Metal contamination online monitoring method, system, electronic device, storage medium and computer program product

Abstract

The application relates to a metal contamination online monitoring method, system, electronic equipment, storage medium and computer program product. The method comprises the following steps: acquiring spectrum data in a process cavity in a semiconductor process; calculating an SPE statistic of the spectrum data according to a principal component analysis model; when it is judged that the SPE statistic exceeds a preset control limit, the spectrum data is regarded as a contamination spectrum, and a metal contamination analysis step is entered, which comprises the following steps: acquiring the closest reference spectrum based on the process time stage of the contamination spectrum, and calculating a difference spectrum of the contamination spectrum and the reference spectrum; calculating a contamination index of the metal to be measured based on the original spectrum data and the difference spectrum; matching the contamination index of the metal to be measured with a preset metal characteristic spectrum database; when a matching result meets a preset condition, it is determined that metal contamination exists in the process cavity, and an alarm signal is output. The application can realize early warning and rapid response of metal contamination, thereby improving the process control level and product yield of semiconductor manufacturing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor process monitoring, and in particular to a metal contamination online monitoring method and system based on optical emission spectrum, an electronic device, a storage medium, and a computer program product. BACKGROUND

[0002] In the field of semiconductor manufacturing, plasma processes are widely used in key process steps such as etching, chemical vapor deposition (CVD), ion implantation, etc. However, in the process of plasma, the inner wall of the reaction chamber, electrodes, fixtures and other components may release trace amounts of metal contaminants (such as Fe, Cu, Al, Ni, Cr, etc.) under the continuous bombardment and corrosion of high-temperature and high-activity plasma. Once these metal contaminants are deposited on the wafer surface, they will cause degradation of device electrical performance, gate oxide integrity failure, increased leakage current, and even significant reduction in product yield. Therefore, strict monitoring and control of metal contamination in the plasma process environment is a key link to ensure the yield and reliability of semiconductor manufacturing.

[0003] Currently, the monitoring of metal contamination mainly relies on a variety of off-line detection methods, including:

[0004] 1) Wafer Acceptance Test (WAT): This test is performed after the wafer completes a specific phase or all processes, and the electrical parameters (such as threshold voltage, leakage current, breakdown voltage, etc.) of test structures (such as transistors, capacitors) are measured to indirectly infer possible contamination during the process; however, it has serious lag, and can only find problems after the wafer processing is completed, and cannot intervene at the moment of contamination, which may lead to large quantities of wafer scrap;

[0005] 2) Off-line Particle Monitoring (OPM): Gas samples are periodically extracted from the process chamber or a portable particle detector is used to conduct spot checks on the chamber environment to evaluate the level of particle contamination, which lacks real-time performance;

[0006] 3) Preventive Maintenance (PM): According to the preset time period or cumulative running time, the plasma equipment is stopped and disassembled, and then the wear and corrosion of the inner wall and components of the chamber are manually inspected and cleaned or replaced. This method is costly and will lead to a decrease in equipment utilization, and is a preventive maintenance method that cannot predict and respond to sudden metal contamination caused by component abnormalities between two maintenance periods;

[0007] 4) Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS): This method bombards the surface of the wafer to be tested with a primary ion beam (such as Bi + , Ga + ), causing sputtering of surface atoms, molecules and atomic groups (i.e., generating secondary ions), and then performing time-of-flight mass spectrometry on these secondary ions to determine their elemental or even molecular composition based on their mass-to-charge ratio, thereby determining the types and distribution of metal contaminants on the wafer surface. Although TOF-SIMS has high surface sensitivity and rich chemical information, the analysis process requires offline processing of the sample in a vacuum environment, which is time-consuming and can cause damage to the sample surface, making it unsuitable for real-time monitoring during the process.

[0008] 5) Inductively Coupled Plasma Mass Spectrometry (ICP-MS): This method typically requires sampling the contaminants on the wafer surface, and the sampling solution is atomized into an aerosol and sent into a high-temperature argon plasma generated by a high-frequency radio frequency induction coil by a carrier gas. The sample undergoes evaporation, decomposition, atomization and ionization in the central region of the plasma, forming monovalent positive ions. These ions are then introduced into a mass spectrometer for separation and detection based on their mass-to-charge ratio, allowing accurate determination of the concentration of metal elements. Although ICP-MS has extremely high sensitivity, its sample pretreatment is complex and the analysis period is long, making it unsuitable for real-time online analysis during the process.

[0009] In summary, the above detection methods cannot achieve real-time, online and high-sensitivity monitoring of metal contamination during the plasma process. They either have a serious time lag and can only confirm the contamination after it occurs, or require sampling or equipment disassembly, resulting in reduced production efficiency and increased costs. This lack of monitoring capability makes it difficult for process engineers to obtain early warning information at the moment of contamination, making it difficult to adjust process parameters or shut down for troubleshooting in a timely manner, thereby effectively preventing batch quality accidents caused by sudden contamination.

[0010] Therefore, there is a need for a method that can be integrated with plasma process equipment, without interrupting production, and can monitor the level of metal contamination in the process environment in real time and online while the process is being performed. SUMMARY

[0011] The technical problem to be solved by the present application is to provide a metal contamination online monitoring method and system based on optical emission spectrum, an electronic device, a storage medium and a computer program product, which can monitor the metal contamination level in a process chamber in real time and online, so as to realize early warning and rapid response to metal contamination and fundamentally improve the process control level and product yield of semiconductor manufacturing.

