Method and apparatus for acquiring structured light field pattern, and method and apparatus for classifying

Abstract

The application provides a structured light field pattern acquisition method and device and a classification method and device. The structured light field pattern acquisition method comprises the following steps: acquiring a first structured light field; performing projection imaging processing on the first structured light field to acquire a second structured light field; acquiring a first data matrix and a second data matrix; constructing an evaluation function according to a first difference value; iteratively optimizing the second structured light field by adjusting the size of the pixel value in the first data matrix; stopping the iterative optimization of the second structured light field when the first difference value is less than a first preset threshold value or the number of iterations is equal to a second preset threshold value, and acquiring a target structured light field; and acquiring an optimized structured light field pattern. The application improves the defect detection efficiency of the integrated circuit chip under the premise that the integrated circuit chip is not damaged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated circuit defect detection, and in particular to a structured light field pattern acquisition method and device and a classification method and device. BACKGROUND

[0002] Integrated circuits (IC) are a kind of electronic devices that are made of a large number of diodes, transistors, resistors, capacitors and other electrical devices on a very small silicon single crystal wafer, and are connected to form electronic circuits with specific functions. With the continuous development of the Internet of Things, 5G, smart devices and national defense technology, IC has become an important foundation for realizing informatization and intelligentization in various industries. With the gradual sinking of the international most advanced semiconductor process nodes, the existing IC chip detection technology and equipment will face more severe challenges, mainly because the size of the IC chip defects under the advanced process nodes has gradually approached tens of atoms. Such measurement requirements far beyond the optical diffraction limit will have extremely weak scattering signal intensity in the traditional optical detection mode, which will be easily submerged in the background scattering noise. The current super-resolution defect detection scheme needs to mark the position of the defect on the chip, which is destructive and low in efficiency.

[0003] Therefore, the present application provides a structured light field pattern acquisition method and device and a classification method and device to improve the efficiency of integrated circuit chip defect detection without damaging the integrated circuit chip. SUMMARY

[0004] The present application provides a structured light field pattern acquisition method and device and a classification method and device to solve the technical problems of the prior art, such as the need to mark the position of the defect on the chip, which is destructive and low in efficiency.

[0005] In a first aspect, the present application provides a method for obtaining a structured light field pattern, comprising: S1, obtaining a first structured light field, the first structured light field being a standard structured light field corresponding to a first target region of an integrated circuit to be measured; S2, performing a projection imaging process on the first structured light field to obtain a second structured light field; S3, obtaining a first data matrix about the pixel value distribution in the first structured light field, and obtaining a second data matrix about the pixel value distribution in the second structured light field; S4, constructing an evaluation function according to a first difference value, the first difference value being the difference between the pixel values at the corresponding positions in the first data matrix and the second data matrix in a second target region; S5, updating the first difference value by adjusting the size of the pixel values in the first data matrix, and iteratively optimizing the second structured light field; S6, when the first difference value is less than a first preset threshold or the number of iterations is equal to a second preset threshold, stopping the iterative optimization of the second structured light field, and obtaining a target structured light field, the target structured light field being the first structured light field corresponding to the current second structured light field; S7, obtaining an optimized structured light field pattern, the optimized structured light field pattern being the structured light field pattern corresponding to the target structured light field.

[0006] The beneficial effects are that the first structured light field in an ideal state or close to an ideal state is obtained by adjusting the size of the pixel values in the first data matrix and iteratively optimizing the second structured light field, so as to obtain accurate defect classification results in subsequent defect problem analysis.

[0007] Optionally, in the S3, the first structured light field is divided in the horizontal direction every A length and in the vertical direction every B length, and the second structured light field is divided in the horizontal direction every C length and in the vertical direction every D length, the A, B, C and D are positive numbers, the ratio of the C to the A and the ratio of the D to the B are equal to the scaling factor of the projection imaging process; the pixel value distribution of each region of the first structured light field after the division is obtained to constitute the first data matrix, and the pixel value distribution of each region of the second structured light field after the division is obtained to constitute the second data matrix. The beneficial effects are that the pixel value data of discrete distribution is obtained by dividing the first structured light field and the second structured light field, so as to construct the data matrix of the pixel value distribution, and the pixel values at the corresponding positions of the first structured light field and the second structured light field can be directly compared to obtain accurate comparison results by the ratio of the C to the A and the ratio of the D to the B being equal to the scaling factor of the projection imaging process.

[0008] Optionally, in step S4, the ratio of the product of A and B to the perimeter of the first structured light field pattern is calculated; the pixel value distribution of the first and second structured light fields within the second target region is obtained; and the evaluation function is constructed based on the measurement window function, the first difference, and the ratio, wherein the measurement window function is used to obtain the aggregated value of the first difference. The beneficial effect is that an effective evaluation function is obtained, facilitating the optimization of the second structured light field through the evaluation function.

