Method, apparatus and storage medium for structure measurement of integrated circuit device
By dividing the spectral signals of integrated circuit devices into wavelength bands and adjusting the sampling frequency, the problem of inaccurate measurement results in traditional optical critical dimension measurement is solved, achieving more efficient and accurate structural measurement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SKYVERSE TECH CO LTD
- Filing Date
- 2025-07-07
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies for measuring integrated circuit device structures suffer from inaccurate measurement results and poor repeatability due to unreasonable sampling methods. This is especially true in logic and DRAM devices as well as 3D NAND structures, where traditional uniform wavelength sampling frequencies cannot effectively extract the characteristics of the measurement spectral signal, resulting in low measurement efficiency.
By acquiring the initial measurement spectral signal, the wavelength range is divided into multiple wavelength bands based on a preset method. The spectral change frequency of each wavelength band is analyzed, the sampling frequency is determined and adjusted, resampling and fitting analysis are performed, and a three-dimensional structural physical model is constructed for final measurement.
It improves the accuracy and efficiency of measurement results, can adaptively adjust the wavelength sampling frequency, enhances the ability to extract spectral features, and avoids increasing the amount of computation and time.
Smart Images
Figure CN120467239B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of optical measurement technology, and in particular to a method, apparatus, device and storage medium for structural measurement of integrated circuit devices. Background Technology
[0002] To ensure consistency and yield in the manufacturing process of large-scale integrated circuits, it is necessary to rapidly and accurately measure the three-dimensional structural morphology parameters of devices in key process steps. Currently, the industry widely adopts Optical Critical Dimension (OCD) technology. Its basic principle is to collect the spectral information of zero-order diffraction light reflected from the sample under test, and then combine the measured spectral information with a theoretical model to fit and analyze it, ultimately obtaining the three-dimensional morphology parameters of the sample.
[0003] To collect the spectral signal of the zero-order diffraction light of a sample, a spectrometer is typically used to obtain the light intensity at different wavelengths. Spectrometers sample wavelengths uniformly, resulting in uniformly sampled spectral measurement signals. When fitting and analyzing the spectral measurement signals using theoretical calculation models, a fixed sampling frequency is usually set to uniformly sample across the measurement wavelength range, or the original wavelength sampling points of the spectrometer are used to perform fitting analysis at these wavelength points to obtain the measurement results. However, for certain semiconductor device structures, such as some logic and DRAM devices, the characteristics of their zero-order diffraction light signals are inconsistent across different wavelengths, exhibiting drastic changes in some wavelength ranges and gradual changes in others. Another example is the 3D NAND structure, whose zero-order diffraction light signal exhibits significant oscillation characteristics across the entire wavelength range. For these special devices, traditional fitting analysis under uniform wavelength sampling suffers from two problems: firstly, a low sampling frequency leads to poor sensitivity of the theoretical model, failing to effectively extract the characteristics of the measured spectral signal, resulting in inaccurate measurement results and reduced repeatability; secondly, a high sampling frequency increases the computational load and time of the theoretical model, lengthening the fitting analysis time and reducing overall measurement efficiency. Summary of the Invention
[0004] In view of this, this application provides a method, apparatus, device and storage medium for measuring the structure of integrated circuit devices, so as to solve the problem of inaccurate measurement results of circuit device structure caused by unreasonable sampling methods.
[0005] To address the aforementioned technical problems, this application provides a method for structural measurement of integrated circuit devices, comprising: acquiring an initial measurement spectral signal of the sample under test within a preset wavelength range; dividing the preset wavelength range into multiple wavelength bands based on a preset method; analyzing the spectral change frequency of each wavelength band to determine the sampling frequency corresponding to each wavelength band; resampling the initial measurement spectral signal of each wavelength band according to the sampling frequency to obtain a final measurement spectral signal; and performing fitting analysis on the final measurement spectral signal to obtain the structural parameters of the sample under test.
[0006] As a further improvement of this application, the preset wavelength range is divided into multiple wavelength segments based on a preset method, including: performing discrete wavelet transform analysis on the initial measured spectral signal to obtain the frequency change distribution of the spectral change frequency in the preset wavelength range; and dividing the preset wavelength range into multiple wavelength segments based on the frequency change distribution.