[0012] According to a first aspect of the embodiments of the present application, a metal contamination online monitoring method is provided, comprising:

[0013] acquiring spectrum data in a process chamber in a semiconductor process;

[0014] calculating an SPE statistic of the spectrum data according to a principal component analysis model, and when it is judged that the SPE statistic exceeds a preset control limit, regarding the spectrum data as a contamination spectrum and entering a metal contamination analysis step, the metal contamination analysis step comprising: acquiring a closest reference spectrum based on a process time stage of the contamination spectrum, and calculating a difference spectrum of the contamination spectrum and the reference spectrum, wherein the reference spectrum is obtained from original spectrum data collected by running the semiconductor process in a clean process chamber for multiple times;

[0015] calculating a contamination index of a to-be-tested metal based on the original spectrum data and the difference spectrum;

[0016] matching the contamination index of the to-be-tested metal with a preset metal characteristic spectrum database, and when a matching result meets a preset condition, determining that there is metal contamination in the process chamber and outputting an alarm signal.

[0017] In an embodiment, the closest reference spectrum is acquired based on the process time stage of the contamination spectrum, comprising: acquiring the original spectrum data;

[0018] filtering a spectrum subset meeting a target time stage from the original spectrum data based on a preset time stage criterion;

[0019] calculating an average spectrum and a variance spectrum of the spectrum subset, and performing standardization processing to generate a reference spectrum representing the spectral characteristics of the target time stage as the closest reference spectrum.

[0020] In an embodiment, the establishment step of the preset metal characteristic spectrum database is:

[0021] determining characteristic spectral lines of at least two to-be-tested metals based on the semiconductor process and the original spectrum data, acquiring reference spectra corresponding to different process time stages in the semiconductor process, and calculating a reference index CL of each to-be-tested metal corresponding to the characteristic spectral line in each reference spectrum. mThe process time stage and the benchmark index CL for each metal being tested will be used. m Establish corresponding relationships to form a metal characteristic spectrum database;

[0022] Specifically, the contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets preset conditions, it is determined that metal contamination exists in the process chamber, including:

[0023] Based on the process time stage of the pollution spectrum, the reference index CL of the metal to be tested is obtained from the preset metal characteristic spectrum database. m ;

[0024] The pollution index CL of the metal to be tested is compared with the benchmark index CL corresponding to the metal to be tested. m If the comparison result meets the preset condition, it is determined that metal contamination exists in the process chamber. The preset condition is that CL is greater than CL within a preset time period. m .

[0025] In one embodiment, calculating the pollution index of the metal to be tested based on the original spectral data and the difference spectrum includes:

[0026] Obtain the reference intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas, wherein the first characteristic peak and the second characteristic peak of the inert gas are selected according to the semiconductor process, and the reference intensity ratio is the spectral intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas in the original spectral data collected in the clean process chamber;

[0027] Based on the difference spectrum, the ratio of the spectral intensity of the first characteristic peak of the inert gas to the second characteristic peak of the inert gas is calculated and used as the current ratio.

[0028] When the current ratio is compared with the reference intensity ratio and the difference between the two is within a preset range, the ratio of the characteristic wavelength spectral intensity of the pollutant to be tested in the difference spectrum to the spectral intensity of the first characteristic peak of the inert gas in the original spectral data is calculated as the pollution index of the metal to be tested.

[0029] In one embodiment, the online monitoring method for metal pollution further includes:

[0030] When the current ratio is compared with the reference intensity ratio and the difference between the two exceeds a preset range, the reference spectrum is calibrated based on the difference spectrum to generate a background spectrum;

[0031] Based on the difference spectrum, the background spectrum is subtracted from the characteristic wavelength spectrum of the metal to be tested and the spectral lines corresponding to the first characteristic peak of the inert gas, respectively, and the respective background-removed peak areas are calculated and denoted as I'1 and I'2.

[0032] The ratio of the area of ​​the background-removed peak corresponding to the metal to be tested to the area of ​​the background-removed peak corresponding to the first characteristic peak of the inert gas is calculated as the pollution index of the metal to be tested.

[0033] In one embodiment, the steps for establishing the principal component analysis model include: performing N semiconductor processes within a cleanroom, acquiring raw spectral data using an OES sensor, wherein the raw spectral data is acquired at M time points during each process, and each time point covers a full-band spectrum of P wavelengths; constructing a two-dimensional data matrix from the raw spectral data, wherein the dimension of the two-dimensional data matrix is ​​(N × M) rows × P columns; standardizing the two-dimensional data matrix column-wise to obtain a reference spectral matrix; calculating its covariance matrix based on the reference spectral matrix, and solving for the eigenvalues ​​and eigenvectors of the covariance matrix, defining the eigenvectors as principal component directions;

[0034] The step of calculating the SPE statistic of the spectral data based on the principal component analysis model includes:

[0035] The spectral data is projected onto the principal component direction to calculate the SPE statistic.

[0036] In one embodiment, when it is determined that the SPE statistic exceeds a preset control limit, the spectral data is used as the pollution spectrum and the process proceeds to the metal pollution analysis step. The process further includes: when it is determined that all the SPE statistics within a certain time period exceed the preset control limit, the average value of the corresponding spectral data within the certain time period is used as the pollution spectrum and the process proceeds to the metal pollution analysis step.