[0009] Optionally, in S5, the second structured light field is iteratively optimized by adjusting the size of the pixel values ​​in the first data matrix and by using a multi-objective optimization algorithm based on conjugate gradients.

[0010] In a second aspect, the present invention provides a method for classifying defects in integrated circuits, comprising: importing the optimized structured light field pattern obtained by the method described in any embodiment of the first aspect into a reflective amplitude spatial light modulator to obtain a third structured light field, wherein the third structured light field is a structured light field generated by the reflective amplitude spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude spatial light modulator is matched with the selection of A and B; aligning the third structured light field with the pattern of the integrated circuit under test, and collecting a first far-field microscopic image under the illumination of the third structured light field; comparing the first far-field microscopic image with a second far-field microscopic image to obtain defects of the integrated circuit under test relative to an ideal integrated circuit, wherein the second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field, and the ideal integrated circuit is an integrated circuit without defect problems; and classifying the defects according to their location and type to complete the defect classification.

[0011] Its beneficial effects are as follows: The integrated circuit defect classification method provided by the present invention ensures the accuracy and efficiency of defect detection, while eliminating the need to mark the integrated circuit under test and being non-destructive.

[0012] Optionally, the pixel size of the reflective amplitude-type spatial light modulator is matched with the selection of A and B, including: A and B are both integer multiples of the pixel size of the reflective amplitude-type spatial light modulator. Its advantage is that it facilitates obtaining a processing-friendly light field structure, thereby simplifying the defect classification process.

[0013] Optionally, comparing the first far-field micrograph with the second far-field micrograph to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit includes: obtaining the location of the defect, which is the location in the first far-field micrograph where the pixel values ​​are inconsistent with those in the second far-field micrograph; and determining the type of defect by combining the location of the defect with the shape formed by the inconsistent pixel locations. Its advantages are: this invention only needs to determine the location and type of defect by judging the inconsistent pixel locations and the shape formed by the inconsistent pixel locations, without marking the chip. That is, this invention ensures the accuracy of defect identification while being non-destructive, and because the defect classification process requires almost no participation from test personnel, efficiency and automation are improved to a certain extent.

[0014] Thirdly, the present invention provides a structured light field pattern acquisition device for performing a structured light field pattern acquisition method as described in any embodiment of the first aspect, comprising: an acquisition module, a projection processing module, an evaluation module, and an optimization module; the acquisition module includes a first acquisition unit, a second acquisition unit, a third acquisition unit, a fourth acquisition unit, and a fifth acquisition unit; the first acquisition unit is used to acquire a first structured light field, wherein the first structured light field is a standard structured light field corresponding to a first target area of ​​an integrated circuit under test; the projection processing module is used to perform projection imaging processing on the first structured light field; the second acquisition unit is used to acquire a second structured light field based on the result of the projection imaging processing of the first structured light field by the projection processing module; the third acquisition unit is used to acquire a first data matrix regarding the pixel value distribution within the first structured light field, and to acquire a data matrix regarding the pixel value distribution within the first structured light field. The second data matrix represents the pixel value distribution within the second structured light field; the evaluation module is used to construct an evaluation function based on a first difference, where the first difference is the difference between the pixel values ​​at corresponding positions within the first data matrix and the second data matrix in the second target region; the optimization module is used to update the first difference by adjusting the size of the pixel values ​​within the first data matrix, and to iteratively optimize the second structured light field; and to stop the iterative optimization of the second structured light field when the first difference is less than a first preset threshold or the number of iterations is equal to a second preset threshold; the fourth acquisition unit is used to acquire the target structured light field, where the target structured light field is the first structured light field corresponding to the current second structured light field; the fifth acquisition unit is used to acquire the optimized structured light field pattern, where the optimized structured light field pattern is the structured light field pattern corresponding to the target structured light field.

[0015] Fourthly, the present invention provides an integrated circuit defect classification apparatus for performing the integrated circuit defect classification method as described in the second aspect, comprising: a structured light field acquisition unit, an alignment unit, a defect acquisition unit, and a defect classification unit; the structured light field acquisition unit is used to import the optimized structured light field pattern obtained by the method described in any embodiment of the first aspect into a reflective amplitude spatial light modulator to obtain a third structured light field, the third structured light field being the structured light field generated by the reflective amplitude spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude spatial light modulator and the selection of A and B, and the equipment for projection imaging processing. The scaling factor is matched; the alignment unit is used to align the third structured light field with the pattern of the integrated circuit under test, and collect a first far-field microscopic image under the illumination of the third structured light field; the defect acquisition unit is used to compare the first far-field microscopic image with a second far-field microscopic image to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit, wherein the second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field, and the ideal integrated circuit is an integrated circuit without defect problems; the defect classification unit is used to classify the defects according to their location and type to complete the defect classification.