[0007] As a further improvement to this application, the process of discrete wavelet transform analysis is expressed as follows:
[0008] ;
[0009] in, Indicates the frequency variation distribution. Indicates the first The initial measured spectral signal corresponding to each wavelength point Indicates the wavelet function at the displacement position The value at that location, This indicates the total number of wavelength points in the initial measured spectral signal.
[0010] As a further improvement of this application, the preset wavelength range is divided into multiple wavelength segments based on the frequency variation distribution, including: performing gradient calculation on the frequency variation distribution to obtain the gradient value of each wavelength point; calculating the mean and standard deviation of all gradient values based on the gradient values, and setting a gradient threshold based on the mean and standard deviation; and dividing the preset wavelength range into multiple wavelength segments based on the gradient threshold.
[0011] As a further improvement of this application, the spectral variation frequency of each wavelength band is analyzed to determine the sampling frequency corresponding to each wavelength band, including: confirming the maximum value of the spectral variation frequency in each wavelength band; multiplying each maximum value by a preset scaling factor to obtain the sampling frequency corresponding to each wavelength band.
[0012] As a further improvement of this application, the initial measurement spectral signal of each wavelength band is resampled according to the sampling frequency to obtain the final measurement spectral signal, including: confirming the sampling point of each wavelength band according to the sampling frequency; and sampling and interpolating the initial measurement spectral signal according to the sampling point to obtain the final measurement spectral signal.
[0013] As a further improvement of this application, the final measured spectral signal is fitted and analyzed to obtain the structural parameters of the sample under test, including: constructing a three-dimensional structural physical model of the sample under test, the three-dimensional structural physical model including multiple parameter combinations; constructing a theoretical spectral calculation model of the sample under test using electromagnetic simulation technology, and setting the wavelength of the theoretical spectral calculation model to a preset wavelength range; solving the three-dimensional structural physical model using the theoretical spectral calculation model to obtain the theoretical spectral signals corresponding to all parameter combinations within the preset wavelength range; comparing the final measured spectral signal with each theoretical spectral signal to confirm the closest target theoretical spectral signal; and using the parameter combination corresponding to the target theoretical spectral signal as the structural parameters of the sample under test and outputting it.
[0014] To address the aforementioned technical problems, another technical solution adopted in this application is: providing a structural measurement device for integrated circuit devices, comprising: an acquisition module for acquiring an initial measurement spectral signal of the sample under test within a preset wavelength range; a wavelength division module for dividing the preset wavelength range into multiple wavelength bands based on a preset method; a frequency analysis module for analyzing the spectral change frequency of each wavelength band and determining the sampling frequency corresponding to each wavelength band; a resampling module for resampling the initial measurement spectral signal of each wavelength band according to the sampling frequency to obtain a final measurement spectral signal; and a fitting module for performing fitting analysis on the final measurement spectral signal to obtain the structural parameters of the sample under test.
[0015] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide a computer device, the computer device including a processor and a memory coupled to the processor, the memory storing program instructions, and when the program instructions are executed by the processor, causing the processor to perform the steps of the integrated circuit device structure measurement method as described above.
[0016] To solve the above-mentioned technical problems, another technical solution adopted in this application is to provide a storage medium storing program instructions capable of implementing the structural measurement method of the integrated circuit device described above.
[0017] The beneficial effects of this application are as follows: The structural measurement method for integrated circuit devices of this application divides the initial measurement spectral signal into multiple wavelength bands, analyzes the frequency variation distribution of each wavelength band, sets the sampling frequency for each wavelength band, and then resamples each wavelength band according to the sampling frequency to obtain a new measurement spectral signal. Finally, the new measurement spectral signal is used for fitting analysis to obtain the structural parameters of the sample under test. It can analyze the distribution trend of the frequency variation of the spectral signal with wavelength, and determine the appropriate wavelength sampling frequency according to the frequency variation of the spectral signal. It can realize high-frequency sampling for the range of high spectral oscillation frequency and low-frequency sampling for the range of low spectral oscillation frequency. The wavelength sampling frequency is adaptively adjusted according to the specific characteristics of the measurement spectral signal, so that the fitting analysis can better extract spectral features and improve the accuracy of the measurement results without increasing the total number of samples and the amount of calculation. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating a method for measuring the structure of an integrated circuit device according to an embodiment of the present invention.