[0037] According to a second aspect of the embodiments of this application, an online monitoring system for metal pollution is provided, which includes a spectral acquisition module, a first processing module, and a second processing module connected together, wherein...

[0038] The spectral acquisition module is used to acquire spectral data within the process chamber in real time.

[0039] The first processing module is used to calculate the SPE statistic of the spectral data according to the principal component analysis model, and when it is determined that the SPE statistic exceeds the preset control limit, output a trigger command to enter the metal pollution analysis step.

[0040] The second processing module is used to execute the metal contamination analysis step when the trigger command is received. The metal contamination analysis step includes: obtaining the closest reference spectrum based on the process time stage of the contamination spectrum, and calculating the difference spectrum between the contamination spectrum and the reference spectrum, wherein the reference spectrum is obtained from the raw spectral data collected by running the semiconductor process multiple times in the clean process chamber.

[0041] Based on the original spectral data and the difference spectrum, the pollution index CL of the metal to be tested is calculated;

[0042] The contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets the preset conditions, it is determined that there is metal contamination in the process chamber, and an alarm signal is output.

[0043] In one embodiment, the online monitoring system further includes a data storage module connected to the first processing module and the second processing module. The data storage module is used to store the principal component analysis model, the preset metal characteristic spectrum database, and the reference spectrum.

[0044] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising a memory and a processor, the memory being configured to store a computer program executable by the processor; the processor being configured to execute the computer program in the memory to implement the method as described in any of the preceding claims.

[0045] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the executable computer program in the storage medium is executed by a processor, it is capable of implementing the method described in any of the preceding claims.

[0046] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any of the preceding claims.

[0047] Compared with the prior art, the beneficial effects of this application are as follows:

[0048] This application first performs principal component analysis on the real-time acquired spectral data. Only when the SPE statistic exceeds the preset control limit is the metal contamination analysis step triggered. This eliminates the need to calculate the metal contamination analysis index for all real-time spectral data, enabling real-time, online monitoring of metal contamination levels within the process chamber. This avoids performing metal contamination level analysis on every real-time spectrum, improving analytical efficiency and accuracy. Furthermore, the metal contamination analysis step employs differential spectral analysis, and the reference spectrum used for differential spectral analysis is selected to be closest to the process time stage of the contamination spectrum. Through a time alignment mechanism, only the original spectral data (i.e., historical data) corresponding to the process time stage in which the contamination spectrum occurred is analyzed. This eliminates the need for global processing of the original spectral data, improving analytical efficiency and accuracy. It enables early warning and rapid response to metal contamination, fundamentally improving the process control level and product yield in semiconductor manufacturing. Attached Figure Description

[0049] Figure 1This is a flowchart illustrating an online monitoring method for metal pollution according to an exemplary embodiment;

[0050] Figure 2 This is a flowchart illustrating a metal contamination analysis according to an exemplary embodiment;

[0051] Figure 3 This is a block diagram illustrating an online monitoring system for metal pollution according to an exemplary embodiment;

[0052] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0053] Unless otherwise defined, the technical or scientific terms used in this specification and claims shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. Specific embodiments of the invention will be described below with reference to the accompanying drawings. It should be noted that, in order to provide a concise description, this specification cannot provide a detailed description of all features of the actual embodiments. Without departing from the spirit and scope of the invention, those skilled in the art can make modifications and substitutions to the embodiments of the invention, and the resulting embodiments are also within the protection scope of the invention.

[0054] In related technologies, the monitoring of metal contamination during plasma processes suffers from severe time lag, and can only be confirmed retrospectively after contamination occurs; or the production process needs to be interrupted for sampling or equipment dismantling, resulting in reduced production efficiency and increased costs.

[0055] To address the aforementioned technical problems, this application proposes a method, device, electronic equipment, and storage medium for online monitoring of metal contamination. This method can monitor the level of metal contamination in process chambers in real time and online, enabling early warning and rapid response to metal contamination, thereby fundamentally improving the process control level and product yield in semiconductor manufacturing.

[0056] One embodiment of this application provides a method for online monitoring of metal contamination. This method can be applied to electronic devices such as control computers in OES equipment and central controllers in chip manufacturing processes. Please refer to... Figure 1 The online monitoring method for metal pollution may include the following steps 201 to 203:

[0057] Step 201: Obtain spectral data within the process cavity of the semiconductor process; specifically, optical emission spectroscopy, i.e., an OES sensor, can be used to collect spectral signals within the process cavity in real time. The OES sensor can be embedded in the monitoring window of the process cavity. The semiconductor process can be a plasma process (such as etching, chemical vapor deposition, or resist removal), and is not limited to this step.

[0058] Step 202: Calculate the SPE statistic of the spectral data according to the principal component analysis model (i.e., PCA model). When it is determined that the SPE statistic exceeds the preset control limit, the spectral data is used as the pollution spectrum and the process proceeds to the metal pollution analysis step, i.e., step 203. When it is determined that the SPE statistic does not exceed the preset control limit, continue to collect spectral data in real time and calculate the SPE statistic.

[0059] Step 203, the metal contamination analysis includes the following steps 301 to 303:

[0060] Step 301: Based on the process time stage of the contamination spectrum, obtain the closest reference spectrum, and calculate the difference spectrum between the contamination spectrum and the reference spectrum, wherein the reference spectrum is obtained from the original spectral data collected by running the semiconductor process multiple times in a clean process chamber;

[0061] Step 302: Calculate the pollution index of the metal to be tested based on the original spectral data and the difference spectrum;

[0062] Step 303: Match the contamination index of the metal to be tested with a preset metal characteristic spectrum database. When the matching result meets the preset conditions, it is determined that there is metal contamination in the process chamber, and an alarm signal is output.

