A device and method for detecting metal impurities in a raw material of a KrF photoresist

Abstract

The present application relates to the technical field of metal impurity detection, and particularly relates to a kind of KrF photoresist raw material metal impurity detection device and method, solve the technical problems that the accuracy and stability of photoresist raw material metal impurity detection result are low due to the neglect of volatile metal escape loss, container adsorption effect and batch difference in prior art.The method comprises: performing sealed digestion pretreatment on the sample to be measured, collecting environmental time series data, original measurement concentration of the sample to be measured and blank background concentration of reagent blank sample;According to the concentration change of the sample to be measured before and after adding standard solution, determine the volatility compensation coefficient and adsorption compensation coefficient;The digestion environment parameters are extracted from the environmental time series data, and the compensation coefficients are corrected;According to the original measurement concentration, the blank background concentration, the corrected volatility compensation coefficient and the adsorption compensation coefficient, the concentration of metal elements in the sample to be measured is determined.

Description

Technical Field

[0001] This invention relates to the field of metal impurity detection technology, and specifically to a device and method for detecting metal impurities in KrF photoresist raw materials. Background Technology

[0002] In the manufacturing process of ultra-large-scale integrated circuits, KrF photoresist is a core key material. The content of trace metal impurities in its raw materials is a core quality control indicator that determines the quality of photoresist and the yield of chip manufacturing. Excessive levels of elements such as mercury and arsenic can directly cause circuit defects and electrical failures. Therefore, extremely stringent requirements are placed on the detection accuracy and stability of this indicator.

[0003] Inductively coupled plasma mass spectrometry (ICP-MS) has become the mainstream technology for detecting metal impurities in KrF photoresist raw materials due to its extremely high sensitivity. In actual industrial testing, the conventional approach is to digest the photoresist raw material with a mixture of strong acids such as nitric acid and hydrofluoric acid, then bring the volume to a constant and directly measure the metal element signals using an ICP-MS. The impurity concentration is then calculated based on a standard curve and simple blank background correction.

[0004] However, due to the complex organic matrix environment of photoresist raw materials, the acid digestion process inevitably involves the release and loss of volatile metal compounds such as mercury and arsenic, as well as the physical adsorption effect of trace metals on the digestion container wall. At the same time, the fluctuation of raw material composition and the difference in digestion environment conditions between different batches will further amplify the above-mentioned detection deviations, making it difficult for existing static detection schemes to truly reflect the actual metal impurity content of the sample. Summary of the Invention

[0005] To address the technical problems of low accuracy and stability in the detection of metal impurities in photoresist raw materials due to neglecting the loss of volatile metals, container adsorption effects, and batch variations in existing technologies, the present invention aims to provide a device and method for detecting metal impurities in KrF photoresist raw materials. The specific technical solution adopted is as follows: In a first aspect, a method for detecting metal impurities in KrF photoresist raw materials is provided, comprising: performing a sealed digestion pretreatment on the sample to be tested; collecting environmental time-series data of the digestion process, the original measured concentration of the sample to be tested, and the blank background concentration of the reagent blank sample; determining the volatility compensation coefficient and adsorption compensation coefficient, which characterize the degree of loss of metal elements in the sample matrix, based on the concentration change of the sample to be tested before and after the addition of the standard solution; extracting digestion environmental parameters from the environmental time-series data, correcting the volatility compensation coefficient and adsorption compensation coefficient, and obtaining the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient; and determining the concentration of metal elements in the sample to be tested based on the original measured concentration, the blank background concentration, the corrected volatility compensation coefficient, and the corrected adsorption compensation coefficient.

[0006] Based on the above technical solution, in the metal impurity detection method for KrF photoresist raw materials provided by this invention, the sample to be tested is subjected to sealed digestion pretreatment with integrated environmental sensing to suppress metal evaporation and simultaneously collect temperature and pressure time-series data of the digestion process. Then, by using a standard solution spiked recovery experiment in a real sample matrix, the degree of actual volatile metal evaporation loss and adsorption loss on the container wall is accurately quantified, and a volatility and adsorption compensation coefficient matching the sample matrix is ​​obtained. Then, the aforementioned compensation coefficient is corrected a second time based on the environmental time-series data collected in real time at the digestion site. Finally, the blank background concentration and the corrected compensation coefficient are used to perform background subtraction and back-calculation of the comprehensive recovery rate of the original concentration measured by the instrument. This eliminates the systematic detection deviation introduced by the mismatch of the standard solution matrix and the fluctuation of the digestion environment in the traditional scheme, and realizes high-precision quantitative detection of trace metal impurities in KrF photoresist raw materials.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for determining the volatility compensation coefficient based on the concentration change of the test sample before and after the addition of the standard solution specifically includes: obtaining a first theoretical concentration of the volatile metal standard solution; the volatile metal standard solution containing a specified concentration of volatile target metal ions; obtaining the sample detection concentration of the test sample and the first spike detection concentration of the first spiked sample; the first spiked sample being obtained by adding the volatile metal standard solution to the test sample; and determining the volatility compensation coefficient based on the first theoretical concentration, the first spike detection concentration, and the sample detection concentration.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for determining the adsorption compensation coefficient based on the concentration change of the test sample before and after the addition of the standard solution specifically includes: obtaining a second theoretical concentration of the non-volatile metal standard solution; the non-volatile metal standard solution containing a specified concentration of non-volatile target metal ions; obtaining the sample detection concentration of the test sample and the second spike detection concentration of the second spiked sample; the second spiked sample being obtained by adding the non-volatile metal standard solution to the test sample; and determining the adsorption compensation coefficient based on the second theoretical concentration, the second spike detection concentration, and the sample detection concentration.

