Ion implanter stability monitoring method and fault detection and classification system

Abstract

The present application discloses a monitoring method for the stability of an ion implanter and a fault detection and classification system, the monitoring method comprising: obtaining multiple values ​​of key parameters of the ion implanter at different times during the ion implantation process, the key parameters being parameters dynamically adjusted to stabilize the extraction current of the ion implanter; using a target statistical algorithm that is correlated with the yield of products implanted by the ion implanter to perform statistics on the multiple values ​​to obtain statistical results; and determining the stability of the ion implanter during the ion implantation process based on the statistical results. The present application realizes real-time and accurate monitoring of the stability of the ion implanter.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing technology, and in particular to a monitoring method for the stability of an ion implanter and a fault detection and classification system. Background Art

[0002] During the process of ion implantation of products on the ion implanter, the instability of the ion implanter causes the voltage compensation (offset) required by the product to have a large fluctuation range, thereby increasing the product's defective rate (suffer ratio). Therefore, stability monitoring of the ion implanter is very necessary.

[0003] At present, the monitoring method of ion implanter stability is to implant ions on a bare wafer, perform high-temperature annealing on the bare wafer after ion implantation, and then use a probe to measure the resistance of the bare wafer ion implantation layer. The stability of the ion implanter is determined by analyzing the average value and standard deviation of the resistance. However, this monitoring method can only monitor the stability of the ion implanter when implanting ions on a bare wafer. The stability of the ion implanter during the transfer of goods cannot be known in real time. In addition, due to the influence of multiple factors such as annealing and measurement, it is also impossible to accurately reflect the stability of the ion implanter when implanting ions on a bare wafer. Summary of the invention

[0004] In view of the above problems, the purpose of the present application is to provide a method for monitoring the stability of an ion implanter and a fault detection and classification system to achieve real-time and accurate monitoring of the stability of the ion implanter.

[0005] According to a first aspect of the present application, a method for monitoring stability of an ion implanter is provided, comprising:

[0006] During the ion implantation process, a plurality of values ​​of a key parameter of the ion implanter at different times are obtained, wherein the key parameter is a parameter that is dynamically adjusted to stabilize the extraction current of the ion implanter;

[0007] The plurality of values ​​are counted by a target statistical algorithm that is correlated with the yield of a product that undergoes ion implantation using the ion implanter to obtain a statistical result;

[0008] The stability of the ion implanter during the ion implantation process is determined according to the statistical results.

[0009] Optionally, the monitoring method further includes:

[0010] Acquiring a plurality of parameters correlated with an extraction current of the ion implanter;

[0011] Fitting a curve graph showing the change of the value of each parameter of the plurality of parameters over time when the extraction current of the ion implanter remains stable;

[0012] The key parameter is determined from the plurality of parameters according to the variation curve diagram.

[0013] Optionally, the multiple parameters include: filament voltage, filament current, bias voltage, bias current, arc voltage, and arc current.

[0014] Optionally, the variation curve of each parameter of the multiple parameters is fitted while the extraction current of the ion implanter is maintained at the same preset value, and the preset value is set by the user.

[0015] Optionally, the variation curve graph of each parameter of the multiple parameters is fitted through an application supporting automatic expansion and contraction.

[0016] Optionally, the monitoring method further includes:

[0017] The ion implanter is used to implant ions into the wafer in advance to produce the product;

[0018] By using a plurality of statistical algorithms, the values ​​of the key parameters of the ion implanter at different times during the ion implantation process on the wafer are counted to obtain a plurality of statistical values;

[0019] Dividing the statistical values ​​of the plurality of wafers obtained by the same statistical algorithm into the same data set to obtain a plurality of data sets;

[0020] A data set among the multiple data sets that is correlated with the yield of products produced on multiple wafers is determined as a target data set, and a statistical algorithm for obtaining the target data set is determined as the target statistical algorithm.

[0021] Optionally, the multiple statistical algorithms include: a mean value statistical algorithm, a full range statistical algorithm, a mid-range statistical algorithm, a standard deviation statistical algorithm and a moving range statistical algorithm.

