A test system for residual monomers in a photoresist resin

Abstract

The present application relates to the field of chemical or physical testing technology, and more particularly to a test system for residual monomers in photoresist resin, corresponding steps comprising: determining a target spectrum curve of a target sample, determining spectral characterization characteristic values of a target monomer by using absorption intensity of the target monomer in the target spectrum curve; determining key samples highly associated with the target monomer in a sample set by using similarity of the target monomer of any two samples in the sample set within a corresponding target wave number range; determining a comprehensive residual feature of the target sample by using each spectral characterization characteristic value in the target spectrum curve, determining a comprehensive residual intensity index of the target monomer in the corresponding sample set by using the key samples and the comprehensive residual feature; and determining a content detection result of the target monomer in the photoresist resin to be detected based on the comprehensive residual intensity index of the target monomer. Through the technical scheme of the present application, the detection accuracy of the content determination of the residual monomers in the photoresist resin is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of chemical or physical testing technology, and specifically to a testing system for residual monomers in photoresist resin. Background Technology

[0002] As the core film-forming material of photoresist, photoresist resin is highly focused on high-end manufacturing fields that rely on photolithography to achieve fine pattern transfer, covering multiple core industries such as semiconductors, flat panel displays, and new energy. Different types of resin are selected according to process requirements in different scenarios, and their performance directly affects the quality and precision of the process. The relative strength and abnormal fluctuations of residual monomers in photoresist resin are core aspects of photoresist production and quality control, and their importance permeates the entire process from photoresist synthesis and process application to semiconductor device yield.

[0003] Currently, spectroscopic methods are widely used for detecting residual monomers in photoresist resins due to their relatively simple operation and rapid detection. However, in actual detection, the polymer chain structure of the photoresist resin itself often contains functional groups similar to those of the residual monomers. For example, in some photoresist systems, the polymer chain of the photoresist resin may contain functional groups such as carbon-carbon double bonds, and the residual monomers may also contain similar double bond structures. This leads to the absorption peaks of the photoresist resin overlapping with the characteristic peaks of the residual monomers during spectroscopic detection, directly affecting the accurate determination of the residual monomer content. Moreover, some photoresists also contain various additives, such as photoacid generators and sensitizers. These additives also have specific chemical structures and functional groups, and their characteristic peaks in the spectrum are also very likely to overlap with the peaks of the residual monomers. This overlap not only increases the difficulty of distinguishing the characteristic peaks of the residual monomers, but also makes the determination of the residual monomer content based on spectroscopic analysis extremely inaccurate. Summary of the Invention

[0004] To address the low accuracy of current spectroscopic methods for detecting residual monomer content in photoresist resins, this invention aims to provide a testing system for residual monomers in photoresist resins. The specific technical solution adopted is as follows:

[0005] This invention provides a testing system for residual monomers in photoresist resin, the system comprising:

[0006] The monomer analysis module is used to determine the target spectral curve of the target sample taken from the photoresist resin to be tested, and to determine the spectral characterization value of the target monomer by using the absorption intensity of the target monomer in the target spectral curve; to determine the sample set corresponding to the target monomer, and to determine the key samples in the sample set that are highly correlated with the target monomer by using the similarity of the target monomers of any two samples in the sample set within the corresponding target wavenumber range; to determine the comprehensive residual characteristics of the target sample by using the various spectral characterization values ​​in the target spectral curve, and to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set by using the key samples and the comprehensive residual characteristics;

[0007] The content prediction module is used to determine the content of the target monomer in the photoresist resin under test based on the comprehensive residual strength index of the target monomer.

[0008] Furthermore, the step of determining the spectral characterization features of the target monomer using the absorption intensity of the target monomer in the target spectral curve includes:

[0009] Determine the tangent slope curve of the target spectral curve, and determine the zero-crossing point of the target monomer within the target wavenumber range corresponding to the tangent slope curve;

[0010] The absorption intensity of the target monomer in the target spectral curve is determined based on the left-side average slope and the right-side average slope at the zero-crossing point, and the spectral characterization feature value of the target monomer is determined using the absorption intensity.

[0011] Furthermore, the determination of the spectral characterization features of the target monomer using absorption intensity includes:

[0012] Based on the shape of the curve segment within the target wavenumber range in the target spectral curve, the possibility of the actual existence of the target monomer in the target spectral curve is determined.

[0013] Using the actual probability of existence and absorption intensity, the spectral characterization features of the target monomer are calculated.