[0016] For the beneficial effects of the third to fourth aspects mentioned above, please refer to the corresponding descriptions in the first or second aspects mentioned above. Attached Figure Description

[0017] Figure 1 This is a flowchart of an embodiment of a method for obtaining structured light field patterns provided by the present invention;

[0018] Figure 2 This is a flowchart of another embodiment of the method for obtaining structured light field patterns provided by the present invention;

[0019] Figure 3 This is a schematic diagram of a first target region in an integrated circuit under test provided by the present invention;

[0020] Figure 4 This is a schematic diagram of an embodiment of far-field microscopic imaging provided by the present invention;

[0021] Figure 5 This is a schematic diagram of an embodiment of a structured light field pattern and structured light field provided by the present invention;

[0022] Figure 6 This is a schematic diagram of another embodiment of the structured light field pattern and structured light field provided by the present invention;

[0023] Figure 7A flowchart illustrating an embodiment of a method for classifying defects in integrated circuits provided by the present invention;

[0024] Figure 8 A flowchart illustrating an embodiment of another integrated circuit defect classification method provided by the present invention;

[0025] Figure 9 This is a schematic diagram of an embodiment of a structured light super-resolution defect detection system provided by the present invention;

[0026] Figure 10 This is a schematic diagram of another far-field microscopic image embodiment provided by the present invention;

[0027] Figure 11 A schematic diagram of an embodiment of a structured light field pattern acquisition device provided by the present invention;

[0028] Figure 12 This is a schematic diagram of an embodiment of an integrated circuit defect classification device provided by the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of this application are described below with reference to the accompanying drawings. In the description of the embodiments of this application, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to limit the application. As used in the specification and appended claims of this application, the singular expressions "a," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, "at least one" and "one or more" refer to one or more (including two). The term "and / or" is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0030] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. The term "connection" includes direct connections and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0031] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0032] This invention proposes a method and apparatus for acquiring structured light field patterns, as well as a classification method and apparatus, to solve the technical problems existing in the prior art, which involve marking defective locations on chips, are somewhat destructive, and have low efficiency.

[0033] Before introducing this invention, let me briefly describe the accompanying drawings. Figures 3-6 ,as well as Figure 10 The black part in the diagram represents a light field intensity of 0, and the white part represents a light field intensity of 1. When the light field intensity is 1, it indicates the presence of a structure in an integrated circuit.

[0034] This invention provides a method for obtaining structured light field patterns, the process of which is shown in the figure. Figure 1 As shown, it includes:

[0035] S101: Obtain the first structured light field, wherein the first structured light field is the standard structured light field corresponding to the first target region of the integrated circuit under test;

[0036] S102: Perform projection imaging processing on the first structured light field to obtain the second structured light field;

[0037] S103: Obtain a first data matrix regarding the pixel value distribution within the first structured light field, and obtain a second data matrix regarding the pixel value distribution within the second structured light field;

[0038] S104: Construct an evaluation function based on the first difference, where the first difference is the difference between the pixel values ​​at corresponding positions in the first data matrix and the second data matrix within the second target region;

[0039] S105: Update the first difference and iteratively optimize the second structured light field by adjusting the size of the pixel values ​​in the first data matrix;

[0040] S106: When the first difference is less than the first preset threshold or the number of iterations is equal to the second preset threshold, stop the iterative optimization of the second structured light field and obtain the current target structured light field, wherein the target structured light field is the first structured light field corresponding to the current second structured light field.

[0041] S107: Obtain the optimized structured light field pattern, wherein the optimized structured light field pattern is the structured light field pattern corresponding to the current second structured light field.

[0042] In this embodiment, the first structured light field can be designed by the tester based on the pattern distribution on the integrated circuit under test. The distribution of data in the first data matrix should be consistent with the pixel value distribution within the first structured light field, and the distribution of data in the second data matrix should be consistent with the pixel value distribution within the second structured light field. The second target region refers to a randomly selected local region in the first and second data matrices, the position of which corresponds to its position in the first data matrix is ​​consistent with its position in the second data matrix. The first difference is the difference between all data in the first and second data matrices within the second target region, specifically the data occupying the same position.

[0043] This invention improves the method of iteratively optimizing the second structured light field by adjusting the pixel values ​​within the first data matrix to obtain a first structured light field in an ideal or near-ideal state, thereby facilitating accurate defect classification results in subsequent defect analysis.