[0009] In conjunction with the first aspect above, in one possible implementation, before determining the volatility compensation coefficient and adsorption compensation coefficient based on the concentration change of the sample before and after adding the standard solution, the method further includes: detecting the organic component characteristics of the sample and reading the historically stored standard matrix component characteristics; if the difference between the organic component characteristics and the standard matrix component characteristics is within a preset difference threshold range, then directly calling the volatility compensation coefficient and adsorption compensation coefficient corresponding to the historically stored standard matrix component characteristics; if the difference between the organic component characteristics and the standard matrix component characteristics exceeds the preset difference threshold range, then performing a spiking experiment by adding the standard solution to the sample to be tested to redetermine the volatility compensation coefficient and adsorption compensation coefficient.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for collecting environmental time-series data of the digestion process, the original measured concentration of the test sample, and the blank background concentration of the reagent blank sample specifically includes: placing the test sample in a sealed digestion container integrated with a temperature sensor and a pressure sensor, and adding a mixed acid digestion solution to the sealed digestion container; executing a preset temperature-progression digestion curve, and synchronously recording temperature and pressure data in real time during the digestion process using the temperature sensor and pressure sensor to constitute environmental time-series data; after digestion is completed and the sample solution is brought to a constant volume, measuring the signal intensity of the metal element using inductively coupled plasma mass spectrometry, and converting the signal intensity to obtain the original measured concentration; preparing a reagent blank sample that is digested synchronously with the test sample, and using the same digestion and measurement conditions as the test sample to obtain the blank background concentration.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method of extracting digestion environmental parameters from environmental time-series data and correcting the volatility compensation coefficient and adsorption compensation coefficient to obtain the corrected volatility compensation coefficient and adsorption compensation coefficient specifically includes: from temperature data, statistically analyzing the actual average temperature of the highest temperature insulation stage, and determining the volatility temperature correction coefficient based on the deviation between the actual average temperature and the preset highest temperature; from temperature data, statistically analyzing the actual average temperature of the cooling and constant-volume stage, and determining the adsorption temperature correction coefficient based on the deviation between the actual average temperature and the preset constant-volume temperature; from pressure data, extracting the actual average pressure corresponding to the highest temperature insulation stage; determining the pressure correction coefficient based on the deviation between the actual average pressure and the preset pressure value; fusing the volatility compensation coefficient with the volatility temperature correction coefficient and the pressure correction coefficient to obtain the corrected volatility compensation coefficient; and fusing the adsorption compensation coefficient with the adsorption temperature correction coefficient to obtain the corrected adsorption compensation coefficient.

[0012] In conjunction with the first aspect above, in one possible implementation, the method further includes: if the value of either the corrected volatility compensation coefficient or the corrected adsorption compensation coefficient is greater than or equal to a preset effectiveness threshold, then the current digestion process is determined to be ineffective, and the subsequent steps for determining the concentration of metal elements are terminated.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for determining the concentration of a metal element in a sample based on the original measured concentration, blank background concentration, corrected volatility compensation coefficient, and corrected adsorption compensation coefficient specifically includes: if the metal element to be tested is a volatile element, then the corrected adsorption compensation coefficient is set to zero, and the concentration of the metal element to be tested is determined based on the original measured concentration, blank background concentration, and corrected volatility compensation coefficient; if the metal element to be tested is a readily adsorbed non-volatile element, then the corrected volatility compensation coefficient is set to zero, and the concentration of the metal element to be tested is determined based on the original measured concentration, blank background concentration, and corrected adsorption compensation coefficient.

[0014] In conjunction with the first aspect above, in one possible implementation, the method further includes: comparing the concentration of each metal element with the corresponding standard concentration threshold to obtain the compliance judgment result of each metal element, and obtaining the comprehensive quality index based on the ratio of the concentration of each metal element to the corresponding standard concentration threshold; and outputting the quality assessment result of the sample to be tested based on the compliance judgment result of each metal element and the comprehensive quality index.

[0015] Secondly, a device for detecting metal impurities in KrF photoresist raw materials is provided, comprising: a pretreatment and acquisition unit for performing sealed digestion pretreatment on the sample to be tested, acquiring environmental time-series data of the digestion process, the original measured concentration of the sample to be tested, and the blank background concentration of the reagent blank sample; a matrix compensation unit for determining the volatility compensation coefficient and adsorption compensation coefficient, which characterize the degree of loss of metal elements in the matrix of the sample to be tested, based on the concentration change of the sample to be tested before and after the addition of the standard solution; a dynamic correction unit for extracting digestion environmental parameters from the environmental time-series data, correcting the volatility compensation coefficient and adsorption compensation coefficient, and obtaining the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient; and a calibration output unit for determining the concentration of metal elements in the sample to be tested based on the original measured concentration, the blank background concentration, the corrected volatility compensation coefficient, and the corrected adsorption compensation coefficient.

[0016] The present invention has the following beneficial effects: By performing a sealed digestion pretreatment with integrated environmental sensing on the sample to be tested, metal evaporation is suppressed and temperature and pressure time-series data of the digestion process are collected simultaneously. Then, by using a standard solution spiked recovery experiment in a real sample matrix, the actual evaporation loss of volatile metals and the degree of adsorption loss on the container wall are accurately quantified, and a volatility and adsorption compensation coefficient matching the sample matrix is ​​obtained. The aforementioned compensation coefficient is then corrected a second time based on the environmental time-series data collected in real time at the digestion site. Finally, the blank background concentration and the corrected compensation coefficient are used to perform background subtraction and back-calculation of the overall recovery rate of the original concentration measured by the instrument. This eliminates the systematic detection bias introduced by the mismatch of the standard solution matrix and the fluctuation of the digestion environment in the traditional scheme, and realizes high-precision quantitative detection of trace metal impurities in KrF photoresist raw materials. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a schematic diagram of a metal impurity detection device for KrF photoresist raw material according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for detecting metal impurities in KrF photoresist raw materials, provided as an embodiment of the present invention. Detailed Implementation

[0019] 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 metal impurity detection device and method for KrF photoresist raw materials proposed 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.