[0014] Furthermore, determining the likelihood of the target monomer's actual existence in the target spectral curve based on the curve segment shape within the target wavenumber range of the target monomer in the target spectral curve includes:

[0015] Determine the morphological similarity between the curve segments of the target monomer within the target wavenumber range in the target spectral curve and the standard spectral curve, respectively.

[0016] The morphological similarity is used to determine the probability that the target monomer actually exists in the target spectral curve; wherein the morphological similarity is positively correlated with the probability of actual existence.

[0017] Furthermore, the step of determining key samples in the sample set that are highly correlated with the target individual by utilizing the similarity between any two samples in the sample set within the corresponding target wavenumber range includes:

[0018] Determine the similarity of the curve segments of the target individual in any two samples in the sample set within the corresponding target wavenumber range;

[0019] Samples with a curve segment morphological similarity greater than a preset similarity threshold are designated as key samples that are highly correlated with the target individual.

[0020] Furthermore, the determination of the comprehensive residual characteristics of the target sample using the various spectral characterization feature values ​​in the target spectral curve includes:

[0021] The total number of monomers and the average value of spectral characterization features in the target sample are determined using the individual spectral characterization features in the target spectral curve.

[0022] The comprehensive residual characteristics of the target sample are calculated using the total number of monomers and the average value of the spectral characterization features.

[0023] Furthermore, the determination of the comprehensive residual intensity index of the target monomer in the corresponding sample set using key samples and comprehensive residual characteristics includes:

[0024] The first percentage of the number of key markers in a target sample that are marked as key samples relative to the total number of individuals in the target sample;

[0025] The adjusted comprehensive residual characteristics are obtained by adjusting the comprehensive residual characteristics using the first quantity ratio, and the comprehensive residual intensity index of the target monomer in the corresponding sample set is determined by using key samples and the adjusted comprehensive residual characteristics.

[0026] Furthermore, the determination of the comprehensive residual intensity index of the target monomer in the corresponding sample set using key samples and adjusted comprehensive residual characteristics includes:

[0027] By using the second proportion of the number of key samples in the sample set relative to the total number of all samples, the correlation index representing the commonality of the target individual in all samples in the sample set is determined;

[0028] Using the correlation index and the adjusted comprehensive residual characteristics, the comprehensive residual intensity index of the target monomer in the corresponding sample set is determined.

[0029] Furthermore, the determination of the correlation index representing the commonalities of the target entities among all samples in the sample set by using the second proportion of the number of key samples in the sample set relative to the total number of all samples includes:

[0030] Determine the similarity of the curve segments of any two target individuals in the sample set within the corresponding target wavenumber range, and determine the average similarity of the curve segments.

[0031] Using the average similarity of the curve segments and the second quantity ratio, the correlation index representing the commonality of the target individual in all samples in the sample set is calculated.

[0032] Furthermore, the determination of the content detection results of the target monomer in the photoresist resin under test based on the comprehensive residual strength index of the target monomer includes:

[0033] The comprehensive residual strength index of the target monomer is input into the preset regression model, and the content detection results of the target monomer in the photoresist resin to be tested are output.

[0034] The preset regression model is trained using a dataset consisting of the comprehensive residual intensity index of each monomer and its corresponding real concentration.

[0035] The present invention has the following beneficial effects:

[0036] This invention analyzes the spectral curves of multiple samples of the same photoresist resin under test individually, combining the absorption peak characteristics of various monomers in the spectral curves to determine the possible residual monomer content in each sample. Based on the residual monomer content of the samples, a comprehensive residual characteristic of the samples is obtained. Further analysis of the differences between the spectral curves of different samples is conducted to comprehensively screen and determine the residual intensity index of each type of residual monomer, thereby obtaining the content of each monomer. Based on the spectral detection of each sample and the analysis of the spectral differences between samples, starting from the overall spectral characteristics of the photoresist resin under test, the true characteristics of the corresponding monomers are gradually locked in, avoiding noise interference from irrelevant characteristic peaks. This significantly improves the accuracy of residual monomer content determination in photoresist resin and greatly reduces the accuracy interference caused by the overlap of characteristic peaks of the same functional groups. Attached Figure Description

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 The flowchart shows the steps of a testing system for residual monomers in photoresist resin according to an embodiment of the present invention.

[0039] Figure 2A detailed flowchart of step S1 in a test system for residual monomers in photoresist resin provided in an embodiment of the present invention.