[0044] In some embodiments, the first structured light field is divided into sections at intervals of length A in the horizontal direction and length B in the vertical direction, and the second structured light field is divided into sections at intervals of length C in the horizontal direction and length D in the vertical direction. A, B, C, and D are all positive numbers, and the ratio of C to A and the ratio of D to B are both equal to the scaling factor of the projection imaging process. The pixel value distribution of each region after the division of the first structured light field is obtained to form the first data matrix; the pixel value distribution of each region after the division of the second structured light field is obtained to form the second data matrix. The advantages are: by dividing the first and second structured light fields, it is convenient to obtain discretely distributed pixel value data to construct a pixel value distribution data matrix; and because the ratio of C to A and the ratio of D to B are both equal to the scaling factor of the projection imaging process, the pixel values ​​of the first and second structured light fields at corresponding positions can be directly compared to obtain accurate comparison results. For example, the value of A is the length of the first structured light field. Preferably, the value of A is the length of the first structured light field. or

[0045] In some embodiments, the ratio of the product of A and B to the perimeter of the first structured light field pattern is calculated; the pixel value distribution of the first and second structured light fields within a second target region is obtained; and the evaluation function is constructed based on a measurement window function, the first difference, and the ratio, wherein the measurement window function is used to obtain the aggregated value of the first difference. The beneficial effect is that an effective evaluation function is obtained, facilitating the optimization of the second structured light field through the evaluation function.

[0046] In some embodiments, the second structured light field is iteratively optimized by adjusting the size of the pixel values ​​in the first data matrix and by using a multi-objective optimization algorithm based on conjugate gradients.

[0047] To provide a more detailed explanation of the method for obtaining structured light field patterns provided by this invention, a specific example is given below, and the process is as follows: Figure 2 As shown, it includes:

[0048] S201: Select the first target region on the integrated circuit under test;

[0049] Specifically, the pattern of the target area selected in this preferred embodiment is as follows: Figure 3As shown in (a), this is a two-dimensional rectangular grating circuit. The length and width of the rectangle in the circuit are 500 nm and 100 nm, respectively. Two typical defects are selected, namely, Figure 3 Edge bridging as shown in (b) and as in Figure 3 The intermediate fracture shown in (c) is used as a defect object in the integrated circuit under test. In some other embodiments, other common functional circuits in integrated circuits can also be selected as the circuit structure under test, such as one-dimensional line grating circuits and logic gate circuits.

[0050] Furthermore, in this preferred embodiment, when a conventional plane wave is used to illuminate the selected integrated circuit structure, the far-field microscopic images collected by the detector are as follows: Figure 4 As shown in (a), (b), and (c), due to the diffraction limit, far-field microscopic images cannot distinguish the type and location of defects carried in integrated circuits.

[0051] S202: Obtain the first structured light field, wherein the first structured light field is the standard structured light field corresponding to the first target region of the integrated circuit under test;

[0052] Specifically, according to Figure 3 The circuit structure to be tested selected in (a) (i.e., the first target region) is designed. Figure 5 The first structured light field shown in (a) consists of an array of rectangular arrays of the same size. The length and width of each rectangle are 100 times the rectangular feature size in the circuit structure, i.e., 50 μm and 10 μm.

[0053] S203: Perform projection imaging processing on the first structured light field to obtain the second structured light field;

[0054] S204: Perform meshing processing on the first structured light field and the second structured light field;

[0055] For example, for Figure 5 The first structured light field designed in (a) is meshed, that is, divided into several equal parts at intervals of length A in the horizontal direction and length B in the vertical direction, so that the pixel value distribution of the first structured light field can be represented as a discretized data matrix, which facilitates subsequent numerical simulation calculations. Both A and B are 1 μm. Similarly, the second structured light field is divided at intervals of length C in the horizontal direction and length D in the vertical direction. The ratio of C to A and the ratio of D to B are both equal to the scaling factor of the projection imaging process.

[0056] S205: Construct an objective function F to evaluate the difference in pixel value distribution between the first structured light field and the second structured light field;

[0057] Specifically, after being scaled down by the projection system, the theoretical characteristic dimensions of the first structured light field are 500 nm in length and 100 nm in width. Calculations show that the theoretical resolution of the second structured light field obtained for the projection imaging process is approximately 0.61 * 421 nm / 0.95 ≈ 270 nm. Therefore, the obtained second structured light field will exhibit significant feature degradation compared to the first structured light field. Figure 5 As shown in (b), an evaluation function F is constructed to evaluate the difference between the target light field, i.e., the first structured light field and the second structured light field: In the formula, δx represents A, δy represents B, L represents the perimeter of the second target region corresponding to the first structured light field, w(x, y) represents the measurement window function, which is used to obtain the aggregate value of the first difference, T represents the pixel value of the first structured light field at position (x, y), and z* represents the pixel value of the second structured light field at position (x, y).