[0022] Optionally, the yield of the products produced on the wafer is determined based on the proportion of defective products in the products produced on the wafer.

[0023] Optionally, the monitoring method further comprises: determining the stability of the ion implanter according to the stability of the ion implanter during multiple ion implantation processes.

[0024] According to a second aspect of the present application, a fault detection and classification system is provided, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, any one of the monitoring methods described in the first aspect is implemented.

[0025] The unexpected technical effects of this application are:

[0026] The monitoring method for the stability of an ion implanter provided in an embodiment of the present application includes: obtaining multiple values ​​of key parameters of the ion implanter at different times during the ion implantation process, wherein the key parameters are parameters that are dynamically adjusted to stabilize the extraction current of the ion implanter; performing statistics on the multiple values ​​through a target statistical algorithm that is correlated with the yield of products that use the ion implanter for ion implantation to obtain statistical results; and determining the stability of the ion implanter during the ion implantation process based on the statistical results. Since the more stable the extraction current of the ion implanter is, the higher the yield of the product that uses the ion implanter for ion implantation has, the above statistical results are determined as data features that characterize the stability of the ion implanter from two aspects of parameter type and parameter processing method, so that the stability of the ion implanter can be accurately determined based on the statistical results. The above monitoring method is implemented based on multiple values ​​of the key parameters of the ion implanter at different times during the ion implantation process, so that the stability of the ion implanter during the ion implantation process can be determined in real time.

[0027] Furthermore, the monitoring method for the stability of the ion implanter provided in the embodiment of the present application also includes: obtaining multiple parameters that are correlated with the extraction current of the ion implanter; fitting a curve chart of the change of the value of each parameter in the multiple parameters over time when the extraction current of the ion implanter is stable; and determining the key parameters from the multiple parameters according to the change curve chart. Since the multiple parameters that are correlated with the extraction current of the ion implanter can all affect the size of the extraction current, but the parameters that affect the stability of different ion implanters are not necessarily the same, the above-mentioned change curve chart is obtained based on the value fitting of each parameter of the ion implanter itself, and thus the key parameters of the ion implanter can be determined more accurately.

[0028] Furthermore, the monitoring method of the stability of the ion implanter provided in the embodiment of the present application also includes: pre-using the ion implanter to implant ions into the wafer to produce products; using multiple statistical algorithms to count the values ​​of the key parameters of the ion implanter at different times during the ion implantation process on the wafer to obtain multiple statistical values; dividing the statistical values ​​obtained by the same statistical algorithm from the multiple statistical values ​​of multiple wafers into the same data set to obtain multiple data sets; determining a data set among the multiple data sets that is correlated with the yield of the products produced on the multiple wafers as the target data set, and determining the statistical algorithm for obtaining the target data set as the target statistical algorithm. Since the dynamic adjustment method of the key parameters is not necessarily the same for different ion implanters to make the extraction current stable, the above-mentioned pre-using the ion implanter to implant ions into the wafer to produce products and determining the target statistical algorithm through the statistical values ​​of the key parameters of the ion implanter at different times in the process and the yield of the produced products, it can ensure that the determined target statistical algorithm has a relatively reliable correlation with the yield of the product using the ion implanter for ion implantation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other objects, features and advantages of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings, in which:

[0030] Figure 1 Two exemplary distribution diagrams showing the compensation voltage required at different positions of the product;

[0031] Figure 2 shows two exemplary defect distribution diagrams on wafers after products are produced on the wafers;

[0032] Figure 3 A flow chart showing a method for monitoring the stability of an ion implanter according to an embodiment of the present application;

[0033] Figure 4 A schematic diagram showing the structure of an ion source of an ion implanter;

[0034] Figure 5 A curve diagram showing changes in six parameters of an exemplary ion implanter according to an embodiment of the present application that are correlated with the extraction current of the ion implanter when the extraction current is stable;

[0035] Figure 6 A defective distribution diagram of each wafer after the product is produced by pre-implanting ions into the wafer using an ion implanter according to an embodiment of the present application is shown;

[0036] Figure 7 A distribution diagram of movement ranges of multiple ion sources during multiple ion implantation processes according to an embodiment of the present application is shown;

[0037] Figure 8 A structural block diagram of a fault detection and classification system according to another embodiment of the present application is shown.