[0040] Figure 3 A detailed flowchart of step S12 in a test system for residual monomers in photoresist resin provided in an embodiment of the present invention;

[0041] Figure 4 A detailed flowchart of step S3 in a test system for residual monomers in photoresist resin provided in an embodiment of the present invention;

[0042] Figure 5 A detailed flowchart of step S32 in a test system for residual monomers in photoresist resin provided in an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the hardware operating environment of the testing equipment for residual monomers in photoresist resin involved in the embodiments of the present invention;

[0044] Figure 7 This is a schematic diagram of the framework structure of the testing system for residual monomers in photoresist resin according to an embodiment of the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a testing system for residual monomers in photoresist resin according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a testing system for residual monomers in photoresist resin provided by the present invention.

[0048] Example 1:

[0049] For the testing system for residual monomers in photoresist resin provided by this invention, please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a schematic diagram of the framework structure of the testing system for residual monomers in photoresist resin according to an embodiment of the present invention.

[0050] The testing system for residual monomers in the photoresist resin includes:

[0051] The monomer analysis module A10 is used to determine the target spectral curve of the target sample taken from the photoresist resin to be tested, and to determine the spectral characterization value of the target monomer by using the absorption intensity of the target monomer in the target spectral curve; to determine the sample set corresponding to the target monomer, and to determine the key samples in the sample set that are highly correlated with the target monomer by using the similarity of the target monomers of any two samples in the sample set within the corresponding target wavenumber range; to determine the comprehensive residual characteristics of the target sample by using the various spectral characterization values ​​in the target spectral curve, and to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set by using the key samples and the comprehensive residual characteristics;

[0052] The content prediction module A20 is used to determine the content detection results of the target monomer in the photoresist resin to be tested based on the comprehensive residual strength index of the target monomer.

[0053] Please see Figure 1 , Figure 1 A flowchart of the steps corresponding to the testing system for residual monomers in photoresist resin provided in one embodiment of the present invention is shown.

[0054] The method steps corresponding to the testing system for residual monomers in the photoresist resin include:

[0055] Step S1: Determine the target spectral curve of the target sample taken from the photoresist resin to be tested, and determine the spectral characterization feature value of the target monomer by using the absorption intensity of the target monomer in the target spectral curve.

[0056] In this embodiment, the sample to be tested is first determined, and several samples are selected from the photoresist resin to be tested. If there are impurities on the sample surface, the visible impurities are carefully removed with clean tweezers or a scraper, and then the sample is ground and prepared. Then, a Fourier transform infrared spectrometer is used to test all samples and obtain the infrared spectrum curve of each sample. The following description will be based on the target sample (which can refer to any sample) and its target spectrum curve.

[0057] Among them, sample grinding and slide preparation, and infrared spectroscopy detection are existing technologies and will not be discussed in detail here; in addition, baseline correction and noise reduction processing can be performed on the detected spectral data to eliminate instrument background noise.

[0058] During the synthesis of photoresist resins, due to the difficulty in achieving absolute complete polymerization, some residual monomers are unavoidable. These include acrylates (such as methyl methacrylate and hydroxyethyl acrylate), styrene derivatives (such as styrene and p-hydroxystyrene), epoxy derivatives (such as bisphenol A diglycidyl ether and epichlorohydrin), and maleic anhydride. The content of these residual monomers varies depending on the type of photoresist, the synthesis process, and the quality of the raw materials. Each residual monomer exhibits specific absorption peaks in its spectral curve. For example, acrylate monomers, due to the presence of ester groups, show peaks in the wavenumber range of 1720-1730. It exhibits a strong absorption peak, which is characteristic of ester compounds; the benzene ring skeleton of styrene monomers will show a peak at 1450-1600. There are multiple absorption peaks; epoxy monomers contain epoxy groups, which will appear at 900-915. The presence of a characteristic absorption peak is an important recognition peak for epoxy groups; maleic anhydride monomers will show a peak at 1750-1800. and 1770-1820 Two strong absorption peaks appear; this is relatively easy to understand. If the target monomer is an acrylate monomer, then the target wavenumber range in the target spectrum is 1720 - 1730. .

[0059] Therefore, it is necessary to first analyze the spectral characteristics of each sample within the corresponding wavenumber range of the spectral curve to preliminarily determine the residual characteristics of each possible monomer.

[0060] Specifically, please refer to Figure 2 Step S1, which involves determining the spectral characterization features of the target monomer using the absorption intensity of the target monomer in the target spectral curve, includes:

[0061] Step S11: Determine the tangent slope curve of the target spectral curve, and determine the zero-crossing point of the target monomer within the target wavenumber range in the tangent slope curve;

[0062] Step S12: Determine the absorption intensity of the target monomer in the target spectral curve based on the left-hand average slope and the right-hand average slope of the zero-crossing point, and use the absorption intensity to determine the spectral characterization feature value of the target monomer.