[0058] Step S206: Establish a multi-objective optimization algorithm based on conjugate gradients, update the first difference by adjusting the size of the pixel values ​​in the first data matrix, and iteratively optimize the second structured light field; when the first difference is less than a first preset threshold or the number of iterations is equal to a second preset threshold, stop the iterative optimization of the second structured light field, and obtain the structured light field pattern corresponding to the target structured light field, i.e., the optimized structured light field pattern, where the target structured light field is the first structured light field corresponding to the current second structured light field.

[0059] Specifically, a multi-objective optimization algorithm is used to adjust the grayscale values ​​filled in each grid of the structured light field. The optimization objective is to minimize the value of the evaluation function F. Iterative optimization stops when the value of the evaluation function F falls below a given value (difference) or the number of iterations reaches its upper limit. The resulting optimized structured light field pattern is shown below. Figure 6 As shown in (a) in the figure.

[0060] Based on the method for obtaining structured light field patterns described in any of the embodiments, the present invention provides a method for classifying defects in integrated circuits, the process of which is as follows: Figure 7 As shown, it includes:

[0061] S701: The optimized structured light field pattern obtained by the method described in any of the above embodiments is imported into a reflective amplitude spatial light modulator to obtain a third structured light field, wherein the third structured light field is the structured light field generated by the reflective amplitude spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude spatial light modulator is matched with the selection of A and B;

[0062] S702: Align the third structured light field with the pattern of the integrated circuit under test, and collect a first far-field microscopic image under the illumination of the third structured light field;

[0063] S703: Compare the first far-field microscopic image with the second far-field microscopic image to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit. The second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field. The ideal integrated circuit is an integrated circuit without defects.

[0064] S704: Classify the defects according to their location and type to complete the classification of the defects.

[0065] In S703, the locations of inconsistent pixel values ​​are the locations of defects on the far-field micrograph. The type of defect is determined by combining these locations with the shape formed by the inconsistent pixel values. Optionally, when determining the type of defect, the intensity differences between pixel values ​​also need to be considered.

[0066] Its beneficial effects are as follows: The integrated circuit defect classification method provided by the present invention ensures the accuracy and efficiency of defect detection, while eliminating the need to mark the integrated circuit under test and being non-destructive.

[0067] Optionally, the pixel size of the reflective amplitude-type spatial light modulator is matched with the selection of A and B, including: A and B are both integer multiples of the pixel size of the reflective amplitude-type spatial light modulator. Its advantage is that it facilitates obtaining a processable light field structure, thereby simplifying the defect classification process.

[0068] Optionally, comparing the first far-field micrograph with the second far-field micrograph to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit includes: obtaining the location of the defect, which is the location in the first far-field micrograph where the pixel values ​​are inconsistent with those in the second far-field micrograph; and determining the type of defect by combining the location of the defect with the shape formed by the inconsistent pixel locations. Its advantages are: this invention only needs to determine the location and type of defect by judging the inconsistent pixel locations and the shape formed by the inconsistent pixel locations, without marking the chip. That is, this invention ensures the accuracy of defect identification while being non-destructive, and because the defect classification process requires almost no participation from test personnel, efficiency and automation are improved to a certain extent.

[0069] To provide a more detailed explanation of the integrated circuit defect classification method provided by this invention, a specific example is given below, and the process is as follows: Figure 8 As shown, it includes:

[0070] S801: Uses a reflective amplitude-type spatial light modulator and is configured to build a structured light super-resolution defect detection system;

[0071] Specifically, the structure of the structured light super-resolution defect detection system is described in the following reference. Figure 9 The system includes, in sequence, a 421nm laser source 210, an illumination optical path structure 220, a structured light modulation mechanism 230 (i.e., a reflective amplitude-type spatial light modulator), a sample stage 240, a projection and imaging structure 250, and a signal light collection mechanism 260. The first plane mirror 221 and the second plane mirror 222 are at 30° and 135° to the optical axis of the illumination optical path, respectively. The magnification of the conjugate lens system composed of the objective lens 251 and the tube mirror 252 is ≥100X, and the numerical aperture of the objective lens 251 is ≥0.8. A reflective amplitude-type spatial light modulator is added to the incident light path. The beam is first introduced into the reflective amplitude-type spatial light modulator using two plane mirrors, and then the modulated structured light is refracted back into the illumination optical path. The angle between the beam incident on the reflective amplitude-type spatial light modulator and its normal is 10°. The beam entering the beam splitter is parallel light. The illumination and imaging optical paths share the same set of objectives and tube mirrors, and the pixel arrays of the reflective amplitude-type spatial light modulator and the detector are both located on the focal plane of the tube mirror. Alternatively, the scaling factor of the conjugate system composed of the lens and tube mirror can be set to 100X, 150X, 200X, or 500X. The light in the laser source and the light in the illumination optical path belong to the visible light band.