[0020] 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.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the metal impurity detection device and method for KrF photoresist raw materials provided by the present invention.

[0022] Please see Figure 1The diagram shows a schematic of a metal impurity detection device for KrF photoresist raw material according to an embodiment of the present invention. The metal impurity detection device for KrF photoresist raw material includes: a preprocessing and acquisition unit 1, a matrix compensation unit 2, a dynamic correction unit 3, and a correction output unit 4.

[0023] Optionally, the metal impurity detection device for the KrF photoresist raw material further includes: a quality assessment unit 5 and a database management unit.

[0024] The pretreatment and acquisition unit 1 is the basic execution module of the entire device. It mainly consists of a vortex mixer, an intelligent sealed digestion container with temperature and pressure sensors, an ultrapure water preparation device, an inductively coupled plasma mass spectrometer, and a blank sample preparation assembly. This module first homogenizes the KrF photoresist raw material sample to be tested. After completing the decontamination pretreatment of the digestion container, a matrix-matched mixed acid digestion solution and a thiourea-ascorbic acid mixed protective agent are added to the sample to be tested. The sealed digestion operation is carried out according to the preset temperature rise curve. During the digestion process, temperature and pressure data are collected in real time to form environmental time-series data. After the digestion is brought to a constant volume, the original measured concentration of the sample to be tested is detected by the inductively coupled plasma mass spectrometer. At the same time, a reagent blank sample is prepared and the blank background concentration is detected. The environmental time-series data generated by the pretreatment and acquisition unit 1 is transmitted to the dynamic correction unit 3. The original measured concentration and the blank background concentration are transmitted synchronously to the correction output unit 4 to provide core basic data for subsequent coefficient correction and concentration calculation.

[0025] The matrix compensation unit 2 is the core module for calculating the compensation coefficient. It consists of a Fourier transform infrared spectrometer, a spiking experiment execution component, and a data processing submodule. This module first detects the organic component characteristics of the sample to be tested using the Fourier transform infrared spectrometer, and then retrieves the standard matrix component characteristics stored in the database management unit to complete the difference comparison. Based on the comparison results, it determines whether to directly call the historical compensation coefficient or to re-perform the spiking experiment. By calculating the concentration changes of the sample before and after adding volatile metal standard solutions and non-volatile metal standard solutions, the volatile compensation coefficient and the adsorption compensation coefficient, which characterize the degree of metal element loss, are calculated respectively. The two types of compensation coefficients generated by the matrix compensation unit 2 are directly transmitted to the dynamic correction unit 3 as the original parameters for subsequent coefficient correction.

[0026] The dynamic correction unit 3 is a key module for optimizing the compensation coefficient. It includes an environmental parameter analysis submodule, a coefficient correction calculation submodule, and a digestion effectiveness determination submodule. This module extracts the environmental time-series data transmitted by the preprocessing and acquisition unit 1, filters out the actual average temperature and actual average pressure, and calculates the temperature correction coefficient and pressure correction coefficient accordingly. Then, it corrects the volatility compensation coefficient and adsorption compensation coefficient transmitted by the matrix compensation unit 2 to obtain the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient. At the same time, it determines whether the digestion process has failed. The two types of compensation coefficients after correction are transmitted to the correction output unit 4. If the digestion failure signal is received, the subsequent concentration calculation process is directly terminated.

[0027] The calibration output unit 4 is the execution module for calculating the metal element concentration. It consists of an element classification and matching submodule and a concentration correction calculation submodule. This module calls the original measured concentration and blank background concentration of the preprocessing and acquisition unit 1, as well as the correction compensation coefficient of the dynamic correction unit 3. According to the physicochemical properties of the metal element to be tested and the matching calculation rules, it distinguishes between volatile elements and easily adsorbed non-volatile elements to complete the concentration calculation. Finally, it determines the true metal element concentration in the sample to be tested. The metal element concentration obtained by the calibration output unit 4 will be transmitted to the quality assessment unit 5 as the basis for sample quality judgment.

[0028] The quality assessment unit 5 is the output module for determining the test results. It includes a standard threshold comparison submodule, a comprehensive quality index calculation submodule, and a result display submodule. This module compares the concentration of each metal element transmitted by the correction output unit 4 with the corresponding standard concentration threshold one by one, calculates the comprehensive quality index, and outputs the quality assessment result of the sample to be tested by combining the single element comparison result and the comprehensive quality index, and intuitively presents the status of the sample as qualified, awaiting retesting, or unqualified.

[0029] The database management unit is a supporting module for data storage and model optimization. It includes a data storage submodule and a correlation model optimization submodule. This module stores the detection parameters of the preprocessing and acquisition unit 1, the compensation coefficients of the matrix compensation unit 2, the correction parameters of the dynamic correction unit 3, the concentration data of the calibration output unit 4, and the judgment results of the quality assessment unit 5. It retrieves multiple batches of different sample data every month for verification testing. It updates the correlation model between the compensation coefficients and the digestion parameters through a preset regression analysis algorithm, and provides the matrix compensation unit 2 with support for calling standard matrix component characteristics and historical compensation coefficients, ensuring the stability and accuracy of the device's long-term detection.

[0030] Please see Figure 2 The diagram illustrates a method flowchart for detecting metal impurities in KrF photoresist raw materials according to an embodiment of the present invention. The method includes: S1. Perform sealed digestion pretreatment on the sample to be tested, and collect environmental time-series data of the digestion process, the original measured concentration of the sample to be tested, and the blank background concentration of the reagent blank sample.