[0063] This embodiment uses real-time principal component analysis (PCA) to analyze spectral data acquired during semiconductor manufacturing processes. Based on the real-time calculated SPE statistic, a preliminary assessment of metal contamination is made. The metal contamination analysis step is triggered only when the SPE statistic exceeds a preset control limit. This eliminates the need to calculate the metal contamination index for all real-time spectral data, enabling real-time, online monitoring of metal contamination levels within the process chamber. This avoids performing metal contamination level analysis on every real-time spectrum, improving analysis efficiency and accuracy. Furthermore, the metal contamination analysis step employs differential spectral analysis, using a reference spectrum closest to the process time stage of the contamination spectrum. Through a time alignment mechanism, only the original spectral data (i.e., historical data) corresponding to the process time stage where the contamination spectrum occurred is analyzed, eliminating the need for global processing of the original spectral data. This improves analysis efficiency and accuracy, enabling early warning and rapid response to metal contamination, fundamentally enhancing process control and product yield in semiconductor manufacturing.

[0064] In one embodiment, step 201, the sampling process for acquiring spectral data, is as follows: For real-time acquired spectral signals, a sampling window of 5 is set. For example, if 10 frames are continuously acquired, then 1-5 constitute one sampling window, and 2-6 constitute another. The spectral signal within each sampling window is calculated in real time, that is, the average spectrum of the 5 frames within the sampling window is calculated as the current real-time spectrum. This embodiment sets a sampling window to reduce the processing load of spectral data. The sampling window is not limited to 5 frames; it can also be 6, 8, or 10 frames, depending on the semiconductor process requirements.

[0065] To facilitate real-time monitoring, in one embodiment, the aforementioned principal component analysis model and reference spectrum are both formed based on pre-collected historical data, which can be pre-stored in the control system of the OES equipment or the local control system of the semiconductor process. Specifically, N semiconductor processes are performed in the cleanroom, and raw spectral data is collected using OES sensors. This raw spectral data is collected at M time points during each process, with each time point covering the full-band spectrum of P wavelengths. The raw spectral data is constructed into a two-dimensional data matrix, where the dimension of the two-dimensional data matrix is ​​(N × M) rows × P columns. The two-dimensional data matrix is ​​standardized column-wise. This standardization process may include calculating the mean μ and standard deviation σ of all elements in each column of the two-dimensional data matrix, and subtracting the mean μ from each raw value in that column and dividing by the standard deviation σ to obtain a standard matrix. The wavelength variables in the standard matrix are then centered and scaled to obtain the reference spectral matrix. Based on the reference spectral matrix, its covariance matrix is ​​calculated, and the eigenvalues ​​and eigenvectors of the covariance matrix are solved. The eigenvectors are defined as the principal component directions. The original spectral data (i.e., historical data) is projected along the principal component directions to obtain the dimensionality-reduced score matrix, and its T² and SPE statistics are calculated. The formula for T² is as follows: Where x is the spectral vector in the original spectral data, P is the principal component loading matrix, and Λ is the eigenvalue diagonal matrix of the principal components. The SPE calculation formula is as follows: Where x is the spectral vector in the spectral data, This is the spectral vector reconstructed through the principal component directions. The aforementioned baseline spectral matrix, along with its corresponding T² and SPE statistics, are used as key parameters of the principal component analysis model, available for use by online monitoring methods. The T² and SPE statistics calculated from the original spectral data, and then determined according to the semiconductor process formulation, can be used to determine the critical values ​​of the T² and SPE statistics, which serve as the preset control limits for the process, enabling early warning analysis of metal contamination.

[0066] Specifically, step 202 above may include projecting the real-time acquired spectral data (i.e., the spectral data corresponding to the current sampling window) onto the principal component direction determined by the aforementioned benchmark spectral matrix to obtain a dimension-reduced score matrix. The T² and SPE statistics are then calculated according to the aforementioned formulas, and the calculated T² and SPE statistics are compared with preset control limits. If the real-time calculated SPE statistics do not exceed the preset control limits, the current spectral data can be considered normal data, and real-time spectral acquisition can continue for PCA analysis. If the real-time calculated SPE statistics exceed the preset control limits, the current spectral data is considered a contamination spectrum, and the process proceeds to the metal contamination analysis step for further analysis to determine whether it is indeed metal contamination.

[0067] Furthermore, in step 202 above, when it is determined that all SPE statistics exceed the preset control limits within a certain time period, the average value of the corresponding spectral data within that time period is taken as the contamination spectrum, and the process proceeds to the metal contamination analysis step. This involves calculating the similarity between the real-time acquired spectral data and multiple original spectra for the corresponding process time period, comparing them with the aforementioned benchmark spectral library to find the closest benchmark spectrum for that time period, and performing numerical averaging and window smoothing on the spectral data acquired within a certain time period (e.g., spectral data corresponding to 5 sampling windows), using the average value as the contamination spectrum to reduce noise interference.