[0063] More specifically, please refer to Figure 3 Step S12, which uses absorption intensity to determine the spectral characterization features of the target monomer, includes:

[0064] Step S121: Based on the shape of the curve segment corresponding to the target wavenumber range in the target spectral curve, determine the possibility that the target monomer actually exists in the target spectral curve.

[0065] More specifically, step S121 includes:

[0066] Determine the morphological similarity between the curve segments of the target monomer within the target wavenumber range in the target spectral curve and the standard spectral curve, respectively.

[0067] The morphological similarity is used to determine the probability that the target monomer actually exists in the target spectral curve; wherein the morphological similarity is positively correlated with the probability of actual existence.

[0068] Step S122: Using the actual existence probability and absorption intensity, calculate the spectral characterization features of the target monomer.

[0069] In this embodiment, for the target spectral curve of the target sample, the spectral curve characteristics of each target wavenumber range are analyzed:

[0070] Taking acrylate monomers as an example, their target wavenumber range is 1720-1730. It has a strong absorption peak.

[0071] The first derivative of the entire target spectral curve can be used to obtain the tangent slope curve (i.e., the first derivative curve). The derivative spectrum can highlight the slope change of the spectral curve, specifically the 1720-1730° range of the first derivative curve. Within the range, determine whether there is a zero-crossing point (i.e., whether it intersects the x-axis). If there is a zero-crossing point, and the original spectral intensity near the zero-crossing point (e.g., taking a neighborhood radius of 20) exceeds the noise floor threshold (calculating the noise floor threshold is an existing technique that can be obtained through statistical calculation based on the spectral baseline region), then it indicates that there is an absorption peak at the corresponding wavenumber position of the original spectral curve; if there is no absorption peak, it indicates that there is no absorption peak within this wavenumber range.

[0072] If an absorption peak exists, the average slope of the first derivative curve within a certain range on both sides of the zero-crossing point is calculated to represent the intensity of the absorption peak. For example, when the zero-crossing point is located at wavenumber a0, the absolute value of the average slope from a0-n to a0 on the left is calculated and denoted as v1, and the absolute value of the average slope from a0+n to a0 on the right is denoted as v2. Combining v1 and v2, the absorption intensity of the target monomer in the target spectrum curve can be obtained. The value of n can be, for example, 20 (the number of data points), or it can be adjusted according to the actual detection accuracy requirements. For example, n can be "1 / 2 of the target wavenumber range width" or "the width of a preset ratio," without restriction.

[0073] Simultaneously, standard spectral curves of acrylate monomers (i.e., the spectral curves of the monomer itself, unaffected by other structures) were obtained, and the 1720-1730°C values ​​in the standard curve and the actual sample curve were compared. The Euclidean distance of the inner curve segment is used to represent the (morphological) similarity between the actual curve (corresponding to the target spectral curve) and the standard curve in the corresponding curve segment. The similarity coefficient between the two is represented by b. The larger the value of b, the smaller the Euclidean distance between the two. The negative number of the Euclidean distance can be normalized as the similarity. The greater the probability of the actual existence of the target single entity in the target spectral curve, the greater the probability of actual existence can be quantified as the morphological similarity b. Alternatively, the normalized (such as maximum and minimum value normalization) morphological similarity b can be used as the probability of actual existence.

[0074] Therefore, in the measured spectral curve of the target sample, the spectral characterization feature value A of the acrylate monomer can be expressed as: ;

[0075] in, The value of A represents the absorption intensity of the target wavenumber range reflected in the measured spectrum (target spectral curve), which is corrected by the morphological similarity b. The larger the absolute value of the slope on both sides of the zero point and the more similar it is to the standard spectrum, the more obvious the spectral characterization characteristics of this type of monomer are, and the greater the probability that this type of monomer exists in the sample, and the larger the corresponding value of A is. norm represents the normalization function (such as maximum and minimum value normalization), and the value range can be [0,1]. The value of A represents the comprehensive index of "the similarity between the spectral morphology and the standard monomer" and "the rate of change of the characteristic interval". b plays the role of morphological filtering in this process. Only signals with morphology close to the monomer and a large rate of change will be amplified.

