the aberration influence prediction model is constructed according to the correlation between the lithography parameter information and the lithography influence distribution information, and the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system. The processing module 720 can be used to perform the operation S220 described above, which will not be elaborated here.

[0161] The analysis module 730 is configured to analyze the lithography influence distribution information to obtain the target error influence analysis result of the extreme ultraviolet lithography system. The analysis module 730 can be used to perform the operation S230 described above, which will not be elaborated here.

[0162] According to an embodiment of the present disclosure, the processing module 720 includes: a first input sub-module.

[0163] The first input sub-module is configured to input the aberration information into the aberration influence prediction model for distribution prediction processing to obtain the exposure pattern distribution information, or input the exposure pattern information into the aberration influence prediction model for distribution prediction processing to obtain the aberration distribution information.

[0164] According to an embodiment of the present disclosure, the device for aberration analysis of the extreme ultraviolet lithography system further includes: a construction module.

[0165] A construction module is used to construct an aberration influence prediction model based on a Poisson distribution model, with aberration information and exposure pattern change information as variables, and an exposure pattern distribution range statistical value and a correction factor as parameters. Among them, the exposure pattern change distribution information is used to characterize the degree of change in the line width of the exposure pattern of an extreme ultraviolet lithography system deviating from a predetermined line width under the influence of aberration information.

[0166] According to an embodiment of the present disclosure, the construction module includes: a first acquisition sub-module, a first construction sub-module, a first processing sub-module, and a first obtaining sub-module.

[0167] The first acquisition sub-module is used to obtain an initial prediction model based on predetermined exposure pattern change distribution information. Among them, the initial prediction model is constructed based on the Poisson distribution model and the gamma function.

[0168] The first construction sub-module is used to construct a sub-model of the relationship between the number of exposure image changes and a sub-model of the relationship between the exposure image change distribution of the initial prediction model based on the correction factor, the exposure pattern distribution range statistical value, and the exposure pattern change information. Among them, the sub-model of the relationship between the number of exposure image changes is constructed based on the correction factor and the exposure pattern change information, and the sub-model of the relationship between the exposure image change distribution is constructed based on the correction factor and the exposure pattern distribution range statistical value.

[0169] The first processing sub-module is used to process the initial prediction model according to the sub-model of the relationship between the number of exposure image changes and the sub-model of the relationship between the exposure image change distribution to obtain an intermediate prediction model.

[0170] The first obtaining sub-module is used to process the intermediate prediction model by using a predetermined correction factor-aberration relationship model to obtain an aberration influence prediction model. Among them, the predetermined correction factor-aberration relationship model is constructed with aberration information as the independent variable and correction factor coefficient information.

[0171] According to an embodiment of the present disclosure, the first obtaining sub-module includes: a first determination unit and a first obtaining unit.

[0172] The first determination unit is used to determine the correction factor parameter value in the intermediate prediction model based on the predetermined exposure pattern change distribution information by using a genetic algorithm.

[0173] The first obtaining unit is used to perform polynomial fitting according to the predetermined aberration information and the correction factor parameter value to obtain the correction factor coefficient information.

[0174] According to an embodiment of the present disclosure, the first acquisition sub-module includes: a first acquisition unit, a first processing unit, a second obtaining unit, and a third obtaining unit.

[0175] A first acquisition unit, configured to acquire predetermined exposure pattern change information corresponding one by one to each of a plurality of predetermined aberration information items.

[0176] A first processing unit, configured to perform partitioning processing on the plurality of predetermined aberration information items based on predetermined aberration range information to obtain a plurality of predetermined aberration intervals.

[0177] A second obtaining unit, configured to obtain initial exposure pattern change distribution information corresponding to each predetermined aberration interval according to each predetermined aberration interval and the predetermined exposure pattern change information corresponding to each predetermined aberration interval.

[0178] A third obtaining unit, configured to perform normalization processing on the initial pattern change distribution information corresponding to each predetermined aberration interval to obtain predetermined exposure pattern change distribution information corresponding to each predetermined aberration interval.

[0179] According to an embodiment of the present disclosure, the first acquisition sub-module further includes: a second acquisition unit, a first random unit, a fourth obtaining unit, and a fifth obtaining unit.