[0072] Step S802: The optimized structured light field pattern obtained through the above embodiment is introduced into the reflective amplitude spatial light modulator in the incident light path. The structured light field generated by the reflective amplitude spatial light modulator is imaged to the object plane of the objective lens through the tube lens and the objective lens to obtain the third structured light field.

[0073] Specifically, the pixel size of the reflective amplitude-type spatial light modulator is the same as that of A and B, both being 1 μm. The incident beam, after being shaped by the reflective amplitude-type spatial light modulator, is projected onto the structured light field of the objective lens surface as follows: Figure 6 As shown in (b) above. Alternatively, the sizes of A and B can be equal to the pixel size of the reflective amplitude spatial light modulator or an integer multiple thereof, and can be flexibly adjusted according to the pixel size of the reflective amplitude spatial light modulator.

[0074] Step S803: Spatial position matching of the third structured light field with the pattern of the integrated circuit under test on the object surface, and collection of the first far-field microscopic image under the illumination of the third structured light field;

[0075] Specifically, the sample to be tested is placed on a precision displacement stage, and the stage is adjusted so that the structured light field on the objective lens surface matches the structure of the circuit under test in spatial position. The far-field microscopic image collected by detector 260 is as follows: Figure 10 As shown, Figure 10 (a) in the image is a far-field micrograph of a defect-free ideal integrated circuit. Figure 10 (b) is a far-field micrograph of an integrated circuit carrying edge bridging defects. Figure 10 (c) is a far-field microscopic image of an integrated circuit carrying an intermediate fracture defect. Further, the integrated circuit under test is much larger than the third structured light field, and the position of the third structured light field can be adjusted so that it is aligned with the integrated circuit under test to obtain corresponding far-field microscopic images.

[0076] Step S804: Compare the second far-field microscopic image with the first far-field microscopic image collected by the detector to complete the localization and classification of integrated circuit defects. The second far-field microscopic image is a far-field microscopic image of an ideal integrated circuit obtained under the illumination of the third structured light field. The ideal integrated circuit should be consistent with the design goals of the integrated circuit under test; however, the integrated circuit under test may have defects, but the ideal integrated circuit is defect-free.

[0077] Specifically, in this preferred embodiment, the second far-field microscopic image is compared with the first far-field microscopic image, resulting in better defect characterization and a more intuitive representation of the defect type and location, including edge bridging and intermediate fractures. Furthermore, this invention can detect defects at any nanometer scale.

[0078] Furthermore, to meet the measurement needs of integrated circuits with more complex functions and layouts, differential processing is performed on far-field microscopic images of defect-free and defective integrated circuits to improve defect contrast, avoid missed defects, and reduce the difficulty of locating and classifying defects through algorithms or direct observation. This enables rapid and accurate location and classification of defects in large-area measurement areas.

[0079] Structured light microscopy illuminates the sample's test area using a structured light field corresponding to the integrated circuit structure. This unique illumination method suppresses the intensity of scattered fields generated by inherent patterns and non-defect areas, without affecting the intensity of scattered fields corresponding to defects. This improves the signal-to-noise ratio of the weak scattered signals corresponding to integrated circuit defects, making it possible to detect the weak scattered fields generated by tiny defects. A typical structured light microscopy measurement system usually selects a reflective amplitude-type spatial light modulator to generate the required structured light field. The first structured light field is reduced and projected onto the objective lens surface through a conjugate lens group formed by the tube and objective lens. After the structured light field and the corresponding circuit structure on the integrated circuit are spatially matched, the reflected scattered field is magnified and imaged onto the detector image plane through the objective and tube lenses, obtaining a super-resolution far-field microscopic image of the integrated circuit under test. By comparing the far-field microscopic image of the ideal integrated circuit with the super-resolution far-field microscopic image, defects can be located and classified.