[0038] Explanation of the reference numerals: 10 - ion source; 11 - reaction chamber wall; 12 - cathode; 13 - reflector; 14 - filament; 15a - first magnet; 15b - second magnet; 16 - filament power supply; 17 - bias power supply; 18 - arc power supply; 1300 - fault detection and classification system; 1310 - memory; 1320 - processor; 1330 - power supply assembly; 1340 - network interface; 1350 - input and output interface. DETAILED DESCRIPTION

[0039] The present application will be described in more detail below with reference to the accompanying drawings. In each of the accompanying drawings, the same elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not drawn to scale. In addition, some well-known parts may not be shown.

[0040] This application may be presented in a variety of forms, some examples of which are described below.

[0041] During the production process, products need to be implanted with ions. For example, complementary metal oxide semiconductors need to be implanted with ion implanters to form source and drain electrodes. Ion implanters form plasma through ion sources. When the ion source forms a stable plasma, the ion implanter can generate the extraction current set by the product's corresponding recipe. When the ion source forms an unstable plasma, the ion implanter extracts different numbers of ions at different times, thereby generating unstable extraction currents.

[0042] During the process of ion implantation of the product on the ion implanter, the ion implanter injects ions into different positions of the product at different times. The instability of the ion implanter will cause different concentrations of ions to be injected into different positions of the product, which will result in different compensation voltage values ​​required at different positions of the product. Figure 1 In the figure (a), the fluctuation between sequence 2 and sequence 3 is not obvious in sequence 1, but is as follows Figure 1 As shown in Figure (b), Series 1 fluctuates more obviously from above Series 2 to below Series 3, which means that the voltage values ​​required to be compensated at different positions of the product have a larger fluctuation range, so the product will be identified as defective. Figure 1 In Figures (a) and (b), the horizontal axis represents the position on the product, and the vertical axis represents the voltage value. Figure 2 FIG. 1 shows a defective distribution map (suffer map) on two exemplary wafers after products are produced on the wafers. The black area in the figure indicates the area where the defective products are located. Figure 2The unstable ion implantation opportunity shown makes areas of the wafer with defective products more obvious.

[0043] At present, the monitoring method of the stability of the ion implanter is to use the ion implanter to implant ions into different positions on the bare wafer at different times after the appearance and insulation of the ion source are qualified, and then the bare wafer after ion implantation is subjected to high temperature annealing, and then the resistance of different positions on the ion implantation layer of the bare wafer is measured by a probe and the stability of the ion implanter is judged by analyzing the average value and standard deviation of the resistance. However, this monitoring method can only monitor the stability of the ion implanter when implanting ions on the bare wafer, and the stability of the ion implanter during the over-the-counter cannot be known in real time, so the defective rate of the product implanted by the ion implanter is not correlated with the stability of the ion implanter. In addition, the high temperature annealing process will cause the ions implanted into the wafer to diffuse, and the probe for measuring the resistance of the ion implantation layer will become more and more blunt due to the increase in the number of uses, so the resistance measured by the probe cannot accurately reflect the number of ions implanted by the ion implanter at different positions on the bare wafer, and therefore cannot accurately characterize the stability of the ion implanter when implanting ions on the bare wafer.

[0044] In view of this, an embodiment of the present application provides a method for monitoring the stability of an ion implanter. Figure 3 The flowchart of the monitoring method is shown in FIG. Figure 3 As shown, the monitoring method includes:

[0045] Step S110, obtaining multiple values ​​of key parameters of the ion implanter at different times during the ion implantation process, where the key parameters are parameters that are dynamically adjusted to keep the extraction current of the ion implanter stable;

[0046] Step S120, performing statistics on multiple values ​​using a target statistical algorithm that is correlated with the product yield of ion implantation using an ion implanter to obtain a statistical result;

[0047] Step S130, determining the stability of the ion implanter during the ion implantation process according to the statistical results.