[0068] To better monitor and analyze in real time, in one embodiment, the above-mentioned raw spectral data is aligned and divided based on the time dimension to establish a benchmark spectral library, specifically including the following steps:

[0069] S402, Obtain the above-mentioned raw spectral data;

[0070] S404, based on a preset time stage criterion, selects a subset of spectra that meet the target time stage from the original spectral data; specifically, the preset time stage criterion can be set according to the semiconductor process, such as plasma etching process, which can be divided into the initial, middle and late stages of main etching. For example, the spectral data corresponding to the initial stage of main etching in multiple processes can be selected to form an initial spectral subset.

[0071] S406, calculate the average spectrum and variance spectrum of the spectral subset and perform standardization processing to generate a reference spectrum representing the spectral characteristics of the target time period, that is, to form a reference spectral library divided by the time dimension. For example, the average spectrum and variance spectrum of the above initial spectral data are calculated and standardized. The standardization processing includes, but is not limited to, vector normalization, standard normal variable transformation, or maximum value normalization to form the initial reference spectrum.

[0072] Based on this benchmark spectral library, in real-time online monitoring of metal contamination, the closest spectrum can be found at the process time stage where the contamination spectrum is generated by the semiconductor process. This combines spectral analysis with precise process timing, improving the traditional monitoring system from offline analysis of metal contamination to online detection and predictive monitoring of "when metal contamination occurred". This significantly improves the yield control capability, production efficiency and automation level of semiconductor manufacturing.

[0073] Specifically, in step 301 above, based on the process time stage of the contamination spectrum, the closest reference spectrum is obtained. Specifically, according to the current process time stage of the contamination spectrum, the closest reference spectrum is searched from the aforementioned reference spectrum library. If step 202 detects that the SPE statistic of the real-time spectrum during the main etching period exceeds a preset control limit, then step 301 obtains the reference spectrum of the main etching period as the background spectrum, and performs difference spectral analysis with the real-time spectrum. This embodiment compares the real-time spectrum with a reference spectrum library aligned by time stage, which can immediately determine which process time stage's reference spectrum is most similar to the currently acquired contamination spectrum. When metal contamination is detected, the process time stage can be output synchronously, i.e., the alarm signal can be displayed as "metal contamination occurs in the middle of the main etching." Thus, when contamination has just occurred and has not yet caused irreversible damage to the entire batch of wafers, an alarm signal can be immediately identified and triggered, achieving early monitoring, diagnosis, and intervention.

[0074] To better monitor the process chamber environment online, in one embodiment, the process equipment and metal materials used in the semiconductor process are considered as contamination sources, and a preset metal characteristic spectrum database is used for detection and tracking. Specifically, the establishment of the preset metal characteristic spectrum database may include the following steps S501-S503:

[0075] Step S501: Based on the semiconductor process and the raw spectral data collected above, determine the characteristic spectral lines of at least two metals to be tested; for example, determine the potential metal contamination elements that need to be monitored based on the materials used in the semiconductor process (such as Al, Ti, W, Cu, Fe, Ni, etc.); if the main components are aluminum alloy electrodes and stainless steel cavity walls, then the monitored metals are Al and Cu; specifically, select one or more atomic or ion characteristic emission spectral lines with high sensitivity and low interference for each metal to be tested (such as Al and Cu) as the monitoring targets.

[0076] Step S502: Obtain the reference spectrum corresponding to different process time stages in the semiconductor process. The reference spectrum is obtained from the reference spectrum library obtained in step S403 above.

[0077] Step S503: Calculate the reference index CL corresponding to the characteristic spectral line of each metal to be tested in each reference spectrum. mThe process time stage and the benchmark index CL for each metal being tested will be used. m Establish corresponding relationships to form a metal characteristic spectrum database.

[0078] Specifically, the reference index CL corresponds to the characteristic spectral lines of each metal being tested. m The calculation process is as follows:

[0079] In the aforementioned reference spectral library, the reference spectrum corresponding to each process time point t is searched. For example, if the original spectral data contains 100 process data points (i.e., N is 100), and there are 100 frames corresponding to time point t, a background index Cl can be calculated for each spectrum in the 100 frames. a Value, for 100 Cl a The average value is calculated as Mean, and the standard deviation is calculated as σ. Mean and σ are used as the benchmark index CL. m Storage. Wherein, Cl a The value is the ratio of the intensity of the characteristic emission line of the metal element to be tested to the intensity of the characteristic emission line of the inert gas selected according to the semiconductor process.

[0080] To eliminate the impact of overall spectral intensity variations caused by fluctuations in process power and pressure, a stable background gas spectral line is first identified. The specific selection depends on the semiconductor process, and inert gases are typically used as background gases. These gases do not participate in reactions, have stable chemical properties, and their spectra are only affected by the plasma physical state (such as power and pressure) and are not interfered with by metal contamination. Nitrogen is generally the inert gas selected based on the semiconductor process. To improve monitoring accuracy, in this embodiment, during the acquisition of raw spectral data, two different spectral lines of an inert gas are selected as dual references. That is, when establishing the reference spectral library, the following steps are also included:

[0081] Step S403: Determine two different spectral lines of the inert gas according to the semiconductor process, such as two wavelengths of nitrogen as reference characteristic emission spectral lines, such as the first characteristic peak of the inert gas being 750.4 nm and the second characteristic peak of the inert gas being 575.5 nm, and use these as background gases for monitoring.