[0031] In some implementations, the sample to be tested is placed in a sealed digestion container integrated with temperature and pressure sensors. Specifically, the KrF photoresist raw material sample to be tested is homogenized. A preset weight (e.g., 2g) of the sample is placed in the digestion container, such as a polytetrafluoroethylene container, and mixed using a vortex mixer at a preset speed (e.g., 3000rpm) for a preset time (e.g., 5min) to eliminate component inhomogeneities within the sample batch and ensure the representativeness of subsequent sampling. Simultaneously, the digestion container undergoes a decontamination pretreatment by immersing it in nitric acid of a preset concentration (e.g., 10%) for a preset time (e.g., 24h), followed by rinsing with ultrapure water a preset number of times (e.g., 3 times) and drying. A 10% concentration (volume fraction) of nitric acid can efficiently dissolve and elute trace metal impurities adsorbed on the container wall. The 24h immersion time ensures that residual contamination on the container wall is fully removed, avoiding background contamination introduced by residual impurities on the container wall and eliminating interference from the container itself for trace metal detection.

[0032] A mixed acid digestion solution, compatible with the KrF photoresist matrix, is added to a sealed digestion container. This solution is prepared using nitric acid and hydrofluoric acid at a volume ratio of 3:1, which effectively digests the organic resin components in the photoresist. The suppression of volatile metal element emissions relies primarily on the fully enclosed environment provided by the sealed digestion container, which integrates temperature and pressure sensors, and the precise control of the reaction rate via a preset temperature-programmed digestion curve. The preset temperature-programmed digestion curve is executed, sequentially progressing through stages of heating from room temperature to 120°C and holding for 30 minutes, heating to 180°C and holding for 60 minutes, and heating to 220°C and holding for 40 minutes. During the digestion process (including the post-digestion cooling and volume adjustment stage), temperature and pressure data are recorded synchronously in real-time using the temperature and pressure sensors, forming environmental time-series data.

[0033] After digestion, the digestion container was allowed to cool to room temperature. The digested sample solution was then diluted to 50 ml with ultrapure water to obtain the sample digestion solution. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the metal elements. The instrument parameters were set as follows: RF power 1500 W, nebulizer gas flow rate 1.0 L / min, and auxiliary gas flow rate 0.8 L / min. The mass spectrometry signal intensity of each target metal element was acquired. The original measured concentration was obtained by converting the signal intensity using a preset signal intensity-concentration conversion relationship.

[0034] Prepare reagent blank samples simultaneously with the digestion process of the test samples. Take the mixed acid digestion solution from the same batch as the test samples and prepare multiple (e.g., 3) equal-volume reagent blank samples. The processing procedure for the blank samples is exactly the same as that for the test samples, including the same liquid addition, programmed temperature digestion, and volume adjustment. Using the same digestion and measurement conditions as the test samples, perform inductively coupled plasma mass spectrometry (ICP-MS) detection on the reagent blank samples. Acquire the signal intensity of the blank samples and convert it into blank background concentration. Obtain the blank background concentration by averaging the detection results of multiple parallel blank samples using a preset algorithm.

[0035] S2. Based on the concentration change of the sample before and after the addition of the standard solution, determine the volatility compensation coefficient and adsorption compensation coefficient, which characterize the degree of loss of metal elements in the sample matrix.

[0036] In some implementations, the organic component characteristics of the sample to be tested are detected, and the historically stored standard matrix component characteristics are read.

[0037] In practice, a Fourier transform infrared spectrometer is used to acquire the infrared spectrum of the sample to be tested, and the characteristic absorption peaks of the main organic components such as resin, solvent, and additives are extracted. At the same time, internal standard reference peaks that are stable in the sample and unaffected by matrix changes are extracted (e.g., the CH stretching vibration peak of the polymer backbone, wavenumber approximately 2930 cm⁻¹). -1 The ratio of the integrated area of ​​each characteristic absorption peak to the integrated area of ​​the internal standard reference peak is calculated as the relative peak area of ​​that component. The relative peak areas of the corresponding characteristic absorption peaks of the standard KrF photoresist matrix stored in the database are read and compared with the integrated area of ​​the same internal standard reference peak. The ratio of the relative peak areas of the sample to be tested and the standard matrix is ​​calculated. The absolute value of the difference between this ratio and the theoretical standard ratio 1 is used to characterize the degree of difference between the characteristics of the organic component and the characteristics of the standard matrix components.

[0038] If the difference between the organic component characteristics and the standard matrix characteristics is within the preset difference threshold range (e.g., ≤10%), it indicates that the organic component ratio of the test sample and the standard matrix fluctuates little, and the influence of the matrix on the volatilization and adsorption characteristics of metal elements is negligible. In this case, the volatility compensation coefficient and adsorption compensation coefficient corresponding to the historically stored standard matrix characteristics can be directly called to reduce the operational cost of repeated spiking experiments and improve detection efficiency. If the difference between the organic component characteristics and the standard matrix characteristics exceeds the preset difference threshold range (e.g., >10%), it indicates that the organic component of the test sample and the standard matrix differs greatly, and the matrix has a significant impact on the volatilization and adsorption characteristics of metal elements. In this case, a spiking experiment is performed by adding a standard solution to the test sample to redetermine the volatility compensation coefficient and adsorption compensation coefficient.

[0039] In some implementations, a spiked recovery experiment is conducted under standard digestion conditions to obtain the compensation coefficient of the matrix itself. The standard digestion conditions are defined as follows: using the same sealed digestion container and mixed acid digestion solution as S1, digestion is performed according to a preset temperature rise digestion curve (maximum temperature 220℃, holding for 40 min), and the temperature fluctuation during the maximum temperature holding stage is ensured to be no more than ±2℃ and the pressure fluctuation to be no more than ±0.05MPa through an external temperature control device and pressure regulator.