[0048] The execution process of the monitoring method including the above steps S110 to S130 is described in detail below.

[0049] In an optional embodiment, the above-mentioned monitoring method also includes: obtaining multiple parameters that are correlated with the extraction current of the ion implanter; fitting a curve chart of the change of the value of each parameter of the multiple parameters over time when the extraction current of the ion implanter is stable; and pre-determining the key parameters described in step S110 from the multiple parameters according to the change curve chart.

[0050] Figure 4The figure shows a schematic diagram of the structure of an ion source of an ion implanter. Figure 4 As shown, the ion source 10 includes a reaction chamber wall 11, a cathode 12 and a reflector 13 arranged opposite to each other at two ends of the reaction chamber wall 11, a filament 14 arranged on a side of the cathode 12 away from the reflector 13, a first magnet 15a arranged on a side of the filament 14 away from the cathode 12, and a second magnet 15b arranged on a side of the reflector 13 away from the cathode 12. The first magnet 15a and the second magnet 15b create a magnetic field in the reaction chamber surrounded by the reaction chamber wall 11; in addition, the ion source 10 also includes a filament power supply 16, a bias power supply 17 and an arc power supply 18.

[0051] Reference Figure 4 The working principle of the ion source 10 is as follows: the filament power supply 16 applies a filament voltage C1 to the filament 14 to form a filament current C2, and the filament 14 is heated to generate hot electrons under the action of the filament voltage C1 and the filament current C2; the bias power supply 17 applies a bias voltage C3 between the filament 14 and the cathode 12, and the filament 14 is used as the negative electrode, so that an electric field is formed between the filament 14 and the cathode 12, and the hot electrons generated by the filament 14 move toward the cathode 12 under the action of the electric field, and the current of the loop between the filament 14 and the cathode 12 is the bias current C4, and the bias current C4 represents the amount of hot electrons absorbed by the cathode 12, and the cathode 12 moves toward the cathode 12 under the collision of the hot electrons. A steady supply of electrons is provided in the reaction chamber; the arc power supply 18 applies an arc voltage C5 between the reaction chamber wall 11 and the cathode 12, and the cathode 12 serves as the negative electrode, so that an electric field and a magnetic field exist in the reaction chamber and the electrons in the reaction chamber perform a spiral motion under the action of the electric field and the magnetic field, so that the gas in the reaction chamber is fully collided and dissociated into plasma, and the current of the loop between the reaction chamber wall 11 and the cathode 12 is the arc current C6; finally, the outlet of the ion source 10 extracts ions, and the number of ions extracted by the ion source 10 per unit time is characterized by the extraction current Y1, and a stable ion source 10 can provide a stable extraction current Y1.

[0052] According to the working principle of the ion source, multiple parameters related to the extraction current of the ion implanter can be directly determined. Figure 4 In the illustrated ion source 10 , the above-mentioned multiple parameters may be determined to include: a filament voltage C1 , a filament current C2 , a bias voltage C3 , a bias current C4 , an arc voltage C5 , and an arc current C6 .

[0053] Figure 5 FIG. 1 is a graph showing the variation of six parameters of an exemplary ion implanter that are correlated with the extraction current of the ion implanter when the extraction current is stable. Figure 5In each figure, the horizontal axis indicates the time scale, the left vertical axis indicates the scale of the lead-out current Y1, and the right vertical axis indicates the scale of one of the six parameters. In each figure, the dotted line indicates the change curve of the lead-out current Y1 over time, and the solid line indicates the change curve of one of the six parameters. Among them, (a) shows the change curve of the filament voltage C1 through a solid line, (b) shows the change curve of the filament current C2 through a solid line, (c) shows the change curve of the bias voltage C3 through a solid line, (d) shows the change curve of the bias current C4 through a solid line, (e) shows the change curve of the arc voltage C5 through a solid line, and (f) shows the change curve of the arc current C6 through a solid line. Figure 5 As shown, in this exemplary ion implanter, when the extraction current Y1 is stable, only the change curve of the bias voltage C3 in Figure (c) has obvious fluctuations, which means that when the extraction current Y1 is stable, only the bias voltage C3 changes with time. Therefore, the bias voltage C3 can be directly determined as the key parameter based on the change curves of the six parameters.