[0082] Step S405: Based on the raw spectral data obtained in step S402, calculate the spectral intensity ratio corresponding to the two reference characteristic emission lines within each process time stage, denoted as the reference intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas. Each process time stage can be consistent with the preset time stage in step S406. Specifically, for the reference spectrum corresponding to each process time stage, calculate the reference intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas to obtain the alignment data between the reference intensity ratio and the time node. This data can be stored in the reference spectrum library for easy retrieval and searching during real-time online monitoring, simplifying the processing time of on-site process data. In the dual-reference system, the reference index CL corresponding to the characteristic spectral line of each metal to be tested... m The benchmark index CL m The spectral intensity of the first characteristic peak corresponding to the inert gas is obtained by calculation.

[0083] Another embodiment of this application provides a method for online monitoring of metal pollution, see [link to application]. Figure 2 As shown, the metal contamination analysis in step S203 above may include:

[0084] Step 301: Based on the process time stage of the contamination spectrum, find the corresponding reference spectrum from the aforementioned reference spectral library, calculate the difference spectrum between the contamination spectrum and the reference spectrum, and find the reference intensity ratio between the first characteristic peak and the second characteristic peak of the inert gas; and find the reference index CL of the corresponding metal to be tested from the aforementioned metal characteristic spectral database. m The reference spectral library can be established by steps S402-S406, and the metal characteristic spectral database can be obtained by steps S501-S503.

[0085] Step 302: Based on the difference spectrum from step 301, calculate the pollution index CL of the metal to be tested, specifically including:

[0086] Step 3021: Based on the difference spectrum, calculate the ratio of the spectral intensity of the first characteristic peak of the inert gas to the second characteristic peak of the inert gas, and use it as the current ratio.

[0087] Step 3022: Calculate the difference between the current ratio and the reference intensity ratio found in step 301, and compare it with a preset range. If the difference is within the preset range, calculate the ratio of the characteristic wavelength spectral intensity of the pollutant to be tested in the difference spectrum to the spectral intensity of the first characteristic peak of the inert gas in the original spectral data, and use this ratio as the pollution index CL of the metal to be tested. Specifically, the spectral intensity of the first characteristic peak of the inert gas is selected for calculation based on the reference index CL calculated from the reference spectral library. m The selected characteristic peaks are chosen to ensure consistency between the two comparison objects. Understandably, if the reference index CL is calculated in the reference spectral library... mSince the selected characteristic peak is the second characteristic peak of inert gas, when calculating the pollution index CL in this step, it is also necessary to select the spectral intensity of the second characteristic peak of inert gas in the original spectral data for calculation.

[0088] If the difference between the two exceeds a preset range, the reference spectrum is calibrated based on the difference spectrum to generate a background spectrum; based on the difference spectrum, the background spectrum is subtracted from the characteristic wavelength spectrum of the metal to be tested and the spectrum of the first characteristic peak of the inert gas, respectively, and the respective de-background peak areas are calculated and denoted as I'1 and I'2; the ratio of the de-background peak area corresponding to the metal to be tested to the de-background peak area corresponding to the first characteristic peak of the inert gas is calculated as the pollution index CL of the metal to be tested;

[0089] Step 303: Compare the pollution index CL of the metal to be tested with the corresponding benchmark index CL of the metal to be tested. m If the comparison result meets a preset condition, it is determined that metal contamination exists in the process chamber, and an alarm signal is output. The preset condition is that CL is greater than CL within a preset time period. m The preset time period can be 5-10 sampling windows, and each sampling window can include 5 frames of data.

[0090] In summary, the technical solution provided in this application has the following advantages: By first performing principal component analysis on the real-time acquired spectral data, and then triggering and entering the metal contamination analysis step only when the calculated SPE statistic exceeds the preset control limit, it is not necessary to calculate the metal contamination analysis index for all real-time spectral data. This allows for real-time, online monitoring of the metal contamination level within the process chamber, avoiding the need to perform metal contamination level analysis on all real-time spectra, thus improving analysis efficiency and accuracy. In addition, the metal contamination analysis step uses differential spectral analysis, and the reference spectrum used for differential spectral analysis is selected to be closest to the process time stage of the contamination spectrum. Through the time alignment mechanism, only the original spectral data (i.e., historical data) corresponding to the process time stage in which the contamination spectrum occurred is analyzed, eliminating the need for global processing of the original spectral data, thus improving analysis efficiency and accuracy. This enables early warning and rapid response to metal contamination, fundamentally improving the process control level and product yield in semiconductor manufacturing.

[0091] Another exemplary embodiment of this application also provides an online monitoring system for metal pollution. For example... Figure 3 As shown, in this embodiment, the online monitoring system for metal pollution includes a connected spectral acquisition module 100, a first processing module 200, and a second processing module 300, wherein...

[0092] The spectral acquisition module 100 is used to acquire spectral data within the process chamber in real time.

[0093] The first processing module 200 is used to calculate the SPE statistic of the spectral data according to the principal component analysis model, and when it is determined that the SPE statistic exceeds the preset control limit, output a trigger command to enter the metal pollution analysis step.

[0094] The second processing module 300 is used to execute the metal contamination analysis step when the trigger command is received. The metal contamination analysis step includes: obtaining the closest reference spectrum based on the process time stage of the contamination spectrum, and calculating the difference spectrum between the contamination spectrum and the reference spectrum, wherein the reference spectrum is obtained from the raw spectral data collected by running the semiconductor process multiple times in the clean process chamber.