[0076] It should be noted that all norm calculations in the embodiments of this invention are linear mappings. If the global extremum cannot be obtained, preset empirical values ​​can be used for analysis. That is, the maximum and minimum values ​​of the normalization process can be set according to the specific scenario and data numerical characteristics. Adjusting, calibrating, or optimizing the maximum and minimum values ​​does not constitute a limitation of this invention. When the maximum value in the dataset normalized by the norm is equal to the minimum value, the normalization result is uniformly set to 0; if the denominator is 0, the result is also set to 0.

[0077] When the first derivative curve does not have a zero-crossing point, that is, when there is no absorption peak, the value of A above is 0.

[0078] Since maleic anhydride monomers have two characteristic peaks in two wavenumber ranges, each wavenumber range is analyzed separately, and the characteristic value obtained from the two wavenumber ranges (e.g., taking the average value) is used as the spectral characterization characteristic value A of maleic anhydride monomers. For any type of monomer, when A is greater than 0, it indicates that the monomer may be present in the sample.

[0079] In the same manner described above, determine the spectral characterization values ​​of all possible residual monomers in the measured spectral curve of each sample.

[0080] Step S2: Determine the sample set corresponding to the target individual, and use the similarity between any two samples in the sample set within the corresponding target wavenumber range to determine the key samples in the sample set that are highly correlated with the target individual.

[0081] In this embodiment, during the production process of photoresist resin, the distribution of residual monomers is affected by the complexity of the polymerization reaction itself, as well as by gravity, temperature gradients, and other factors during storage and transportation. This results in a non-uniform distribution of residual monomers, which may be enriched in local areas, exhibiting high residual levels, while in other areas the content is low and the residual characteristics are not obvious. Due to this non-uniform distribution, random sampling will lead to differences in the residual characteristics exhibited by different samples. Therefore, determining the residual status of photoresist resin based solely on the spectral signal of a single sample is not accurate enough; a comprehensive analysis combining the differences between the spectral signals of all samples is also necessary.

[0082] First, all possible residual monomers can be numbered for easier statistical analysis. For example, acrylate monomers, styrene monomers, epoxy monomers, and maleic anhydride monomers can be labeled as monomers 1, 2, 3, and 4, respectively. Based on the possible residual monomers in all samples, all samples are classified according to the type of residual monomer to form a sample set corresponding to the monomer type. For example, all samples containing monomer 1 are in one category, all samples containing monomer 2 are in another category, and so on, thus dividing all samples into four categories.

[0083] Specifically, step S2 involves determining key samples in the sample set that are highly correlated with the target individual within the corresponding target wavenumber range based on the similarity of the target individual between any two samples in the sample set, including:

[0084] Determine the similarity of the curve segments of the target individual in any two samples in the sample set within the corresponding target wavenumber range;

[0085] Samples with a curve segment morphological similarity greater than a preset similarity threshold are designated as key samples that are highly correlated with the target individual.

[0086] In this embodiment, since some samples may contain multiple residual monomers, they may appear in multiple categories at the same time;

[0087] For each possible residual monomer, determine the number of samples included in its category, denoted as d. The larger the value of d, the higher the prevalence of this type of monomer in the photoresist resin, the wider its distribution range, and the greater its potential impact on the performance of the photoresist resin.

[0088] In the sample set of this category, obtain the Euclidean distance F of the curve segments corresponding to the wavenumber range of the monomer in the spectral curves of any two samples, and then obtain the morphological similarity of the curve segments of the two samples, denoted as f (which can be the reciprocal of the Euclidean distance F; the smaller the Euclidean distance, the higher the similarity between the two samples). The smaller the F value, the larger the f value, indicating that the spectral curves of any two samples are more similar in that wavenumber range.

[0089] Calculate the similarity between any two samples to obtain several f values. Since the Euclidean distance ranges from 0 to positive infinity, normalize all f values ​​to the range of 0-1 based on the range of all f values. The preset similarity threshold can be 0.5. When the normalized f is greater than 0.5, it means that the two samples are similar within the same wavenumber range, which may be due to the residual of the same individual. Both are recorded as key samples.

[0090] In the same manner, identify all key samples in the above categories.

[0091] Step S3: Determine the comprehensive residual characteristics of the target sample using the spectral characterization feature values ​​in the target spectral curve, and determine the comprehensive residual intensity index of the target monomer in the corresponding sample set using key samples and comprehensive residual characteristics.

[0092] Specifically, step S3, which uses the spectral characterization feature values ​​in the target spectral curve to determine the comprehensive residual characteristics of the target sample, includes:

[0093] The total number of monomers and the average value of spectral characterization features in the target sample are determined using the individual spectral characterization features in the target spectral curve.