[0180] The second acquisition unit is configured to acquire an aberration information model, where the aberration information model includes a plurality of geometric aberration terms, and each geometric aberration term corresponds to an aberration coefficient.

[0181] The first random unit is configured to obtain a plurality of random coefficient groups by performing random experiments on the aberration information model, where each random coefficient group includes a plurality of aberration coefficients that are the same in number as the geometric aberration terms.

[0182] The fourth obtaining unit is configured to input the plurality of random coefficient groups into the aberration information model for processing to obtain a plurality of predetermined aberration information items.

[0183] The fifth obtaining unit is configured to obtain a plurality of predetermined exposure pattern change information according to the plurality of predetermined aberration information items and sample exposure width information.

[0184] According to an embodiment of the present disclosure, the fifth obtaining unit includes: a first acquisition subunit and a first obtaining subunit.

[0185] The first acquisition subunit is configured to acquire experimental exposure width information corresponding to each predetermined aberration information item.

[0186] The first obtaining subunit is configured to obtain a plurality of predetermined exposure pattern change information based on a predetermined exposure pattern change degree function according to the plurality of experimental exposure width information and the sample exposure width information.

[0187] According to an embodiment of the present disclosure, any plurality of modules among the acquisition module 710, the processing module 720, and the analysis module 730 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 710, the processing module 720, and the analysis module 730 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in any appropriate combination of several of them. Alternatively, at least one of the acquisition module 710, the processing module 720, and the analysis module 730 may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0188] Figure 8 The block diagram of an electronic device showing a method for aberration analysis of an extreme ultraviolet lithography system according to an embodiment of the present disclosure is shown.

[0189] As Figure 8 shown, the electronic device according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read only memory (ROM) 802 or a program loaded from a storage section 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 801 may also include on-board memory for caching purposes. The processor 801 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.

[0190] In the RAM 803, various programs and data required for the operation of the electronic device are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. The processor 801 performs various operations of the method flow according to an embodiment of the present disclosure by executing the programs in the ROM 802 and / or the RAM 803. It should be noted that the program may also be stored in one or more memories other than the ROM 802 and the RAM 803. The processor 801 may also perform various operations of the method flow according to an embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0191] According to an embodiment of the present disclosure, the electronic device may further include an input / output (I / O) interface 805, and the input / output (I / O) interface 805 is also connected to the bus 804. The electronic device may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. The drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0192] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0193] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803.

[0194] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program includes program codes for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program codes are used to cause the computer system to implement the method for aberration analysis of an extreme ultraviolet lithography system provided by the embodiments of the present disclosure.

[0195] When the computer program is executed by the processor 801, the above functions defined in the system / apparatus of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0196] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program can also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from the removable medium 811. The program code included in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0197] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0198] Those skilled in the art can understand that the features described in the various embodiments of the present disclosure can be combined in various ways or / and combined, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

Claims

1. A method for aberration analysis of an extreme ultraviolet lithography system, comprising: Obtaining lithography parameter information of the extreme ultraviolet lithography system; Processing the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, wherein the aberration influence prediction model is constructed according to the correlation between the lithography parameter information and the lithography influence distribution information, the lithography influence distribution information includes any one of aberration distribution information and exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system; Analyzing the lithography influence distribution information to obtain an analysis result of the target error influence of the extreme ultraviolet lithography system.

2. The method according to claim 1, wherein The lithography parameter information includes any one of aberration information and exposure pattern information; The processing the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information includes: inputting the aberration information into the aberration influence prediction model for distribution prediction processing to obtain the exposure pattern distribution information, or inputting the exposure pattern information into the aberration influence prediction model for distribution prediction processing to obtain the aberration distribution information.

3. The method according to claim 1, wherein, The aberration influence prediction model is constructed through the following operations, including: Based on the Poisson distribution model, taking aberration information and exposure pattern change information as variables and taking the exposure pattern distribution range statistical value and the correction factor as parameters, constructing the aberration influence prediction model, wherein the exposure pattern change distribution information is used to characterize the change degree of the line width of the exposure pattern of the extreme ultraviolet lithography system deviating from the predetermined line width under the influence of the aberration information.