[0095] Based on the original spectral data and the difference spectrum, the pollution index CL of the metal to be tested is calculated;

[0096] The contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets the preset conditions, it is determined that there is metal contamination in the process chamber, and an alarm signal is output.

[0097] In this embodiment of the online metal contamination monitoring system, the first processing module 200 acts as a trigger for the second processing module 300. The second processing module is triggered only when the SPE statistic exceeds the preset control limit. This eliminates the need for global processing of the original spectral data, improving analysis efficiency and accuracy. It enables early warning and rapid response to metal contamination, fundamentally improving the process control level and product yield in semiconductor manufacturing.

[0098] In one embodiment, the online monitoring system for metal pollution further includes:

[0099] A data storage module 400 is connected to the first processing module and the second processing module. The data storage module is used to store the principal component analysis model, the preset metal characteristic spectrum database, and the reference spectrum.

[0100] Embodiments of this application also propose an electronic device, including a processor and a memory; the memory is used to store a computer program executable by the processor; the processor is used to execute the computer program in the memory to implement the online monitoring method for metal pollution described in any of the above embodiments.

[0101] Embodiments of this application also propose a computer-readable storage medium that, when an executable computer program in the storage medium is executed by a processor, enables the implementation of the online metal pollution monitoring method described in any of the above embodiments.

[0102] Embodiments of this application also propose a computer program product, including a computer program that, when executed by a processor, implements a driving method for the universal serial bus interface of any of the above embodiments.

[0103] Regarding the apparatus in the above embodiments, the specific manner in which the processor performs the operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0104] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment. For example, the electronic device (i.e., device 900) may be provided as a server. (Refer to...) Figure 4 The device 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions, such as application programs, that can be executed by the processing component 922. The application programs stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 922 is configured to execute instructions to perform the aforementioned method for online monitoring of metal contamination.

[0105] Device 900 may also include a power supply component 926 configured to perform power management of device 900, a wired or wireless network interface 950 configured to connect device 900 to a network, and an input / output (I / O) interface 958. Device 900 can operate on an operating system stored in memory 932, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or similar.

[0106] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 932 including instructions, which can be executed by a processing component 922 of the device 900 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0107] In this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "multiple" refers to two or more unless otherwise expressly defined.

[0108] The above description of the embodiments is intended to enable those skilled in the art to understand and apply this application. It will be apparent to those skilled in the art that various modifications can be easily made to these embodiments, and the general principles described herein can be applied to other embodiments without creative effort. Therefore, this application is not limited to the embodiments described herein, and any improvements and modifications made by those skilled in the art based on the disclosure of this application without departing from the scope and spirit of this application are within the scope of this application.

Claims

1. A method for online monitoring of metal pollution, characterized in that, include: OES sensors are used to acquire spectral signals in the process cavity in real time to obtain spectral data in the process cavity during semiconductor processing. The SPE statistic of the spectral data is calculated based on the principal component analysis model. When the SPE statistic exceeds a preset control limit, the spectral data is used as a contamination spectrum and proceeds to the metal contamination analysis step, which includes: Obtain raw spectral data collected by an OES sensor after running the semiconductor process multiple times within a cleanroom. Based on the process time stage of the pollution spectrum, the closest reference spectrum is obtained, and the difference spectrum between the pollution spectrum and the reference spectrum is calculated, wherein the reference spectrum is obtained from the original spectral data; Based on the original spectral data and the difference spectrum, the pollution index of the metal to be tested is calculated, including: obtaining a reference intensity ratio of the first characteristic peak of the inert gas to the second characteristic peak of the inert gas, wherein the first characteristic peak of the inert gas and the second characteristic peak of the inert gas are selected according to the semiconductor process, and the reference intensity ratio is the spectral intensity ratio of the first characteristic peak of the inert gas and the second characteristic peak of the inert gas in the original spectral data; calculating the spectral intensity ratio of the first characteristic peak of the inert gas and the second characteristic peak of the inert gas based on the difference spectrum, as the current ratio; when the current ratio is compared with the reference intensity ratio and the difference between the two is within a preset range, calculating the ratio of the characteristic wavelength spectral intensity of the pollutant to be tested in the difference spectrum to the spectral intensity of the first characteristic peak of the inert gas in the original spectral data, as the pollution index of the metal to be tested; The contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets preset conditions, it is determined that metal contamination exists in the process chamber, and an alarm signal is output. The steps for establishing the preset metal characteristic spectrum database are as follows: acquiring raw spectral data; determining characteristic spectral lines of at least two metals to be tested based on the semiconductor process and the raw spectral data; acquiring reference spectra corresponding to different process time stages in the semiconductor process; and calculating the reference index CL corresponding to the characteristic spectral line of each metal to be tested in each reference spectrum. m The process time stage and the benchmark index CL for each metal being tested will be used. m Establish corresponding relationships to form a metal characteristic spectrum database.

2. The online monitoring method for metal pollution as described in claim 1, characterized in that, The process time stage based on the pollution spectrum, obtaining the closest reference spectrum, includes: Obtain the raw spectral data; Based on preset time stage criteria, a subset of spectra that meet the target time stage is selected from the original spectral data; The mean spectrum and variance spectrum of the spectral subset are calculated and standardized to generate a reference spectrum that represents the spectral characteristics of the target time period, which is the closest reference spectrum.