[0080] Based on the method for acquiring structured light field patterns described in any of the embodiments, the present invention provides a device for acquiring structured light field patterns, such as... Figure 11As shown, a method for acquiring a structured light field pattern as described in any of the above embodiments includes: an acquisition module, a projection processing module 1102, an evaluation module 1103, and an optimization module 1104; the acquisition module includes a first acquisition unit 11011, a second acquisition unit 11012, a third acquisition unit 11013, a fourth acquisition unit 11014, and a fifth acquisition unit 11015; the first acquisition unit 11011 is used to acquire a first structured light field, which is a standard structured light field corresponding to a first target area of ​​the integrated circuit under test; the projection processing module 1102 is used to perform projection imaging processing on the first structured light field; the second acquisition unit 11012 is used to acquire a second structured light field based on the result of the projection imaging processing of the first structured light field by the projection processing module; the third acquisition unit 11013 is used to acquire a first data matrix regarding the pixel value distribution within the first structured light field, and... The evaluation module 1103 is used to construct an evaluation function based on a first difference, where the first difference is the difference between the pixel values ​​at corresponding positions in the first data matrix and the second data matrix within the second target region; the optimization module 1104 is used to update the first difference by adjusting the size of the pixel values ​​in the first data matrix and to iteratively optimize the second structured light field; and to stop the iterative optimization of the second structured light field when the first difference is less than a first preset threshold or the number of iterations is equal to a second preset threshold; the fourth acquisition unit 11014 is used to acquire the target structured light field, where the target structured light field is the first structured light field corresponding to the current second structured light field; the fifth acquisition unit 11015 is used to acquire the optimized structured light field pattern, where the optimized structured light field pattern is the structured light field pattern corresponding to the target structured light field.

[0081] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding unit module, and will not be repeated here.

[0082] Based on the integrated circuit defect classification method described in any of the above embodiments, the present invention provides an integrated circuit defect classification device, such as... Figure 12As shown, it includes: a structured light field acquisition unit 1201, an alignment unit 1202, a defect acquisition unit 1203, and a defect classification unit 1204; the structured light field acquisition unit 1201 is used to import the optimized structured light field pattern described in any of the above embodiments into a reflective amplitude-type spatial light modulator to obtain a third structured light field, the third structured light field being the structured light field generated by the reflective amplitude-type spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude-type spatial light modulator is matched with the selection of A and B, and the scaling factor of the device used for projection imaging processing; the alignment unit 1202 is used to... The three-structured light field is aligned with the pattern of the integrated circuit under test, and a first far-field microscopic image is collected under the illumination of the third structured light field. The defect acquisition unit 1203 is used to compare the first far-field microscopic image with a second far-field microscopic image to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit. The second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field. The ideal integrated circuit is an integrated circuit without defect problems. The defect classification unit 1204 is used to classify the defects according to their location and type to complete the defect classification.

[0083] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding unit module, and will not be repeated here.

[0084] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A method for acquiring a structured light field pattern, characterized in that, include: S1. Obtain the first structured light field, wherein the first structured light field is the standard structured light field corresponding to the first target region of the integrated circuit under test; S2. Perform projection imaging processing on the first structured light field to obtain the second structured light field; S3. Obtain a first data matrix regarding the pixel value distribution within the first structured light field, and obtain a second data matrix regarding the pixel value distribution within the second structured light field; S4. Construct an evaluation function based on the first difference, where the first difference is the difference between the pixel values ​​at corresponding positions in the first data matrix and the second data matrix within the second target region; S5. By adjusting the size of the pixel values ​​in the first data matrix, the first difference is updated, and the second structured light field is iteratively optimized. S6. When the first difference is less than the first preset threshold or the number of iterations is equal to the second preset threshold, stop the iterative optimization of the second structured light field and obtain the target structured light field, wherein the target structured light field is the first structured light field corresponding to the current second structured light field. S7. Obtain the optimized structured light field pattern, wherein the optimized structured light field pattern is the structured light field pattern corresponding to the target structured light field.

2. The method for obtaining structured light field patterns according to claim 1, characterized in that, In S3, the first structured light field is divided into sections every A length in the horizontal direction and every B length in the vertical direction, and the second structured light field is divided into sections every C length in the horizontal direction and every D length in the vertical direction. A, B, C, and D are all positive numbers, and the ratio of C to A and the ratio of D to B are both equal to the scaling factor of the projection imaging process. Obtain the pixel value distribution of each region after the first structured light field has been divided, so as to form the first data matrix; The pixel value distribution of each region after the second structured light field has been divided is obtained to form the second data matrix.

3. The method for obtaining structured light field patterns according to claim 2, characterized in that, In step S4, the ratio of the product of A and B to the perimeter of the first structured light field pattern is calculated. Obtain the pixel value distribution of the first structured light field and the second structured light field within the second target region; The evaluation function is constructed based on the measurement window function, the first difference, and the ratio, wherein the measurement window function is used to obtain the aggregate value of the first difference.