[0019] Figure 2 This is a schematic diagram of a periodic structure in an integrated circuit device;
[0020] Figure 3 This is a functional module diagram of the integrated circuit device structure measurement device according to an embodiment of the present invention;
[0021] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;
[0022] Figure 5 This is a schematic diagram of the structure of the storage medium according to an embodiment of the present invention. Detailed Implementation
[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0024] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.
[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0026] Figure 1 This is a flowchart illustrating the structural measurement method for integrated circuit devices according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, the method of the present invention is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, the structural measurement method for this integrated circuit device includes the following steps:
[0027] Step S1: Obtain the initial measurement spectral signal of the sample under test within the preset wavelength range.
[0028] Specifically, an OCD measuring device is used to measure the sample to obtain the initial measurement spectral signal of the sample. ,in This represents the measured values at different wavelengths, which can be in various forms such as reflectance, ellipsometric parameters, and Mueller matrices. It should be noted that the initial measured spectral signal is obtained by uniform sampling at a constant sampling frequency, and the preset wavelength range can be set in advance as needed.
[0029] Step S2: Divide the preset wavelength range into multiple wavelength segments based on the preset method.
[0030] Specifically, after acquiring the initial measurement spectral signal, in order to better analyze the frequency variation distribution of different wavelength bands, this embodiment divides the preset wavelength range into multiple wavelength bands. This division can be achieved in various ways. For example: equal-interval division: dividing the entire wavelength range into K equal segments, each with the same length; division based on frequency thresholds: dividing the wavelength bands using a static frequency threshold obtained through prior experiments, or determining the segment boundaries by setting a dynamic threshold based on the spectral variation frequency values obtained from wavelet transform (such as the gradient, absolute value, or local extrema of the frequency variation distribution); clustering algorithm division: using clustering algorithms (such as K-means, DBSCAN) to group wavelength points with similar frequencies into the same segment.
[0031] Furthermore, in order to better divide the wavelength bands so that each wavelength band can reflect the spectral characteristics of that wavelength band, S2 specifically includes:
[0032] 1. Perform discrete wavelet transform analysis on the initial measured spectral signal to obtain the frequency variation distribution of the spectral change frequency within a preset wavelength range.
[0033] Specifically, the process of discrete wavelet transform analysis is represented as follows:
[0034] ;
[0035] in, Indicates the frequency variation distribution. Indicates the first The initial measured spectral signal corresponding to each wavelength point Indicates the wavelet function at the displacement position The value at that location, This indicates the total number of wavelength points in the initial measured spectral signal.
[0036] It should be noted that, Wavelet functions come in various forms, and the appropriate function can be selected based on the specific signal type. For example, the commonly used Haar wavelet is defined as follows.
[0037] .
[0038] 2. Divide the preset wavelength range into multiple wavelength segments based on the frequency variation distribution.
[0039] Specifically, after obtaining the distribution of spectral frequency changes within a preset wavelength range, the preset wavelength range is divided into multiple wavelength segments based on this frequency change distribution, so that wavelength points with similar frequency change characteristics are distributed in the same wavelength segment, making it convenient to set the corresponding sampling frequency for each wavelength segment.
[0040] Furthermore, the step of dividing the preset wavelength range into multiple wavelength bands based on the frequency variation distribution specifically includes:
[0041] 2.1 Calculate the gradient of the frequency variation distribution to obtain the gradient value at each wavelength point.
[0042] Specifically, in this implementation, the central difference method is used to calculate the gradient value at each wavelength point, and the calculation formula is expressed as follows:
[0043] ;
[0044] in, Represents the gradient value. Indicates the first The frequency variation distribution at each wavelength point. It should be noted that the data is smoothed before calculating the gradient value, which can be achieved by applying Gaussian filtering or moving average to the frequency variation distribution.