3. The online monitoring method for metal pollution as described in claim 2, characterized in that, The contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets preset conditions, it is determined that metal contamination exists in the process chamber, including: Based on the process time stage of the pollution spectrum, the reference index CL of the metal to be tested is obtained from the preset metal characteristic spectrum database. m ; The pollution index CL of the metal to be tested is compared with the benchmark index CL corresponding to the metal to be tested. m If the comparison result meets the preset condition, it is determined that metal contamination exists in the process chamber. The preset condition is that CL is greater than CL within a preset time period. m .

4. The online monitoring method for metal pollution as described in claim 1, characterized in that, Also includes: When the current ratio is compared with the reference intensity ratio and the difference between the two exceeds a preset range, the reference spectrum is calibrated based on the difference spectrum to generate a background spectrum; Based on the difference spectrum, the background spectrum is subtracted from the characteristic wavelength spectrum of the metal to be tested and the spectral line of the first characteristic peak of the inert gas, respectively, and the respective background-removed peak areas are calculated and denoted as I'1 and I'2. The ratio of the area of ​​the background-removed peak corresponding to the metal to be tested to the area of ​​the background-removed peak corresponding to the first characteristic peak of the inert gas is calculated as the pollution index of the metal to be tested.

5. The online monitoring method for metal pollution as described in claim 1, characterized in that, The steps for establishing the principal component analysis model include: performing N semiconductor processes within a cleanroom, acquiring raw spectral data using an OES sensor, wherein the raw spectral data is acquired at M time points during each process, and each time point covers a full-band spectrum of P wavelengths; constructing a two-dimensional data matrix from the raw spectral data, wherein the dimension of the two-dimensional data matrix is ​​(N × M) rows × P columns; standardizing the two-dimensional data matrix column-wise to obtain a reference spectral matrix; calculating its covariance matrix based on the reference spectral matrix, and solving for the eigenvalues ​​and eigenvectors of the covariance matrix, defining the eigenvectors as principal component directions; The step of calculating the SPE statistic of the spectral data based on the principal component analysis model includes: The spectral data is projected onto the principal component direction to calculate the SPE statistic.

6. The online monitoring method for metal pollution as described in claim 1, characterized in that, When it is determined that the SPE statistic exceeds the preset control limit, the spectral data is used as the pollution spectrum and the metal pollution analysis step is initiated. The method further includes: when it is determined that all the SPE statistics exceed the preset control limit within a certain time period, the average value of the corresponding spectral data within the certain time period is used as the pollution spectrum and the metal pollution analysis step is initiated.

7. An online monitoring system for metal pollution, characterized in that, It includes a connected spectral acquisition module, a first processing module, and a second processing module, wherein, The spectral acquisition module uses an OES sensor to acquire spectral signals in the process chamber in real time, which is used to obtain spectral data in the process chamber in real time. The first processing module is used to calculate the SPE statistic of the spectral data according to the principal component analysis model, and when it is determined that the SPE statistic exceeds the preset control limit, output a trigger command to enter the metal pollution analysis step. The second processing module is used to execute the metal contamination analysis step when the trigger command is received. The metal contamination analysis step includes: acquiring raw spectral data collected by an OES sensor after running the semiconductor process multiple times in a clean process chamber; acquiring the closest reference spectrum based on the process time stage of the contamination spectrum; and calculating the difference spectrum between the contamination spectrum and the reference spectrum, wherein the reference spectrum is obtained from the raw spectral data collected after running the semiconductor process multiple times in a clean process chamber. Based on the original spectral data and the difference spectrum, the pollution index CL of the metal to be tested is calculated, including: obtaining a reference intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas, wherein the first characteristic peak and the second characteristic peak of the inert gas are selected according to the semiconductor process, and the reference intensity ratio is the ratio of the spectral intensity of the first characteristic peak and the second characteristic peak of the inert gas in the original spectral data collected in the clean process chamber; calculating the spectral intensity ratio of the first characteristic peak and the second characteristic peak of the inert gas based on the difference spectrum, as the current ratio; when the current ratio is compared with the reference intensity ratio and the difference between the two is within a preset range, calculating the ratio of the characteristic wavelength spectral intensity of the pollutant to be tested in the difference spectrum to the spectral intensity of the first characteristic peak of the inert gas in the original spectral data, as the pollution index of the metal to be tested; The contamination index of the metal to be tested is matched with a preset metal characteristic spectrum database. When the matching result meets preset conditions, it is determined that metal contamination exists in the process chamber, and an alarm signal is output. The steps for establishing the preset metal characteristic spectrum database are as follows: acquiring raw spectral data; determining characteristic spectral lines of at least two metals to be tested based on the semiconductor process and the raw spectral data; acquiring reference spectra corresponding to different process time stages in the semiconductor process; and calculating the reference index CL corresponding to the characteristic spectral line of each metal to be tested in each reference spectrum. m The process time stage and the benchmark index CL for each metal being tested will be used. m Establish corresponding relationships to form a metal characteristic spectrum database.

8. The online monitoring system for metal pollution as described in claim 7, characterized in that, It also includes a data storage module connected to the first processing module and the second processing module. The data storage module is used to store the principal component analysis model, the preset metal characteristic spectrum database, and the reference spectrum.

9. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory is used to store a computer program executable by the processor; and the processor is used to execute the computer program in the memory to implement the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the executable computer program in the storage medium is executed by a processor, it can implement the method as described in any one of claims 1 to 6.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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