[0094] The comprehensive residual characteristics of the target sample are calculated using the total number of monomers and the average value of the spectral characterization features.

[0095] Based on the above embodiments, the comprehensive residual feature B of the target sample in this embodiment can be expressed as:

[0096] ;

[0097] Where c represents the total number of monomers that may exist in the sample (determined by A, where 4 represents the theoretical maximum value of the total number of monomers, i.e., the number of the four types of monomers mentioned above); A1 represents the mean value of A (average value of spectral characterization feature) of all possible monomers in the sample; the more possible monomers and the larger the feature value, the more residues are in the sample, and the larger the corresponding value of B; it should be noted that if c is 0, when none of the residual monomers exist in the sample, the value of B is 0 (if the value of B is 0, no further analysis is needed), then the number of the subsequent comprehensive residual feature D is directly set to 0.

[0098] Specifically, please refer to Figure 4 Step S3, which uses key samples and comprehensive residual characteristics to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set, includes:

[0099] Step S31: Determine the first percentage of the number of key markers in the target sample that are marked as key samples relative to the total number of individuals in the target sample;

[0100] Step S32: Adjust the comprehensive residual characteristics using the first quantity ratio to obtain the adjusted comprehensive residual characteristics, and use the key samples and the adjusted comprehensive residual characteristics to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set.

[0101] Based on the above embodiments, in this embodiment, the number of times each sample is marked as a key sample is determined and denoted as the number of key marks g. Since there are a total of four types of residual monomers, the maximum value of g is also 4. The more times a sample is marked as a key sample and the greater its similarity to other samples in the same category, the more residual monomer information it contains and the more reliable it is.

[0102] Based on this, the comprehensive residual feature B of the target sample is adjusted, and the adjusted comprehensive residual feature D can be expressed as:

[0103] ;

[0104] Where c represents the number of residual monomers that may exist in the sample initially (the total number of monomers, that is, the number of A greater than 0, consistent with the meaning in the above embodiment); g represents the number of key markers; The first quantity percentage is represented by f1; f1 represents the arithmetic mean of the morphological similarity of the target sample in all categories marked as key samples. The more times a sample is marked as a key sample and the greater its similarity to other samples in the corresponding category, the more reliable the residual monomer information contained in the sample is, and the larger the corresponding value of D is. Same as above, indicating normalization.

[0105] More specifically, please refer to Figure 5 Step S32, which uses key samples and adjusted comprehensive residual characteristics to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set, includes:

[0106] Step S321: Using the second proportion of the number of key samples in the sample set to the total number of all samples, determine the correlation index of the common characteristics of the target individual in all samples in the sample set.

[0107] More specifically, step S321 includes:

[0108] Determine the similarity of the curve segments of any two target individuals in the sample set within the corresponding target wavenumber range, and determine the average similarity of the curve segments.

[0109] Using the average similarity of the curve segments and the second quantity ratio, the correlation index representing the commonality of the target individual in all samples in the sample set is calculated.

[0110] Based on the above embodiments, in this embodiment, the correlation index C of all samples included in the classification sample set of each type of residual monomer can be expressed as:

[0111] ;

[0112] Where d0 represents the number of all key samples in the sample set corresponding to this category; d, as above, represents the total number of all samples in the sample set corresponding to this category. That is, the second largest proportion; This represents the average morphological similarity among all key samples (i.e., the average morphological similarity of curve segments; the morphological similarity is obtained as described in the above embodiment, and here it can specifically refer to the average similarity among key samples). The larger the proportion of key samples and the larger the average similarity, the larger the corresponding value of C. This indicates that the commonalities among all samples included in this classification are stronger, and the spectral curves have high similarity within a specific wavenumber range, which may all come from the same factors (i.e., the corresponding individual factors) resulting in residues.

[0113] It should be noted that if d is 0, that is, if this type of monomer is not detected in all samples, then the correlation index C is directly set to 0 and no formula calculation is performed.

[0114] Step S322: Using the correlation index and the adjusted comprehensive residual characteristics, determine the comprehensive residual intensity index of the target monomer in the corresponding sample set.

[0115] The overall residual intensity index E of the target monomer across all samples in the corresponding sample set can be expressed as:

[0116] ;

[0117] Where D1 represents the mean of the comprehensive residual characteristic D of all samples containing this type of residual monomer, and C represents the correlation index of all samples; the larger the comprehensive residual characteristic value and the greater the correlation of each sample, the greater the residual intensity of this type of residual monomer in the sample, the larger the corresponding value of E, and the greater its impact on the photoresist resin. Same as above, indicating normalization.