[0054] like Figure 5 As shown, the variation curves of the six parameters that are correlated with the extraction current Y1 of the ion implanter are fitted when the extraction current Y1 of the ion implanter is maintained at the same preset value, and the preset value can be set by the user, so the user can set the preset value to a value in the commonly used value range of the extraction current Y1 of the ion implanter. In order to more accurately determine the correlation between the parameters, the variation curves of each parameter in the multiple parameters that are correlated with the extraction current of the ion implanter can be fitted through an application that supports auto scaling, so that the application will not make the fitted variation curves inaccurate due to insufficient computing resources.

[0055] In another optional embodiment, the above-mentioned monitoring method also includes: using an ion implanter to implant ions into wafers in advance to produce products; using multiple statistical algorithms to count the values ​​of key parameters of the ion implanter at different times during the ion implantation process on the wafer to obtain multiple statistical values; dividing the statistical values ​​of multiple statistical values ​​of multiple wafers obtained by the same statistical algorithm into the same data set to obtain multiple data sets; determining a data set among the multiple data sets that is correlated with the yield of products produced on the multiple wafers as the target data set, and determining the statistical algorithm for obtaining the target data set as the target statistical algorithm described in step S120.

[0056] Combine the following Figure 6 Table 1 and Table 1 illustrate the process of determining the target statistical algorithm.

[0057] Figure 6Figure 2 shows the defective distribution diagram of each wafer after the product is produced by pre-implanting ions into the wafer using an ion implanter, wherein (a) shows the defective distribution diagram of each of the 25 wafers in the first batch of wafers Lot1, and (b) shows the defective distribution diagram of each of the 25 wafers in the second batch of wafers Lot2. Figure 6 In Figures (a) and (b), the wafers in the same row of 25 wafers in each batch of wafers are numbered in increasing order from left to right and the wafers in each row are numbered from top to bottom. For example, the wafer in the first row and first column in Figure (a) is numbered 1 and the wafer in the fourth row, fourth column and fifth column is numbered 20.

[0058] Table 1

[0059]

[0060] listed in Table 1 Figure 6 The figure shows multiple statistical values ​​of some wafers in the two batches of wafers and the defective percentage of the products produced. Among them, wafer #1 numbered 1, wafer #20 numbered 20 and wafer #24 numbered 24 in the first batch of wafers Lot1 are listed with multiple statistical values ​​and the defective percentage of the products produced. Wafer #1 numbered 1, wafer #2 numbered 2, wafer #15 numbered 15 and wafer #16 numbered 16 in the second batch of wafers Lot2 are listed with multiple statistical values ​​and the defective percentage of the products produced. The moving ranges of the multiple statistical values ​​of these wafers are still Figure 6 Marked in.

[0061] In the example shown in Table 1, the multiple statistical algorithms used to obtain multiple statistical values ​​of each wafer include: mean statistical algorithm, range statistical algorithm, midrange statistical algorithm, standard deviation statistical algorithm and moving range statistical algorithm. The following formula (1) shows the calculation of the mean value in the mean statistical algorithm: Formula (2) shows the formula for calculating the full range x in the full range statistical algorithm. r Formula (3) shows the calculation method of the mid-range number x in the mid-range number statistical algorithm. m Formula (4) shows the formula for calculating the standard deviation s in the standard deviation statistical algorithm, and formula (5) shows the formula for calculating the moving range x in the moving range statistical algorithm. mr The formula is, where x i represents the value of the key parameter at time i during the ion implantation process, n represents the total number of values ​​of the key parameter collected during the ion implantation process, x max Indicates the maximum value among the n values ​​of the key parameter, x min Indicates the minimum value among the n values ​​of the key parameter.