[0040] Under standard digestion conditions, obtain the first theoretical concentration of the volatile metal standard solution, which contains a specified concentration of volatile target metal ions (such as arsenic, mercury, etc.). Under standard digestion conditions, obtain the sample detection concentration of the test sample and the first spike detection concentration of the first spiked sample. The first spiked sample is obtained by adding the volatile metal standard solution to the test sample. Specifically, take 1g of the test sample and add a standard solution of volatile metal elements such as arsenic or mercury at a known concentration (e.g., 10ppb) to obtain the first spiked sample. Process the first spiked sample according to the same digestion, volume adjustment, and detection procedure as S1, and measure the first spiked detection concentration. Record the initial theoretical concentration of the volatile metal standard solution as the first theoretical concentration. Simultaneously, take 1g of the test sample without adding the standard solution and process it according to the same digestion and detection procedure to measure the sample detection concentration.

[0041] Based on the first standard theoretical concentration, the first spiked detection concentration, and the sample detection concentration, the volatility compensation coefficient is determined and expressed as: In the formula, The first theoretical concentration of the volatile metal standard solution refers to the initial theoretical concentration of the target metal ions added to the sample before digestion loss, and the dimension is concentration unit; The first spiked concentration of the first spiked sample refers to the measured concentration of the sample after digestion and detection with the addition of volatile metal standard solution, and the dimension is concentration unit; is the sample detection concentration of the sample to be tested, which refers to the actual concentration of the sample to be tested after digestion and detection without the addition of standard solution, and the dimension is concentration unit; L is the volatility compensation coefficient, which is a dimensionless parameter that characterizes the inherent volatilization loss ratio of volatile metal elements in the sample matrix under standard digestion conditions.

[0042] Characterizing the measured concentration of newly added volatile metal ions in the test sample after spiking, i.e., the concentration of metal ions remaining after the added standard solution is digested and detected in the test sample matrix; molecule The loss concentration of added volatile metal ions during the digestion process of the sample matrix is ​​characterized by the difference between the theoretically measurable concentration of the standard solution and the actual remaining concentration. Dividing this value by the denominator normalizes the ratio of the loss concentration to the theoretical concentration, yielding the loss ratio L. The larger this value, the more severe the volatilization loss of volatile metals in the current sample matrix.

[0043] In some implementations, the second theoretical concentration of the non-volatile metal standard solution is obtained under standard digestion conditions. This non-volatile metal standard solution contains a specified concentration of non-volatile target metal ions (such as copper, iron, etc.). The sample detection concentration of the test sample and the second spike detection concentration of the second spiked sample are obtained under standard digestion conditions. The second spiked sample is obtained by adding the non-volatile metal standard solution to the test sample. Specifically, 1g of the test sample can be taken, and a standard solution of copper, iron, or other non-volatile metal elements of known concentration (e.g., 10ppb) can be added to obtain the second spiked sample. The second spiked sample is then processed according to the same digestion, volume adjustment, and detection procedures as in S1, and the second spiked detection concentration is measured. The initial theoretical concentration of the non-volatile metal standard solution is recorded as the second theoretical concentration. Simultaneously, 1g of the test sample is taken without adding the standard solution and processed according to the same digestion and detection procedures to measure the sample detection concentration. Based on the second theoretical concentration, the second spiked detection concentration, and the sample detection concentration, the adsorption compensation coefficient is determined, expressed as: In the formula, The second theoretical concentration of the non-volatile metal standard solution refers to the initial theoretical concentration of the target metal ions added to the sample before digestion loss, and is expressed in units of concentration. The second spiked concentration of the second spiked sample refers to the measured concentration of the sample after digestion and detection with the addition of a non-volatile metal standard solution, and the dimension is concentration unit; The sample detection concentration is the actual concentration of the sample to be tested after digestion and detection without the addition of standard solution, and the dimension is concentration unit; A is the adsorption compensation coefficient, a dimensionless parameter that characterizes the proportion of the inherent adsorption loss of non-volatile metal elements by the digestion vessel during the digestion of the sample under standard digestion conditions.

[0044] Characterizing the measured concentration of newly added non-volatile metal ions in the test sample after spiking, i.e., the concentration of metal ions remaining after digestion and detection of the added standard solution in the test sample matrix; molecule The adsorption loss ratio A is the ratio of the adsorption loss concentration to the theoretical concentration. The larger this value is, the more severe the adsorption loss of the non-volatile metal ions is in the container wall during digestion.

[0045] S3. Extract the digestion environmental parameters from the environmental time series data, and correct the volatility compensation coefficient and adsorption compensation coefficient to obtain the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient.

[0046] In some implementations, the temperature correction for the volatility compensation coefficient is determined by statistically analyzing the actual average temperature during the highest temperature holding stage (corresponding to the 220℃ holding stage for 40 minutes in the digestion program of S1). The volatility temperature correction coefficient is then determined based on the deviation between the actual average temperature and the preset highest temperature (220℃) of the standard digestion conditions (i.e., the difference between the actual average temperature and the preset highest temperature).

[0047] For the temperature correction of the adsorption compensation coefficient, the average temperature of the settling and volume adjustment stage after cooling to room temperature after decomposition is extracted from the temperature data. The adsorption temperature correction coefficient is determined based on the deviation between the actual average temperature and the preset volume adjustment temperature of the standard decomposition conditions (e.g., 25°C).