[0118] Step S4: Based on the comprehensive residual strength index of the target monomer, determine the content detection results of the target monomer in the photoresist resin to be tested.

[0119] Specifically, step S4 includes:

[0120] The comprehensive residual strength index of the target monomer is input into the preset regression model, and the content detection results of the target monomer in the photoresist resin to be tested are output.

[0121] The preset regression model is trained using a dataset consisting of the comprehensive residual intensity index of each monomer and its corresponding real concentration.

[0122] Based on the above embodiments, in this embodiment, the comprehensive residual intensity index of each type of residual monomer is determined in the same manner. And the corresponding preset residual strength threshold can be set to 0.4;

[0123] When the overall residual strength index of a certain residual monomer is greater than the above-mentioned preset threshold, it indicates that the content of this type of residual monomer is relatively high in the tested photoresist resin and its distribution in the sample is relatively wide. High intensity of residue and wide distribution may have a significant impact on the performance of photoresist resin. At this time, it may be necessary to make relevant adjustments to the photoresist production process to reduce the content of residual monomer and ensure the stability and reliability of photoresist performance.

[0124] When the residual strength index is less than the above-mentioned preset threshold, it indicates that the content of this type of residual monomer in the tested photoresist resin is relatively low, the impact on the performance of the photoresist is relatively weak, and the product quality is relatively stable.

[0125] The spectral data of each detected sample is transmitted to the system's data center. The data is then analyzed according to the steps described above to obtain the test results of various residual monomers in the detected photoresist resin.

[0126] Specifically, an index can be established using multiple photoresist resin samples with known monomer concentrations and index E. The pre-defined regression model is used to compare the actual concentration with the dimensionless concentration. The value is converted into a specific concentration reference value for the corresponding residual monomer. The preset regression model here can be an existing model such as convolutional neural network, support vector regression, or multilayer perceptron.

[0127] This invention analyzes the spectral curves of multiple samples of the same photoresist resin under test individually, combining the absorption peak characteristics of various monomers in the spectral curves to determine the possible residual monomer content in each sample. Based on the residual monomer content of the samples, a comprehensive residual characteristic of the samples is obtained. Further analysis of the differences between the spectral curves of different samples is conducted to comprehensively screen and determine the residual intensity index of each type of residual monomer, thereby obtaining the content of each monomer. Based on the spectral detection of each sample and the analysis of the spectral differences between samples, starting from the overall spectral characteristics of the photoresist resin under test, the true characteristics of the corresponding monomers are gradually locked in, avoiding noise interference from irrelevant characteristic peaks. This significantly improves the accuracy of residual monomer content determination in photoresist resin and greatly reduces the accuracy interference caused by the overlap of characteristic peaks of the same functional groups.

[0128] Example 2:

[0129] This invention also proposes a testing device for residual monomers in photoresist resin. The device can be an infrared spectrometer, a computer, a server, or a combination of multiple devices.

[0130] like Figure 6 As shown, Figure 6 This is a schematic diagram of the hardware operating environment of the testing equipment for residual monomers in photoresist resin involved in the embodiments of the present invention.

[0131] like Figure 6 As shown, the testing device for residual monomers in the photoresist resin may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a testing program for residual monomers in the photoresist resin.

[0132] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0133] Continue to refer to Figure 6 , Figure 6 The memory 1005, which is a computer-readable storage medium, may include an operating device, a user interface module, a network communication module, and a test program for residual monomers in photoresist resin.

[0134] exist Figure 6 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the test program for residual monomers in photoresist resin stored in the memory 1005 and execute the steps in the above embodiments.

[0135] Based on the hardware structure of the above-mentioned testing equipment for residual monomers in photoresist resin, various embodiments of the testing system for residual monomers in photoresist resin of the present invention are used to implement the present invention.

[0136] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention stores a test program for residual monomers in photoresist resin, wherein when the test program for residual monomers in photoresist resin is executed by a processor, the steps of the method corresponding to the test system for residual monomers in photoresist resin described above are implemented.

[0137] The method implemented when the test procedure for residual monomers in photoresist resin is executed can be referred to in various embodiments of the test system for residual monomers in photoresist resin of the present invention, and will not be repeated here.