[0045] 2.2 Calculate the mean and standard deviation of all gradient values based on the gradient values, and set the gradient threshold based on the mean and standard deviation.
[0046] Specifically, this embodiment uses a dynamically set threshold to set the gradient threshold. The formulas for calculating the mean and standard deviation are as follows:
[0047] ;
[0048] ;
[0049] in, This represents the mean. Indicates standard deviation, This indicates the number of wavelength points.
[0050] The formula for calculating the gradient threshold is expressed as follows:
[0051] ;
[0052] in, Indicates the gradient threshold. This indicates the preset adjustment coefficient. The larger the value, the more conservative the segmentation. In this embodiment, different values can be compared on the test dataset. Choose the optimal combination of fitting accuracy and computation time for the given value. Value. For spectra with high noise (such as 3D NAND structures), the value can be increased. This value is used to reduce incorrect segmentation.
[0053] 2.3 Divide the preset wavelength range into multiple wavelength segments based on the gradient threshold.
[0054] Specifically, by traversing all wavelength points, it is confirmed whether the wavelength point exceeds the gradient threshold. If it does, the wavelength point is marked as a boundary point. After finding all boundary points, the preset wavelength range is divided into multiple wavelength segments based on the boundary points.
[0055] Furthermore, to avoid dividing the wavelength segments too short, this embodiment can also set a minimum band length and merge boundary points with adjacent spacing less than the minimum band length. For example, assuming the minimum band length is 10 nm, if the initial segment boundary points are [450 nm, 455 nm, 460 nm, 600 nm, 605 nm], then they are merged into [450 nm, 460 nm, 600 nm].
[0056] Furthermore, this embodiment can also extend the wavelength by a preset distance (e.g., ±5 nm) before and after the region with high spectral oscillation frequency to ensure that the region with high spectral oscillation frequency is fully covered.
[0057] Step S3: Analyze the spectral variation frequency of each wavelength band and determine the sampling frequency corresponding to each wavelength band.
[0058] Specifically, after dividing the wavelength into multiple wavelength bands, the sampling frequency corresponding to each wavelength band is calculated by combining the spectral change frequency of each wavelength band.
[0059] Step S3 specifically includes:
[0060] 1. Determine the maximum value of the spectral variation frequency in each wavelength band.
[0061] 2. Multiply each maximum value by a preset scaling factor to obtain the sampling frequency corresponding to each wavelength band.
[0062] The preset scaling factor is a pre-set constant. The value of the preset scaling factor depends on the characteristics of the spectral signal and the final spectral fitting effect. The larger the value of the preset scaling factor, the denser the sampling. It can be set in advance through multiple experiments based on the final fitting results obtained from the experiments.
[0063] Specifically, after dividing the wavelength into multiple wavelength bands, the frequency distribution of spectral variations within each wavelength band is calculated. maximum value K represents the number of wavelength bands. The maximum spectral variation frequency within each wavelength band is then multiplied by a preset scaling factor to obtain the sampling frequency within that wavelength band, denoted as: .
[0064] Step S4: Resample the initial measurement spectral signal for each wavelength band according to the sampling frequency to obtain the final measurement spectral signal.
[0065] Specifically, after obtaining the sampling frequency Then, based on the sampling frequency corresponding to each wavelength band, the initial measurement spectral signal corresponding to that wavelength band is sampled to obtain the final measurement spectral signal.
[0066] Furthermore, step S4 specifically includes:
[0067] 1. Determine the sampling points for each wavelength band based on the sampling frequency.
[0068] Specifically, after obtaining the sampling frequency corresponding to each wavelength band, the number of sampling points for that wavelength band is determined based on the sampling frequency, and then the location of the sampling points is determined based on the number of sampling points and the length of the wavelength band.
[0069] 2. The initial measured spectral signal is sampled and interpolated based on the sampling points to obtain the final measured spectral signal.
[0070] Specifically, after confirming the location of each sampling point, the spectral signal of each sampling point is acquired from the initial measurement spectral signal to obtain the final measurement spectral signal.