4. The method according to claim 3, wherein The constructing the aberration influence prediction model based on the Poisson distribution model, taking aberration information and exposure pattern change information as variables and taking the exposure pattern distribution range statistical value and the correction factor as parameters, includes: Obtaining an initial prediction model based on the predetermined exposure pattern change distribution information, wherein the initial prediction model is constructed according to the Poisson distribution model and the gamma function; Based on the correction factor, the exposure pattern distribution range statistical value and the exposure pattern change information, constructing an exposure image change number relationship sub-model and an exposure image change distribution relationship sub-model of the initial prediction model, wherein the exposure image change number relationship sub-model is constructed according to the correction factor and the exposure pattern change information, and the exposure image change distribution relationship sub-model is constructed according to the correction factor and the exposure pattern distribution range statistical value; Processing the initial prediction model according to the exposure image change number relationship sub-model and the exposure image change distribution relationship sub-model to obtain an intermediate prediction model; Processing the intermediate prediction model by using a predetermined correction factor aberration relationship model to obtain the aberration influence prediction model, wherein the predetermined correction factor aberration relationship model is constructed with aberration information as the independent variable and correction factor coefficient information.

5. The method according to claim 4, wherein The correction factor coefficient information is obtained according to the following operations, including: Based on the predetermined exposure pattern change distribution information, use a genetic algorithm to determine the correction factor parameter values in the intermediate prediction model; Perform polynomial fitting according to the predetermined aberration information and the correction factor parameter values to obtain the correction factor coefficient information.

6. The method according to claim 3, wherein The predetermined exposure pattern change distribution information is obtained in the following manner, including: Obtain predetermined exposure pattern change information corresponding one-to-one to each of the plurality of predetermined aberration information; Based on the predetermined aberration range information, perform division processing on the plurality of predetermined aberration information to obtain a plurality of predetermined aberration intervals; According to each predetermined aberration interval and the predetermined exposure pattern change information corresponding to each predetermined aberration interval, obtain the initial exposure pattern change distribution information corresponding to each predetermined aberration interval; Perform normalization processing on the initial pattern change distribution information corresponding to each predetermined aberration interval to obtain the predetermined exposure pattern change distribution information corresponding to each predetermined aberration interval.

7. The method according to claim 6, wherein, The method further includes: Obtain an aberration information model, where the aberration information model includes a plurality of geometric aberration terms, and each geometric aberration term corresponds to an aberration coefficient; Through random experiments on the aberration information model, obtain a plurality of random coefficient groups, where each random coefficient group includes a plurality of the aberration coefficients that are the same as the number of geometric aberration terms; Input the plurality of random coefficient groups into the aberration information model for processing to obtain the plurality of predetermined aberration information; According to the plurality of predetermined aberration information and the sample exposure width information, obtain the plurality of predetermined exposure pattern change information.

8. The method according to claim 7, wherein The obtaining of the plurality of predetermined exposure pattern change information according to the plurality of predetermined aberration information and the sample exposure width information includes: Obtain experimental exposure width information corresponding to each of the predetermined aberration information; Based on the predetermined exposure pattern change degree function, obtain the plurality of predetermined exposure pattern change information according to the plurality of experimental exposure width information and the sample exposure width information.

9. An apparatus for aberration analysis of an extreme ultraviolet lithography system, including: An acquisition module, configured to acquire the lithography parameter information of the extreme ultraviolet lithography system; A processing module, configured to process the lithography parameter information by using an aberration influence prediction model to obtain lithography influence distribution information, where the aberration influence prediction model is constructed according to the correlation relationship between the lithography parameter information and the lithography influence distribution information, the lithography influence distribution information includes any one of the aberration distribution information and the exposure pattern distribution information, and the lithography influence distribution information is used to characterize the influence degree of the inconsistency between the actual wavefront and the ideal wavefront on the lithography parameter information of the extreme ultraviolet lithography system; An analysis module, configured to analyze the lithography influence distribution information to obtain the target error influence analysis result of the extreme ultraviolet lithography system.

10. An electronic device, including: One or more processors; A storage device, configured to store one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 8.

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