[0048] In practice, if the absolute value of the deviation exceeds a preset temperature deviation threshold (e.g., 5℃), a temperature correction coefficient is calculated using a preset linear correction formula; if the absolute value of the deviation is within the preset threshold range, the temperature correction coefficient is 1, requiring no additional correction, and is expressed as follows: In the formula, The temperature correction slope for the preset linear correction formula is obtained by combining a large number of KrF photoresist raw material matrix matching and spiking experiments with least squares linear regression fitting, and is an empirical coefficient determined by the closed digestion working condition for trace metal detection in the semiconductor industry. For example, 0.01 / ℃, with the dimension of the reciprocal of ℃. Here, represents the actual average temperature, obtained from statistical analysis of environmental time-series data, with dimensions in °C; temperature correction is applied to the volatility compensation coefficient. The preset maximum temperature for standard digestion conditions, the target maximum temperature set for the digestion program, and the temperature correction for the adsorption compensation coefficient. The preset volumetric temperature for standard digestion conditions, in units of °C; The preset temperature deviation threshold; is the temperature correction coefficient, a dimensionless parameter that characterizes the degree of influence of digestion temperature fluctuations on the volatilization loss of metal elements or the adsorption loss of the container.

[0049] From the pressure data, extract the actual average pressure corresponding to the highest temperature insulation stage. Based on the deviation between the actual average pressure and the preset pressure value under standard digestion conditions (i.e., the difference between the two), determine the pressure correction coefficient, expressed as: In the formula, The pressure correction slope is determined through multiple batches of pressure gradient control experiments combined with least squares linear regression fitting, and is an empirical fitting coefficient verified by the trace metal detection conditions in the semiconductor industry, such as 0.4. , where is the actual average pressure during the highest temperature insulation stage, and is the actual digestion pressure obtained from environmental time series data statistics, with the dimension being pressure units; The preset pressure value is the theoretical characteristic pressure of the digestion procedure obtained in advance through standard matrix digestion experiments under standard digestion conditions. The dimension is pressure unit. The deviation between the actual pressure and the preset pressure is represented by a positive deviation when the actual pressure is higher than the preset pressure and a negative deviation when the actual pressure is lower than the preset pressure. P is the pressure correction coefficient, a dimensionless parameter that represents the degree to which pressure fluctuations affect the volatilization loss of volatile metal elements.

[0050] Furthermore, to adapt to the standard correction constraint range of the trace metal detection closed digestion system, an effective range is preset, such as [0.9, 1.1]. This range is determined based on the effective operating boundary of the intelligent closed digestion container, the rationality of the trace metal detection correction range, semiconductor industry quality control standards, and fitting verification through multiple batches of spiking experiments. If the calculated temperature correction coefficient or pressure correction coefficient exceeds the preset effective range, the boundary extreme value is taken as the final correction coefficient; that is, 0.9 is taken when the calculated value is less than 0.9, and 1.1 is taken when the calculated value is greater than 1.1.

[0051] For volatile metal elements, since their volatilization loss rate is affected by both digestion temperature and pressure, the volatility compensation coefficient, volatilization temperature correction coefficient, and pressure correction coefficient under standard digestion conditions are combined to obtain the corrected volatility compensation coefficient under actual conditions. In practice, the volatility compensation coefficient can be combined with the volatilization temperature correction coefficient and the pressure correction coefficient by multiplication. The volatilization temperature correction coefficient reflects the influence of temperature on volatilization, and the pressure correction coefficient reflects the influence of pressure on volatilization. Multiplying the three together yields the actual volatilization loss rate adapted to the current digestion environment.

[0052] For readily adsorbable non-volatile elements, since their adsorption loss rate is mainly affected by digestion temperature, and the effect of pressure on the adsorption effect is negligible, the adsorption compensation coefficient under standard digestion conditions is fused with the adsorption temperature correction coefficient to obtain the corrected adsorption compensation coefficient under actual conditions. In practical implementation, because increasing temperature inhibits physical adsorption, the lower the temperature during the cooling and volume-setting stage, the stronger the adsorption; the adsorption loss rate is inversely related to temperature. Therefore, the adsorption compensation coefficient is divided by the adsorption temperature correction coefficient to achieve fusion. The adsorption temperature correction coefficient reflects the effect of temperature on the container's adsorption effect.

[0053] The correction for the volatility compensation coefficient employs a multiplicative fusion method, where a correction coefficient greater than 1 amplifies the original volatility loss rate. Conversely, the correction for the adsorption compensation coefficient employs a divisional fusion method, where a correction coefficient greater than 1 reduces the original adsorption loss rate. These fusion logics are consistent with the effects of temperature on volatility and adsorption, respectively.

[0054] Specifically, the corrected volatility compensation coefficient and adsorption compensation coefficient characterize the actual loss ratio of metal elements during the digestion process. Under normal operating conditions, this ratio should be between 0 and 1. If either the corrected volatility compensation coefficient or the corrected adsorption compensation coefficient is greater than or equal to the preset effectiveness threshold (usually set to 1), it indicates that the metal element loss ratio exceeds 100%, violating the laws of physical loss. This abnormal situation is usually caused by digestion container sealing failure, excessive adsorption in the digestion container, severe temperature and pressure exceeding the limit, protective agent failure, pretreatment operation errors, or detection instrument drift. In this case, it is directly determined that there is an abnormality in the digestion process, and the subsequent calculation process of metal element concentration is terminated to avoid outputting incorrect detection results.

[0055] S4. Determine the concentration of metal elements in the sample to be tested based on the original measured concentration, blank background concentration, corrected volatility compensation coefficient, and corrected adsorption compensation coefficient.

[0056] In some implementations, background correction is first applied to the original measured concentration to eliminate detection interference caused by reagents, digestion environment, and instrument background. Then, the true concentration of the metal element in the sample is calculated using the corrected compensation coefficient. Specifically, the original measured concentration and the blank background concentration are read, and the difference is used to obtain the pre-corrected concentration after background subtraction. Then, the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient are used to calculate the true concentration of each metal element in the sample using a preset concentration correction formula. The concentration correction calculation formula is as follows: In the formula, Let be the original measured concentration of the i-th metallic element; is the blank background concentration of the i-th metal element; Let represent the overall retention ratio of the i-th metallic element. If the analyte is a volatile element (such as arsenic or mercury), the main source of loss during digestion is volatilization, and the effect of container adsorption can be ignored. , The corrected volatility compensation coefficient for the i-th metal element is used only for concentration correction. If the analyte is a readily adsorbed non-volatile element (such as copper or iron), the main source of loss during digestion is adsorption on the container wall, and the effect of volatilization is negligible. , Let be the corrected adsorption compensation coefficient for the i-th metal element. Only the corrected adsorption compensation coefficient is used for concentration correction. For metal elements that exhibit both volatility and adsorption properties, the direction and logic of the correction for both compensation coefficients conform to their respective physical laws. Therefore, both the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient need to be retained. ; Let be the concentration of the i-th metal element.