4. The method for obtaining structured light field patterns according to claim 3, characterized in that, In step S5, the second structured light field is iteratively optimized by adjusting the pixel values ​​in the first data matrix and using a multi-objective optimization algorithm based on conjugate gradients.

5. A method for classifying defects in integrated circuits, characterized in that, include: The optimized structured light field pattern obtained by the method of any one of claims 1-4 is imported into a reflective amplitude spatial light modulator to obtain a third structured light field, wherein the third structured light field is the structured light field generated by the reflective amplitude spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude spatial light modulator is matched with the selection of A and B, wherein A is the length interval for dividing the first structured light field in the horizontal direction, and B is the length interval for dividing the first structured light field in the vertical direction, and the pixel value distribution of each region of the first structured light field after division constitutes the first data matrix; The third structured light field is aligned with the pattern of the integrated circuit under test, and a first far-field microscopic image is collected under the illumination of the third structured light field. The first far-field microscopic image is compared with the second far-field microscopic image to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit. The second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field. The ideal integrated circuit is an integrated circuit without defects. The defects are categorized according to their location and type to complete the defect classification.

6. The method for classifying integrated circuit defects according to claim 5, characterized in that, The pixel size of the reflective amplitude spatial light modulator is matched with the selection of A and B, including: A and B are both integer multiples of the pixel size of the reflective amplitude spatial light modulator.

7. The method for classifying integrated circuit defects according to claim 5, characterized in that, The step of comparing the first far-field micrograph with the second far-field micrograph to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit includes: The location of the defect is obtained, which is the location in the first far-field micrograph where the pixel value is inconsistent with that in the second far-field micrograph; The type of defect is determined by combining the location of the defect with the shape formed by the locations of the inconsistent pixel values.

8. A device for acquiring structured light field patterns, characterized in that, The method for acquiring a structured light field pattern as described in any one of claims 1-4 includes: an acquisition module, a projection processing module, an evaluation module, and an optimization module; The acquisition module includes a first acquisition unit, a second acquisition unit, a third acquisition unit, a fourth acquisition unit, and a fifth acquisition unit; The first acquisition unit is used to acquire a first structured light field, which is a standard structured light field corresponding to the first target region of the integrated circuit under test. The projection processing module is used to perform projection imaging processing on the first structured light field, and the second acquisition unit is used to acquire the second structured light field based on the result of the projection imaging processing of the first structured light field by the projection processing module. The third acquisition unit is used to acquire a first data matrix regarding the pixel value distribution in the first structured light field, and to acquire a second data matrix regarding the pixel value distribution in the second structured light field; The evaluation module is used to construct an evaluation function based on a first difference, where the first difference is the difference between the pixel values ​​at corresponding positions in the first data matrix and the second data matrix within the second target area. The optimization module is used to update the first difference by adjusting the size of the pixel values ​​in the first data matrix and to iteratively optimize the second structured light field; and to stop the iterative optimization of the second structured light field when the first difference is less than a first preset threshold or the number of iterations is equal to a second preset threshold. The fourth acquisition unit is used to acquire the target structured light field, which is the first structured light field corresponding to the current second structured light field. The fifth acquisition unit is used to acquire an optimized structured light field pattern, wherein the optimized structured light field pattern is the structured light field pattern corresponding to the target structured light field.

9. A classification device for integrated circuit defects, characterized in that, The method for classifying integrated circuit defects as described in any one of claims 5-7 includes: a structured light field acquisition unit, an alignment unit, a defect acquisition unit, and a defect classification unit; The structured light field acquisition unit is used to import the optimized structured light field pattern obtained by the method of any one of claims 1-4 into a reflective amplitude spatial light modulator to obtain a third structured light field, wherein the third structured light field is the structured light field generated by the reflective amplitude spatial light modulator according to the optimized structured light field pattern; the pixel size of the reflective amplitude spatial light modulator is matched with the selection of A and B and the scaling factor of the device used for projection imaging processing, wherein A is the length interval for dividing the first structured light field in the horizontal direction, B is the length interval for dividing the first structured light field in the vertical direction, and the pixel value distribution of each region of the first structured light field after division constitutes the first data matrix; The alignment unit is used to align the third structured light field with the pattern of the integrated circuit under test, and to collect a first far-field microscopic image under the illumination of the third structured light field. The defect acquisition unit is used to compare the first far-field microscopic image with the second far-field microscopic image to obtain the defects of the integrated circuit under test relative to the ideal integrated circuit. The second far-field microscopic image is a far-field microscopic image of the ideal integrated circuit obtained under the illumination of the third structured light field. The ideal integrated circuit is an integrated circuit without defect problems. The defect classification unit is used to classify the defects according to their location and type to complete the defect classification.

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