[0062] (1)

[0063] (2)

[0064] (3)

[0065] (4)

[0066] (5)

[0067] In Table 1, the yield of the products produced on the wafer is determined according to the proportion of defective products in the products produced on the wafer. The multiple statistical values ​​in Table 1 that are in the same row as each statistical algorithm are a set of statistical values ​​obtained by the same statistical algorithm. In the embodiment of the present application, such a set of statistical values ​​is also referred to as a data set. The data set in the same row as the moving range among the five data sets is correlated with the yield of the products produced on the multiple wafers. Specifically, the moving range corresponding to wafer #1 numbered 1 in the first batch of wafers Lot1 is 0.0261, and the proportion of defective products produced on this wafer is 0.0%; the moving range corresponding to wafer #20 numbered 20 in the first batch of wafers Lot1 is 0.1057, and the proportion of defective products produced on this wafer is 20.2%; the moving range corresponding to wafer #24 numbered 24 in the first batch of wafers Lot1 is 0.0993, and the proportion of defective products produced on this wafer is 14.1%; the moving range corresponding to wafer #1 numbered 1 in the second batch of wafers Lot2 is 0.0184, and the proportion of defective products produced on this wafer is 0.0%; the moving range corresponding to wafer #15 numbered 15 in the second batch of wafers Lot2 is 0.0350, and the proportion of defective products produced on this wafer is 11.8%. Therefore, the larger the moving range corresponding to the above five wafers, the greater the proportion of defective products in the products produced on the wafers, so that the above five wafers can better determine that the data set in the same row as the moving range is correlated with the yield of products produced on multiple wafers. The data set in the same row as the moving range is determined as the target data set, and the moving range statistical algorithm is determined as the target statistical algorithm.

[0068] It should be noted that the above combination Figure 6In the exemplary description in Table 1, compared with wafer #15 numbered 15 in the second batch of wafers Lot2, wafer #2 numbered 2 in the second batch of wafers Lot2 does not mean that the larger the moving range, the larger the proportion of defective products. Wafer #2 numbered 2 in the second batch of wafers Lot2 can be regarded as a wafer with interference factors. In practice, more candidate wafers can be selected, so that the target statistical algorithm determined after eliminating individual wafers with interference factors from the candidate wafers is more reliable.

[0069] In step S130, the stability of the ion implanter during the ion implantation process is determined according to the statistical results, which is specifically determined according to the correlation described in step S120. This is because the correlation between the statistical results and the stability of the ion implanter is the same as the correlation between the statistical value calculated by the target statistical algorithm and the product yield. Figure 6 In the exemplary description of Table 1, the correlation described in step S120 is reflected in that the larger the moving range calculated by the moving range statistical algorithm, the larger the defective ratio, that is, the moving range and the defective ratio are positively correlated, and thus the execution process of step S130 is that the larger the statistical result of the moving range, the more unstable the ion implanter is. In practice, a threshold value can be set, and during the execution of step S130, the statistical result of the moving range can be compared with the threshold value to determine whether the ion implanter is stable during the ion implantation process; multiple threshold values ​​of different sizes can also be set, and during the execution of step S130, it can be determined that the statistical result of the moving range is within the range defined by two adjacent thresholds, so as to judge the stability level of the ion implanter during the ion implantation process.

[0070] In another optional embodiment, the above-mentioned monitoring method also includes: after executing steps S110 to S130 multiple times, determining the stability of the ion implanter according to the stability of the ion implanter in multiple ion implantation processes determined in multiple steps S130, that is, determining the overall reliability of the ion implanter.

[0071] Figure 7 The figure shows the distribution diagram of the moving range of multiple ion sources during multiple ion implantation processes, wherein the horizontal axis represents the sequence number of the ion implantation processes of the multiple ion sources, and the vertical axis represents the moving range. Figure 7 As shown, the statistical result of the moving range of ion source Source_1 and ion source Source_3 in each ion injection process is less than 0.020 unit, so the stability of ion source Source_1 and ion source Source_3 is good; the statistical result of the moving range of ion source Source_2 in multiple ion injection processes is less than 0.020 unit only in one ion injection process, so the stability of ion source Source_2 is poor.