[0138] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0139] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0140] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A testing system for residual monomers in photoresist resin, characterized in that, The system includes: The monomer analysis module is used to determine the target spectral curve of the target sample taken from the photoresist resin to be tested, and to determine the spectral characterization value of the target monomer by using the absorption intensity of the target monomer in the target spectral curve; to determine the sample set corresponding to the target monomer, and to determine the key samples in the sample set that are highly correlated with the target monomer by using the similarity of the target monomers of any two samples in the sample set within the corresponding target wavenumber range; to determine the comprehensive residual characteristics of the target sample by using the various spectral characterization values ​​in the target spectral curve, and to determine the comprehensive residual intensity index of the target monomer in the corresponding sample set by using the key samples and the comprehensive residual characteristics; The content prediction module is used to determine the content detection results of the target monomer in the photoresist resin to be tested based on the comprehensive residual strength index of the target monomer. The spectral characterization features of a target monomer are determined by utilizing its absorption intensity in the target spectral curve. This process includes: determining the tangent slope curve of the target spectral curve; identifying the zero-crossing point of the target monomer within the target wavenumber range corresponding to the tangent slope curve; determining the absorption intensity of the target monomer in the target spectral curve based on the average slope to the left and right of the zero-crossing point; determining the probability of the target monomer's actual presence in the target spectral curve based on the curve segment shape within the target wavenumber range corresponding to the target monomer; and calculating the spectral characterization features of the target monomer using the probability of actual presence and the absorption intensity. The key samples that are highly correlated with the target individual in the sample set are determined by the similarity of the target individual between any two samples in the sample set within the corresponding target wavenumber range. This includes: determining the curve segment morphological similarity between any two samples in the sample set within the corresponding target wavenumber range; and designating samples with curve segment morphological similarity greater than a preset similarity threshold as key samples that are highly correlated with the target individual. The comprehensive residual intensity index of the target monomer in the corresponding sample set is determined by using key samples and comprehensive residual characteristics, including: determining the first quantity ratio of the number of key markers of the target sample that are marked as key samples to the total number of monomers in the target sample; adjusting the comprehensive residual characteristics using the first quantity ratio to obtain the adjusted comprehensive residual characteristics; and determining the comprehensive residual intensity index of the target monomer in the corresponding sample set using key samples and adjusted comprehensive residual characteristics.

2. The testing system for residual monomers in photoresist resin according to claim 1, characterized in that, Based on the shape of the curve segment corresponding to the target wavenumber range in the target spectral curve, the likelihood of the target monomer actually existing in the target spectral curve is determined, including: Determine the morphological similarity between the curve segments of the target monomer within the target wavenumber range in the target spectral curve and the standard spectral curve, respectively. Morphological similarity is used to determine the probability of a target monomer actually existing in the target spectral curve; where morphological similarity is positively correlated with the probability of actual existence.

3. The testing system for residual monomers in photoresist resin according to claim 1, characterized in that, The comprehensive residual characteristics of the target sample are determined using the spectral characterization values ​​in the target spectral curve, including: The total number of monomers and the average value of spectral characterization features in the target sample are determined using the individual spectral characterization features in the target spectral curve. The comprehensive residual characteristics of the target sample are calculated by using the total number of monomers and the average value of spectral characterization features.

4. The testing system for residual monomers in photoresist resin according to claim 1, characterized in that, The comprehensive residual intensity index of the target monomer in the corresponding sample set is determined using key samples and adjusted comprehensive residual characteristics, including: By using the second proportion of the number of key samples in the sample set relative to the total number of all samples, the correlation index representing the commonality of the target individual in all samples in the sample set is determined; By using the correlation index and the adjusted comprehensive residual characteristics, the comprehensive residual intensity index of the target monomer in the corresponding sample set is determined.

5. The testing system for residual monomers in photoresist resin according to claim 4, characterized in that, By using the second-highest proportion of key samples in the sample set relative to the total number of all samples, a correlation index representing the commonalities of the target entities across all samples in the sample set is determined, including: Determine the similarity of the curve segments of any two target individuals in the sample set within the corresponding target wavenumber range, and determine the average similarity of the curve segments. By using the average similarity of curve segments and the proportion of second quantity, the correlation index representing the commonality of target individuals in all samples in the sample set is calculated.

6. The testing system for residual monomers in photoresist resin according to claim 1, characterized in that, Based on the comprehensive residual strength index of the target monomer, the content detection results of the target monomer in the photoresist resin under test are determined, including: The comprehensive residual strength index of the target monomer is input into the preset regression model, and the content detection results of the target monomer in the photoresist resin to be tested are output. The preset regression model is trained using a dataset consisting of the comprehensive residual intensity index of each monomer and its corresponding real concentration.

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