[0071] Step S5: Perform fitting analysis on the final measured spectral signal to obtain the structural parameters of the sample to be tested.
[0072] Specifically, after obtaining the final measured spectral signal, the optical critical dimension (OCD) technique is used to fit and analyze the final measured spectral signal to obtain the structural parameters of the three-dimensional structure of the sample under test.
[0073] Furthermore, step S5 specifically includes:
[0074] 1. Construct a three-dimensional physical model of the sample to be tested. The three-dimensional physical model includes multiple parameter combinations.
[0075] Specifically, a three-dimensional structural physical model corresponding to the sample to be tested is established, such as... Figure 2 As shown, Figure 2 (a) represents a common device in integrated circuits. Figure 2 (b) is its cross-sectional view. The dashed box represents a single periodic unit. For each periodic unit, it includes structural parameters such as top width TCD, bottom width BCD, height Ht, and period pitch. These structural parameters are used to characterize the three-dimensional physical model of the device.
[0076] 2. Construct a theoretical spectral calculation model of the sample to be tested using electromagnetic simulation technology, and set the wavelength of the theoretical spectral calculation model to a preset wavelength range.
[0077] It should be noted that this electromagnetic simulation technology includes Rigorous Coupled Wave Analysis (RCWA) and Finite-Difference Time Domain (FDTD). The theoretical spectral calculation model constructed using electromagnetic simulation technology is used to calculate the theoretical spectral signal for each structural parameter.
[0078] 3. Solve the three-dimensional structural physical model using the theoretical spectral calculation model to obtain the theoretical spectral signals corresponding to all parameter combinations within the preset wavelength range.
[0079] Specifically, the wavelength of the theoretical spectral calculation model is set to a preset wavelength range, and then the theoretical spectral calculation model is used to perform spectral calculations on each parameter combination in the three-dimensional structural physical model to obtain the corresponding theoretical spectrum, thereby constructing a theoretical spectral library corresponding to the sample to be tested.
[0080] 4. Compare the final measured spectral signal with each theoretical spectral signal to identify the closest target theoretical spectral signal.
[0081] 5. The parameter combination corresponding to the target theoretical spectral signal is used as the structural parameters of the sample to be tested and output.
[0082] Specifically, the system iterates through every theoretical spectral signal in the theoretical spectral library, finds the target theoretical spectral signal that is closest to the final measured spectrum, then queries the structural parameters corresponding to the target theoretical spectral signal, along with the three-dimensional structural data of the sample to be tested, and outputs the structural parameters as the final measurement result.
[0083] The integrated circuit device structure measurement method of this embodiment divides the initial measurement spectral signal into multiple wavelength bands, analyzes the frequency variation distribution of each wavelength band, sets the sampling frequency for each wavelength band, and then resamples each wavelength band according to the sampling frequency to obtain a new measurement spectral signal. Finally, the new measurement spectral signal is used for fitting analysis to obtain the structural parameters of the sample under test. It can analyze the distribution trend of the frequency variation of the spectral signal with wavelength, and determine the appropriate wavelength sampling frequency according to the frequency variation of the spectral signal. It can realize high-frequency sampling for the range of high spectral oscillation frequency and low-frequency sampling for the range of low spectral oscillation frequency. The wavelength sampling frequency is adaptively adjusted according to the specific characteristics of the measurement spectral signal, so that the fitting analysis can better extract spectral features and improve the accuracy of the measurement results without increasing the total number of samples and the amount of calculation.
[0084] Figure 3 This is a functional module diagram of the structural measurement device for integrated circuit devices according to an embodiment of the present invention. Figure 3As shown, the structural measurement device 20 of the integrated circuit device includes: an acquisition module 21, a wavelength division module 22, a frequency analysis module 23, a resampling module 24, and a fitting module 25.
[0085] The acquisition module 21 is used to acquire the initial measurement spectral signal of the sample under test within a preset wavelength range;
[0086] Wavelength division module 22 is used to divide a preset wavelength range into multiple wavelength segments based on a preset method;
[0087] The frequency analysis module 23 is used to analyze the spectral change frequency of each wavelength band and determine the sampling frequency corresponding to each wavelength band.