[0072] Corresponding to the ion implanter stability monitoring method provided in the above embodiment, another embodiment of the present application provides a fault detection and classification system, such as Figure 8 As shown, the fault detection and classification system 1300 includes: a memory 1310 and a processor 1320, and a program stored in the memory 1310 and executable on the processor 1320. When the program is executed by the processor 1320, each process in each embodiment of the above monitoring method can be implemented and the same technical effect can be achieved. In some examples, the fault detection and classification system 1300 may also include auxiliary sub-devices such as a power supply component 1330, a network interface 1340, and an input / output interface 1350.

[0073] A person of ordinary skill in the art can understand that all or part of the steps of the above method embodiments can be completed by executing a computer program. To this end, another embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of each embodiment of the above monitoring method can be implemented and the same technical effect can be achieved. Among them, computer-readable storage media, such as mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media that can store program codes.

[0074] Finally, it should be noted that the terms "first", "second" and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. According to the embodiments of the present application as described above, these embodiments do not describe all the details in detail, nor do they limit the application to the specific embodiments described. Obviously, many modifications and changes can be made based on the above description. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that technicians in the relevant technical field can make good use of the present application and the modified use based on the present application. The present application is only limited by the claims and their full scope and equivalents.

Claims

1. A method for monitoring the stability of an ion implanter, comprising: During the ion implantation process, a plurality of values ​​of a key parameter of the ion implanter at different times are obtained, wherein the key parameter is a parameter that is dynamically adjusted to stabilize the extraction current of the ion implanter, and the extraction current of the ion implanter is the number of ions extracted by the ion implanter per unit time; The plurality of values ​​are counted by a target statistical algorithm that is correlated with the yield of a product that undergoes ion implantation using the ion implanter to obtain a statistical result; The stability of the current drawn out by the ion implanter during the ion implantation process is determined according to the statistical results.

2. The monitoring method according to claim 1, further comprising: Acquiring a plurality of parameters correlated with an extraction current of the ion implanter; Fitting a curve graph showing the change of the value of each parameter of the plurality of parameters over time when the extraction current of the ion implanter remains stable; The key parameter is determined from the plurality of parameters according to the variation curve diagram.

3. The monitoring method according to claim 2, wherein: The multiple parameters include: filament voltage, filament current, bias voltage, bias current, arc voltage, and arc current.

4. The monitoring method according to claim 2, wherein: The variation curve diagram of each parameter of the plurality of parameters is fitted when the extraction current of the ion implanter is maintained at the same preset value, and the preset value is set by the user.

5. The monitoring method according to claim 2, wherein: The variation curve graph of each parameter of the multiple parameters is fitted through an application program supporting automatic expansion and contraction.

6. The monitoring method according to claim 1, further comprising: The ion implanter is used to implant ions into the wafer in advance to produce the product; By using a plurality of statistical algorithms, the values ​​of the key parameters of the ion implanter at different times during the ion implantation process on the wafer are counted to obtain a plurality of statistical values; Dividing the statistical values ​​of the plurality of wafers obtained by the same statistical algorithm into the same data set to obtain a plurality of data sets; A data set among the multiple data sets that is correlated with the yield of products produced on multiple wafers is determined as a target data set, and a statistical algorithm for obtaining the target data set is determined as the target statistical algorithm.

7. The monitoring method according to claim 6, wherein: The multiple statistical algorithms include: mean value statistical algorithm, full range statistical algorithm, mid-range statistical algorithm, standard deviation statistical algorithm and moving range statistical algorithm.

8. The monitoring method according to claim 6, wherein: The yield of the products produced on the wafer is determined by the proportion of defective products among the products produced on the wafer.

9. The monitoring method according to claim 1, further comprising: The stability of the ion implanter is determined according to the stability of the current drawn out by the ion implanter during multiple ion implantation processes.

10. A fault detection and classification system comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the monitoring method according to any one of claims 1 to 9.

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