[0057] The measured concentration of metal elements after removing the blank background is obtained by eliminating the influence of background contamination from reagents, containers, instruments, etc., which are not related to the sample itself. Characterizes the proportion of metal elements retained during the digestion process after correction for volatilization loss, that is, the proportion of metal elements that are actually not volatilized to the theoretical total amount; Characterized by the proportion of metal elements retained during digestion after adsorption loss correction, i.e., the proportion of metal elements actually not adsorbed by the container relative to the theoretical total; denominator The ratio is the overall retention ratio of metal elements during the digestion process. Dividing the measured concentration after background subtraction by this ratio will restore the true concentration of metal elements in the sample before digestion.

[0058] In addition, the standard concentration thresholds for corresponding metal elements in semiconductor industry standards are read, and the concentration of each metal element is compared with its corresponding standard concentration threshold to obtain the compliance judgment result for each metal element. Specifically, if the concentration exceeds the standard concentration threshold, the metal element is determined to exceed the standard limit and is marked as requiring retesting; if the concentration does not exceed the standard concentration threshold, the metal element is determined to meet the standard requirements. At the same time, the ratio of the concentration of each metal element to its corresponding standard concentration threshold is calculated, and the maximum value among all metal element ratios is extracted as a comprehensive quality index. This maximum value reflects the degree of exceedance of the metal element with the highest pollution risk in the tested sample.

[0059] The sample quality grade is determined based on the range of the comprehensive quality index. The judgment threshold is determined according to semiconductor industry standards, spiked recovery test verification, and production quality control risk classification: when the comprehensive quality index is not greater than 0.85, it is judged as qualified; when the comprehensive quality index is greater than 0.85 but not greater than 1, it is judged as pending re-inspection; when the comprehensive quality index is greater than 1, it is judged as unqualified.

[0060] Based on the compliance judgment results and comprehensive quality index for each metal element, the quality assessment results of the tested samples are output. The quality assessment results of the tested samples are multi-dimensional and traceable comprehensive judgment information, including the compliance judgment results of individual elements, the comprehensive quality index value, the quality grade judgment results, and re-inspection / treatment suggestions. Among them, the compliance judgment results of individual elements clearly indicate the exceedance status and ratio of each metal element, the comprehensive quality index value intuitively reflects the overall pollution level, the quality grade judgment results directly indicate the qualified / re-inspection pending / unqualified status, and the re-inspection / treatment suggestions provide clear follow-up treatment plans for samples of different grades.

[0061] Based on the above technical solution, the sample to be tested is subjected to a sealed digestion pretreatment with integrated environmental sensing to suppress metal evaporation and simultaneously collect temperature and pressure time-series data during the digestion process. Then, by using a standard solution spiked recovery experiment in a real sample matrix, the actual evaporation loss of volatile metals and the adsorption loss on the container wall are accurately quantified, and a volatility and adsorption compensation coefficient matching the sample matrix is ​​obtained. The aforementioned compensation coefficient is then corrected a second time based on the environmental time-series data collected in real time at the digestion site. Finally, the blank background concentration and the corrected compensation coefficient are used to perform background subtraction and back-calculation of the overall recovery rate based on the original concentration measured by the instrument. This eliminates the systematic detection bias introduced by the mismatch of the standard solution matrix and the fluctuation of the digestion environment in the traditional solution, and realizes high-precision quantitative detection of trace metal impurities in KrF photoresist raw materials.

[0062] 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.

[0063] 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.

[0064] In this embodiment of the invention, the metal impurity detection device for KrF photoresist raw materials can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0065] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0066] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for detecting metal impurities in KrF photoresist raw materials, characterized in that, include: The sample to be tested was subjected to sealed digestion pretreatment, and environmental time-series data of the digestion process, the original measured concentration of the sample to be tested, and the blank background concentration of the reagent blank sample were collected. Based on the concentration change of the test sample before and after the addition of the standard solution, the volatility compensation coefficient and adsorption compensation coefficient, which characterize the degree of loss of metal elements in the matrix of the test sample, are determined. The environmental parameters for digestion are extracted from the environmental time series data, and the volatility compensation coefficient and the adsorption compensation coefficient are corrected to obtain the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient. The concentration of metal elements in the sample to be tested is determined based on the original measured concentration, the blank background concentration, the corrected volatility compensation coefficient, and the corrected adsorption compensation coefficient.

2. The method for detecting metal impurities in KrF photoresist raw materials according to claim 1, characterized in that, The volatility compensation coefficient is determined based on the concentration change of the sample before and after the addition of the standard solution, including: Obtain the first standard theoretical concentration of the volatile metal standard solution; the volatile metal standard solution contains a specified concentration of volatile target metal ions; The sample detection concentration of the test sample and the first spike detection concentration of the first spiked sample are obtained; the first spiked sample is obtained by adding the volatile metal standard solution to the test sample; The volatility compensation coefficient is determined based on the first standard theoretical concentration, the first spiked detection concentration, and the sample detection concentration.