[0088] The resampling module 24 is used to resample the initial measurement spectral signal of each wavelength band according to the sampling frequency to obtain the final measurement spectral signal;
[0089] The fitting module 25 is used to perform fitting analysis on the final measured spectral signal to obtain the structural parameters of the sample to be tested.
[0090] Optionally, the wavelength division module 22 performs the operation of dividing a preset wavelength range into multiple wavelength segments based on a preset method, specifically including: performing discrete wavelet transform analysis on the initial measured spectral signal to obtain the frequency change distribution of the spectral change frequency in the preset wavelength range; and dividing the preset wavelength range into multiple wavelength segments based on the frequency change distribution.
[0091] Alternatively, the discrete wavelet transform analysis process can be represented as follows:
[0092] ;
[0093] in, Indicates the frequency variation distribution. Indicates the first The initial measured spectral signal corresponding to each wavelength point Indicates the wavelet function at the displacement position The value at that location, This indicates the total number of wavelength points in the initial measured spectral signal.
[0094] Optionally, the wavelength division module 22 performs the operation of dividing the preset wavelength range into multiple wavelength segments based on the frequency change distribution, specifically including: performing gradient calculation on the frequency change distribution to obtain the gradient value of each wavelength point; calculating the mean and standard deviation of all gradient values based on the gradient values, and setting a gradient threshold based on the mean and standard deviation; and dividing the preset wavelength range into multiple wavelength segments based on the gradient threshold.
[0095] Optionally, the frequency analysis module 23 performs the operation of analyzing the spectral change frequency of each wavelength band and determining the sampling frequency corresponding to each wavelength band, specifically including: confirming the maximum value of the spectral change frequency in each wavelength band; multiplying each maximum value by a preset scaling factor to obtain the sampling frequency corresponding to each wavelength band.
[0096] Optionally, the resampling module 24 performs the operation of resampling the initial measurement spectral signal of each wavelength band according to the sampling frequency to obtain the final measurement spectral signal. Specifically, this includes: confirming the sampling point of each wavelength band according to the sampling frequency; and sampling and interpolating the initial measurement spectral signal according to the sampling point to obtain the final measurement spectral signal.
[0097] Optionally, the fitting module 25 performs fitting analysis on the final measured spectral signal to obtain the structural parameters of the sample under test. Specifically, this includes: constructing a three-dimensional structural physical model of the sample under test, which includes multiple parameter combinations; constructing a theoretical spectral calculation model of the sample under test using electromagnetic simulation technology, and setting the wavelength of the theoretical spectral calculation model to a preset wavelength range; solving the three-dimensional structural physical model using the theoretical spectral calculation model to obtain the theoretical spectral signals corresponding to all parameter combinations within the preset wavelength range; comparing the final measured spectral signal with each theoretical spectral signal to identify the closest target theoretical spectral signal; and outputting the parameter combination corresponding to the target theoretical spectral signal as the structural parameters of the sample under test.
[0098] For further details regarding the implementation of the technical solutions for each module in the structure measurement device of the integrated circuit device in the above embodiments, please refer to the description in the structure measurement method of the integrated circuit device in the above embodiments, which will not be repeated here.
[0099] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0100] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Figure 4 As shown, the computer device 30 includes a processor 31 and a memory 32 coupled to the processor 31. The memory 32 stores program instructions. When the program instructions are executed by the processor 31, the processor 31 performs the steps of the integrated circuit device structure measurement method described in any of the above embodiments.
[0101] The processor 31 can also be referred to as a Central Processing Unit (CPU). The processor 31 may be an integrated circuit chip with signal processing capabilities. The processor 31 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.
[0102] See Figure 5 , Figure 5 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present invention. The storage medium of this embodiment stores program instructions 41 capable of implementing the structural measurement method for the aforementioned integrated circuit device. These program instructions 41 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or computer devices such as computers, servers, mobile phones, and tablets.
[0103] In the several embodiments provided in this application, it should be understood that the disclosed computer devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.