3. The method for detecting metal impurities in KrF photoresist raw materials according to claim 1, characterized in that, The adsorption compensation coefficient is determined based on the concentration change of the sample before and after the addition of the standard solution, including: Obtain the second standard theoretical concentration of a non-volatile metal standard solution; the non-volatile metal standard solution contains a specified concentration of non-volatile target metal ions; The sample detection concentration of the test sample and the second spiked detection concentration of the second spiked sample are obtained; the second spiked sample is obtained by adding the non-volatile metal standard solution to the test sample. The adsorption compensation coefficient is determined based on the second standard theoretical concentration, the second spiked detection concentration, and the sample detection concentration.

4. The method for detecting metal impurities in KrF photoresist raw materials according to claim 2 or 3, characterized in that, Before determining the volatility compensation coefficient and adsorption compensation coefficient based on the concentration change of the test sample before and after the addition of the standard solution, the following steps are also included: The organic component characteristics of the sample to be tested are detected, and the historically stored standard matrix component characteristics are read. If the difference between the organic component characteristics and the standard matrix component characteristics is within a preset difference threshold range, then the volatility compensation coefficient and adsorption compensation coefficient corresponding to the historically stored standard matrix component characteristics are directly called. If the difference between the characteristics of the organic component and the characteristics of the standard matrix component exceeds a preset difference threshold, a spiking experiment is performed by adding a standard solution to the sample to be tested to redetermine the volatility compensation coefficient and the adsorption compensation coefficient.

5. The method for detecting metal impurities in KrF photoresist raw materials according to claim 1, characterized in that, Collect environmental time-series data of the digestion process, the original measured concentration of the test sample, and the blank background concentration of the reagent blank sample, including: The sample to be tested is placed in a sealed digestion container that integrates a temperature sensor and a pressure sensor, and a mixed acid digestion solution is added to the sealed digestion container. The preset temperature rise digestion curve is executed, and during the digestion process, temperature data and pressure data are recorded in real time synchronously through the temperature sensor and the pressure sensor to form the environmental time series data; After digestion was completed and the sample solution was brought to a constant volume, the signal intensity of the metal element was determined by inductively coupled plasma mass spectrometry, and the original measured concentration was obtained after converting the signal intensity. Prepare a reagent blank sample that is digested simultaneously with the sample to be tested, and use the same digestion and measurement conditions as the sample to be tested to obtain the blank background concentration.

6. The method for detecting metal impurities in KrF photoresist raw materials according to claim 5, characterized in that, Dissipation environmental parameters are extracted from the environmental time-series data, and the volatility compensation coefficient and the adsorption compensation coefficient are corrected to obtain the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient, including: From the temperature data, the actual average temperature during the highest temperature insulation stage is statistically analyzed, and the volatility temperature correction coefficient is determined based on the deviation between the actual average temperature and the preset highest temperature. From the temperature data, the actual average temperature during the cooling and volume-determining stage is statistically analyzed, and the adsorption temperature correction coefficient is determined based on the deviation between the actual average temperature and the preset volume-determining temperature. Extract the actual average pressure corresponding to the highest temperature insulation stage from the pressure data; The pressure correction coefficient is determined based on the deviation between the actual average pressure and the preset pressure value; The volatility compensation coefficient is fused with the volatility temperature correction coefficient and the pressure correction coefficient to obtain the corrected volatility compensation coefficient. The adsorption compensation coefficient is combined with the adsorption temperature correction coefficient to obtain the corrected adsorption compensation coefficient.

7. The method for detecting metal impurities in KrF photoresist raw materials according to claim 6, characterized in that, Also includes: If the value of either the corrected volatility compensation coefficient or the corrected adsorption compensation coefficient is greater than or equal to the preset effectiveness threshold, the current digestion process is determined to be ineffective, and the subsequent steps for determining the concentration of metal elements are terminated.

8. The method for detecting metal impurities in KrF photoresist raw materials according to claim 1, characterized in that, The concentration of metal elements in the sample to be tested is determined based on the original measured concentration, the blank background concentration, the corrected volatility compensation coefficient, and the corrected adsorption compensation coefficient, including: If the metal element to be tested is a volatile element, the corrected adsorption compensation coefficient is set to zero, and the concentration of the metal element to be tested is determined based on the original measured concentration, the blank background concentration, and the corrected volatility compensation coefficient. If the metal element to be tested is an easily adsorbed non-volatile element, then the corrected volatility compensation coefficient is set to zero, and the concentration of the metal element to be tested is determined based on the original measured concentration, the blank background concentration, and the corrected adsorption compensation coefficient.

9. The method for detecting metal impurities in KrF photoresist raw materials according to claim 8, characterized in that, Also includes: The concentration of each metal element is compared with the corresponding standard concentration threshold to obtain the compliance judgment result of each metal element, and the comprehensive quality index is obtained based on the ratio of the concentration of each metal element to the corresponding standard concentration threshold. Based on the conformity assessment results of each metal element and the comprehensive quality index, the quality assessment results of the sample to be tested are output.

10. A device for detecting metal impurities in KrF photoresist raw materials, characterized in that, include: The pretreatment and acquisition unit is used to perform sealed digestion pretreatment on the sample to be tested, and to acquire environmental time-series data of the digestion process, the original measured concentration of the sample to be tested, and the blank background concentration of the reagent blank sample. The matrix compensation unit is used to determine the volatility compensation coefficient and adsorption compensation coefficient, which characterize the degree of loss of metal elements in the matrix of the test sample, based on the concentration change of the test sample before and after the addition of the standard solution. The dynamic correction unit is used to extract the digestion environmental parameters from the environmental time series data, correct the volatility compensation coefficient and the adsorption compensation coefficient, and obtain the corrected volatility compensation coefficient and the corrected adsorption compensation coefficient. The calibration output unit is used to determine the concentration of metal elements in the sample to be tested based on the original measured concentration, the blank background concentration, the corrected volatility compensation coefficient, and the corrected adsorption compensation coefficient.

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