[0104] Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for structural measurement of integrated circuit devices, characterized in that, It includes: Acquire the initial measurement spectral signal of the sample under test within a preset wavelength range; Discrete wavelet transform analysis is performed on the initial measured spectral signal to obtain the frequency variation distribution of the spectral change frequency within the preset wavelength range; gradient calculation is performed on the frequency variation distribution to obtain the gradient value at each wavelength point; Calculate the mean and standard deviation of all gradient values based on the gradient values, and set the gradient threshold based on the mean and standard deviation; The preset wavelength range is divided into multiple wavelength segments based on the gradient threshold. Analyze the spectral variation frequency of each wavelength band to determine the corresponding sampling frequency for each wavelength band; The initial measurement spectral signal for each wavelength band is resampled according to the sampling frequency to obtain the final measurement spectral signal; The structural parameters of the sample under test are obtained by fitting and analyzing the final measured spectral signal.
2. The structural measurement method for integrated circuit devices according to claim 1, characterized in that, The process of discrete wavelet transform analysis is represented as follows: ； in, Indicates the frequency variation distribution. Indicates the first The initial measured spectral signal corresponding to each wavelength point Indicates the wavelet function at the displacement position The value at that location, This indicates the total number of wavelength points in the initial measured spectral signal.
3. The structural measurement method for integrated circuit devices according to claim 1, characterized in that, The analysis of the spectral variation frequency of each wavelength band and the determination of the sampling frequency corresponding to each wavelength band include: Identify the maximum frequency of spectral variation in each wavelength band; Each maximum value is multiplied by a preset scaling factor to obtain the sampling frequency corresponding to each wavelength band.
4. The structural measurement method for integrated circuit devices according to claim 1, characterized in that, The step of resampling the initial measurement spectral signal for each wavelength band according to the sampling frequency to obtain the final measurement spectral signal includes: The sampling points for each wavelength band are determined based on the sampling frequency. The initial measured spectral signal is sampled and interpolated based on the sampling points to obtain the final measured spectral signal.
5. The structural measurement method for integrated circuit devices according to claim 1, characterized in that, The process of fitting and analyzing the final measured spectral signal to obtain the structural parameters of the sample to be tested includes: Construct a three-dimensional structural physical model of the sample to be tested, the three-dimensional structural physical model including multiple parameter combinations; A theoretical spectral calculation model of the sample to be tested is constructed using electromagnetic simulation technology, and the wavelength of the theoretical spectral calculation model is set to the preset wavelength range; The theoretical spectral calculation model is used to solve the three-dimensional structural physical model to obtain the theoretical spectral signals corresponding to all parameter combinations within the preset wavelength range; The final measured spectral signal is compared with each theoretical spectral signal to identify the closest target theoretical spectral signal; The parameter combination corresponding to the target theoretical spectral signal is used as the structural parameters of the sample to be tested and output.
6. A structural measurement device for integrated circuit devices, characterized in that, It includes: The acquisition module is used to acquire the initial measurement spectral signal of the sample under test within a preset wavelength range; The wavelength division module is used to perform discrete wavelet transform analysis on the initial measured spectral signal to obtain the frequency change distribution of the spectral change frequency within the preset wavelength range; to perform gradient calculation on the frequency change distribution to obtain the gradient value at each wavelength point; to calculate the mean and standard deviation of all gradient values based on the gradient values, and to set a gradient threshold based on the mean and standard deviation. The preset wavelength range is divided into multiple wavelength segments based on the gradient threshold. The frequency analysis module is used to analyze the spectral variation frequency of each wavelength band and determine the sampling frequency corresponding to each wavelength band. The resampling module is used to resample the initial measurement spectral signal of each wavelength band according to the sampling frequency to obtain the final measurement spectral signal; The fitting module is used to perform fitting analysis on the final measured spectral signal to obtain the structural parameters of the sample to be tested.
7. A computer device, characterized in that, The computer device includes a processor and a memory coupled to the processor, the memory storing program instructions that, when executed by the processor, cause the processor to perform the steps of the structural measurement method for an integrated circuit device as described in any one of claims 1-5.
8. A storage medium, characterized in that, The device stores program instructions capable of implementing the structural measurement method for the integrated circuit device as described in any one of claims 1-5.